Education, Faculty of
Educational and Counselling Psychology, and Special Education (ECPS), Department of
DSpace
UBCV
Saglam, Mehmet Hilmi
Doctor of Philosophy - PhD
University of British Columbia
In recent years, there has been a growing recognition of the need for educational environments that prioritize the diverse needs of all students, promote equity, and embrace constructivist approaches. This study adopts an inclusive and egalitarian ideology that centers on constructing "an education that is gifted for every student" by recognizing, celebrating, and cultivating diverse intelligences and talents. In this regard, this dissertation proposes an initial conceptual framework—the Intelligence-Friendly Classroom (IFC) Framework—which is designed to help educators recognize and cultivate students’ intelligences, talents, strengths, and interests. The framework integrates cognitive, sociocultural, developmental, and motivational aspects of intelligence by synthesizing four established theories: Cattell’s Fluid and Crystallized Intelligence (FCI), Gardner’s Multiple Intelligences (MI), Sternberg’s Successful Intelligence (SI), and Dweck’s Implicit Theories of Intelligence (ITI). This new synthesis supports the creation of inclusive and responsive classrooms for fostering equity, diversity, and talent development in education. Employing a quantitative research methodology with a cross-sectional survey design, this study investigates educators' knowledge, skills, attitudes, and self-efficacy towards constructing an intelligence-friendly classroom through the application of these theories. Results indicate that educators are more familiar with, and more confident in applying the motivational and sociocultural aspects of the framework (ITI and MI theories). In contrast, the developmental and cognitive aspects (FCI and SI theories) are less familiar and perceived as more challenging. Despite variations in knowledge and skills, most participants expressed highly positive attitudes towards constructing intelligence-friendly classrooms and reported a moderate level of self-efficacy in building such classrooms. Therefore, this study contributes to advancing inclusive and strengths-based education by offering the IFC Framework as an initial step toward a practical model. By offering this integrative framework, it aims to support educators in designing enriched learning environments that recognize diversity, promote equity, and empower all students to reach their potential.
Towards an Intelligence-Informed Competency Framework: Examining Educators’ Knowledge, Skills, Attitudes, and Self-Efficacy in Inclusive Classrooms by Mehmet Hilmi Saglam B.A., Istanbul University, 2014 M.A., Marmara University, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Special Education) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2025 © Mehmet Hilmi Saglam, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Towards an Intelligence-Informed Competency Framework: Examining Educators’ Knowledge, Skills, Attitudes, and Self-Efficacy in Inclusive Classrooms submitted by Mehmet Hilmi Saglam in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Special Education Examining Committee: Dr. C. Owen Lo, Educational and Counselling Psychology, Special Education, UBC Supervisor Dr. Leyton Schnellert, Curriculum and Pedagogy, UBC Supervisory Committee Member Dr. Amery Wu, Educational and Counselling Psychology, Special Education, UBC Supervisory Committee Member Dr. Jillianne Code, Curriculum and Pedagogy, UBC University Examiner Dr. Laurie Ford, Educational and Counselling Psychology, Special Education, UBC University Examiner iii Abstract In recent years, there has been a growing recognition of the need for educational environments that prioritize the diverse needs of all students, promote equity, and embrace constructivist approaches. This study adopts an inclusive and egalitarian ideology that centers on constructing "an education that is gifted for every student" by recognizing, celebrating, and cultivating diverse intelligences and talents. In this regard, this dissertation proposes an initial conceptual framework—the Intelligence-Friendly Classroom (IFC) Framework—which is designed to help educators recognize and cultivate students’ intelligences, talents, strengths, and interests. The framework integrates cognitive, sociocultural, developmental, and motivational aspects of intelligence by synthesizing four established theories: Cattell’s Fluid and Crystallized Intelligence (FCI), Gardner’s Multiple Intelligences (MI), Sternberg’s Successful Intelligence (SI), and Dweck’s Implicit Theories of Intelligence (ITI). This new synthesis supports the creation of inclusive and responsive classrooms for fostering equity, diversity, and talent development in education. Employing a quantitative research methodology with a cross-sectional survey design, this study investigates educators' knowledge, skills, attitudes, and self-efficacy towards constructing an intelligence-friendly classroom through the application of these theories. Results indicate that educators are more familiar with, and more confident in applying the motivational and sociocultural aspects of the framework (ITI and MI theories). In contrast, the developmental and cognitive aspects (FCI and SI theories) are less familiar and perceived as more challenging. Despite variations in knowledge and skills, most participants expressed highly positive attitudes towards constructing intelligence-friendly classrooms and reported a moderate level of self-efficacy in building such classrooms. Therefore, this study contributes to advancing inclusive and strengths-based education by offering the IFC Framework as an initial step toward a practical model. By offering this integrative framework, it aims to support educators in iv designing enriched learning environments that recognize diversity, promote equity, and empower all students to reach their potential. v Lay Summary This study examined how educators understand and apply various intelligence theories to create classrooms that recognize and nurture students' unique strengths and abilities. The study introduced the Intelligence-Friendly Classroom Framework, which brings together different ways of understanding intelligence—such as thinking skills (cognitive), social and cultural factors (sociocultural), growth over time (developmental), and motivation (motivational)—to support inclusive and strengths-based education. The findings reveal that educators are more familiar with and confident in applying the motivational aspect (Dweck’s mindset theory) and sociocultural aspect (Gardner’s multiple intelligences theory) of intelligence. In contrast, educators were less familiar with the cognitive aspect (Sternberg’s successful intelligence theory) and the developmental aspect (Cattell’s fluid and crystallized intelligence theory) of intelligence and found them more challenging to apply in their classrooms. Despite these variations, teachers' positive attitudes towards constructing intelligence-friendly classrooms demonstrate their enthusiasm for integrating these theories into practice. The study offers pathways for fostering equitable, inclusive, and effective education for all learners, specifically if the framework were to be introduced into both in-service and pre-service teacher education. vi Preface This dissertation is an original, unpublished, independent work by the author, Mehmet Hilmi Saglam under the supervision of Dr. C. Owen Lo, with funding provided by the Turkish Ministry of National Education. Generative AI was not used for any aspects of the work. This research was approved by The University of British Columbia’s Behavioral Research Ethics Board on December 11, 2023, with the certificate #H23-03462. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Table ................................................................................................................................... x List of Figures ............................................................................................................................. xiii Acknowledgements .................................................................................................................... xiv Dedication .................................................................................................................................... xv Chapter 1: Introduction ............................................................................................................... 1 1.1. The Strengths-Based Approach and Intelligence Theories ............................................. 3 1.2. Smart Context: Intelligence-Friendly Classrooms .......................................................... 5 1.3. Purpose of the Study ....................................................................................................... 8 1.4. Key Terminology ............................................................................................................ 8 Chapter 2: Literature Review .................................................................................................... 10 2.1. Strengths-Based Thinking ............................................................................................. 11 2.1.1. Historical Context of Strengths-Based Approaches ................................................. 11 2.1.2. Strengths-Based Education ....................................................................................... 14 2.2. Historical Context and Major Approaches of Intelligence ........................................... 18 2.3. Intelligence as a Multidimensional Construct............................................................... 25 viii 2.4. Theoretical Foundations of the Intelligence-Friendly Classroom Framework: An Integrative Approach ................................................................................................................ 26 2.4.1. The Theory of Fluid and Crystallized Intelligence (Developmental Aspect) ........... 29 2.4.2. The Theory of Multiple Intelligences (Sociocultural Aspect) .................................. 36 2.4.3. The Theory of Successful Intelligence (Cognitive Aspect) ...................................... 44 2.4.4. Implicit Theories of Intelligence (Belief-Based/Motivational Aspect) .................... 49 2.5. Key Principles of Four Theories in the Intelligence-Friendly Classroom Framework . 55 2.6. Teacher Education in British Columbia ........................................................................ 56 2.7. Research Questions of the Study .................................................................................. 59 Chapter 3: Methodology............................................................................................................. 61 3.1. Cross-Sectional Survey Design..................................................................................... 61 3.2. Participants .................................................................................................................... 62 3.3. Procedures ..................................................................................................................... 67 3.4. Instrument ..................................................................................................................... 68 3.4.1. Development ............................................................................................................. 69 3.4.2. Pilot Study Procedure ............................................................................................... 74 3.4.3. Validity and Reliability ............................................................................................. 74 3.5. Data Analysis ................................................................................................................ 75 Chapter 4: Results....................................................................................................................... 78 4.1. Data Preparation and Preliminary Analyses ................................................................. 78 4.2. Educators' Knowledge of FCI, MI, SI, and ITI Theories.............................................. 81 4.3. Educators' Skills in Applying FCI, MI, SI, and ITI Theories ....................................... 91 4.4. Educators' Attitudes Towards Constructing an Intelligence-Friendly Classroom ...... 102 ix 4.5. Educators' Self-Efficacy in Constructing an Intelligence-Friendly Classroom .......... 105 4.6. Summary ..................................................................................................................... 108 Chapter 5: Discussion ............................................................................................................... 110 5.1. Intelligence-Friendly Classrooms and 21st Century Learning ................................... 110 5.2. Intelligence-Friendly Classroom Framework in This Study ....................................... 112 5.3. General Discussion of Findings .................................................................................. 116 5.4. Educational Implications and Future Directions......................................................... 120 5.5. Strengths and Limitations ........................................................................................... 125 5.6. Conclusion .................................................................................................................. 128 References .................................................................................................................................. 130 APPENDICES ........................................................................................................................... 174 Appendix A: Expert Review Form ....................................................................................... 174 Appendix B: Pilot Study Recruitment Email ....................................................................... 178 Appendix C: Pilot Study Instruction Sheet .......................................................................... 179 Appendix D: Participant Recruitment Flyer ......................................................................... 181 Appendix E: Consent Form and Survey Questionnaire ....................................................... 182 Appendix F: Initial Contact Email ....................................................................................... 188 x List of Table Table 1 Principles of Four Intelligence Theories for Talent Development .................................. 55 Table 2 Frequencies of Personal Demographic Characteristics of the Participants ..................... 64 Table 3 Frequencies of Education-related Demographic Characteristics of the Participants ....... 65 Table 4 Demographic Characteristics Related to Multi-Dimensional Intelligence Theories ....... 67 Table 5 Organization of Survey Sections ..................................................................................... 73 Table 6 Shapiro-Wilk Test Results for the Normality of Dependent Variables' Distribution ...... 80 Table 7 Descriptive Statistics for Participants’ Perceived Knowledge of Theories within the Intelligence-Friendly Classroom Framework .................................................................. 81 Table 8 Comparison of Participants' Perceived Knowledge Levels Across Demographic Variables using Mann-Whitney U Test ........................................................................... 83 Table 9 The Results of Mann-Whitney U Tests ........................................................................... 84 Table 10 Comparison of Participants' Perceived Knowledge Levels Across Demographic Variables using Kruskal-Wallis H test ............................................................................ 85 Table 11 The Results of Kruskal-Wallis H Tests Regarding Undergraduate Major .................... 86 Table 12 The Results of Kruskal-Wallis H Tests for Number of Courses on Multidimensional Intelligence Theories in Bachelor's Program ................................................................... 88 Table 13 The Results of Kruskal-Wallis H Tests for Professional Sessions Attended on the Multidimensional Intelligence Theories in Teaching Careers......................................... 90 Table 14 Descriptive Statistics for Participants’ Perceived Skills of Theories within the Intelligence-Friendly Classroom Framework .................................................................. 92 Table 15 Comparison of Participants' Perceived Skills Levels Across Demographic Variables using Mann-Whitney U Test ........................................................................................... 93 xi Table 16 The Results of Mann-Whitney U Tests (Skills) ............................................................. 94 Table 17 Comparison of Participants' Perceived Skills Levels Across Demographic Variables using Kruskal-Wallis H test............................................................................................. 95 Table 18 The Results of Kruskal-Wallis H Tests Regarding Years of Teaching Experience ...... 96 Table 19 The Results of Kruskal-Wallis H Tests Regarding Undergrad Major ........................... 97 Table 20 The Results of Kruskal-Wallis H Tests for Number of Courses on Multidimensional Intelligence Theories in Bachelor's Program (Skills) ...................................................... 98 Table 21 Results of Kruskal-Wallis H Tests for Professional Sessions Attended on the Multidimensional Intelligence Theories in Teaching Careers (Skills) .......................... 101 Table 22 Descriptive Statistics for Attitude Dimension towards Creating Intelligence-Friendly Classrooms .................................................................................................................... 102 Table 23 Comparison of Participants' Perceived Attitude Level Across Demographic Variables using Mann-Whitney U Test ......................................................................................... 103 Table 24 Comparison of Participants' Perceived Attitude Level Across Demographic Variables using Kruskal-Wallis H test........................................................................................... 104 Table 25 The Results of Kruskal-Wallis H Tests Regarding Undergraduate Major (Attitude) . 105 Table 26 Descriptive Statistics for Self-Efficacy Dimension towards Creating Intelligence-Friendly Classrooms ...................................................................................................... 105 Table 27 Comparison of Participants' Perceived Self-Efficacy Level Across Demographic Variables using Mann-Whitney U Test ......................................................................... 106 Table 28 Comparison of Participants' Perceived Self-Efficacy Level Across Demographic Variables using Kruskal-Wallis H test .......................................................................... 107 xii Table 29 The Results of Kruskal-Wallis H Tests Regarding the variable of number of professional sessions attended on the Multidimensional Intelligence theories in their teaching career (Self-Efficacy) ...................................................................................... 108 xiii List of Figures Figure 1 Intelligence-Friendly Classroom Framework ................................................................. 27 Figure 2 How the Successful Intelligence Theory Works ............................................................ 45 Figure 3 Necessary Sample Size For the Statistical Power in the Study ...................................... 63 Figure 4 Instrument Development Process ................................................................................... 69 xiv Acknowledgements First and foremost, I would like to express my deepest gratitude to my supervisor, Dr. Owen Lo, for his invaluable guidance, support, and mentorship throughout the journey of this dissertation. His insightful feedback and constant encouragement have been instrumental in shaping both my academic growth and this research. I would also like to extend my heartfelt thanks to my supervisory committee members, Dr. Leyton Schnellert and Dr. Amery Wu, for their invaluable contributions. Their expertise, thoughtful critiques, and dedication have greatly enriched my work. Special thanks to my friend, Tonje Molyneux, whose conversations always bolstered my motivation and helped me push through challenging moments. I would also like to thank all the friends who walked alongside me at different stages of this journey, including Talha Göktentürk, whose support and the conversations we had enriched this process. I would also like to acknowledge the Ministry of National Education of Türkiye for granting me the opportunity to pursue my doctoral studies through their scholarship program. Additionally, I am grateful to my friends, colleagues, and the entire UBC Faculty of Education community for creating a nurturing academic environment that has fostered my learning and growth. Lastly, I am forever grateful to my family for their endless love and support. Specifically, I want to thank my wife, whose love, patience, and encouragement have been my greatest source of strength throughout this journey. Her support has truly made all the difference. To my children, thank you for being my constant source of strength and motivation, especially during the most challenging moments of this journey. xv Dedication This dissertation is dedicated to my loving family, whose unwavering belief in me has been my greatest source of inspiration; to my wonderful wife, who has stood by my side through every struggle; to my children, who are my constant source of motivation; and to the cherished memory of my father-in-law, who passed away due to the coronavirus. The world is a strange inn, a rough place, Humans are strange beings, never fitting their space. Life is a sip of water, a momentary dream, Life is a short path without a second scene. Chapter 1: Introduction Inclusive education, one of the globally recognized-contemporary educational approaches, has played a crucial role in the consolidation of the idea that education is for all students (Subotic & Andjic, 2014). By defending the rights of every learner and the importance of quality education for all (Al-Shammari et al., 2019), inclusive education seeks to eliminate barriers to learning and ensure that all students, regardless of their abilities or backgrounds, have their educational needs met without discrimination (UNESCO, 2013). Therefore, contemporary education systems emphasize the importance of creating inclusive learning environments that address the diverse needs of all students and foster equity through constructive approaches (Al-Shammari et al., 2019; Black-Hawkins, 2014; Lo et al., 2022; Milford et al., 2022). While inclusive education lays the groundwork for equitable learning environments, strengths-based education builds upon this foundation by shifting the focus from deficits to potential (Galloway et al., 2020). Instead of focusing on what students cannot do, strengths-based education emphasizes identifying and cultivating their unique strengths, talents, abilities, and interests (Clifton et al., 2006; Lo et al., 2019). Grounded in egalitarian and constructivist philosophies, this approach focuses on cultivating learning spaces that celebrate and nurture the unique strengths of every student (Lo et al., 2022; Lopez & Louis, 2009). It also increases educators' awareness to identify and develop every individual’s talents and strengths rather than focusing on weaknesses (Wellborn et al., 2012). Such an approach can support educators’ metacognitive awareness and help cultivate environments that encourage growth-oriented thinking. This fosters a growth mindset in students, where they view themselves as capable learners who can develop and actualize their potential through making efforts and exerting perseverance (Mason, 2019; Seaton, 2018). 2 Building on the principles of inclusive and strengths-based education, this dissertation proposes an initial conceptual framework that moves towards developing intelligence-friendly classrooms as a smart context, defined as a setting that nurtures students' intellectual abilities through dynamic interactions within their environment (Barab & Plucker, 2002). This dissertation situates the Intelligence-Friendly Classroom (IFC) Framework as a new synthesis that combines established theoretical perspectives into a single conceptual model, rather than claiming it as a fully validated or entirely original construct. This framework integrates four aspects of intelligence applicable in general classroom settings: the cognitive aspect with Sternberg’s Successful Intelligence (SI) theory, the sociocultural aspect with Gardner’s Multiple Intelligences (MI) theory, the developmental aspect with Cattell’s Fluid and Crystallized Intelligence (FCI) theory, and the belief-based (or motivational) aspect with Dweck’s Implicit Theories of Intelligence (ITI). These theories hold significant potential in helping educators understand and support the unique strengths and potential of their students (Dweck, 2000; Kaufman et al., 2013; Lo et al., 2019). For instance, Gardner’s framework encourages the use of varied instructional methods to address the sociocultural aspect of intelligence (Diaz & Heining-Boynton, 1995; Gardner, 1983). Sternberg’s theory, on the other hand, emphasizes the application of analytical, creative, and practical skills to real-world contexts, reflecting the cognitive aspect of intelligence (Gardner, 2011; Sternberg et al., 2021). Similarly, Cattell’s Fluid and Crystallized Intelligence theory highlights the developmental aspect by focusing on the dynamic interplay between these two forms of intelligence, particularly how they change with age (Horn & Cattell, 1967). Finally, Dweck’s ITI emphasizes the importance of fostering a growth mindset, which motivates students to view intelligence as improvable through effort and persistence, thereby highlighting the motivational aspect of intelligence (Dweck, 1986; Molway 3 & Mutton, 2019). Taken together, the implied pedagogical foci are placed on understanding and developing the multiple facets of intelligences, talents, strengths, and interests that resides in our diverse student body (Krechevsky & Seidel, 1998). In light with understanding the potential pedagogical value of constructing intelligence-friendly classrooms, this research aims to understand educators' self-perceived knowledge, skills, attitudes, and efficacy in building intelligence-friendly classrooms in their teaching practices. This chapter will provide an introduction by first discussing the background of this study through (1) the strengths-based approach and intelligence theories, and (2) smart context: intelligence-friendly classrooms. It will then continue with (3) the purpose of the study and (4) key terminology used in the dissertation. 1.1. The Strengths-Based Approach and Intelligence Theories The purpose of education should center around the development of the talents and potentials of youth (Gentry, 2021). However, much of our education system has largely been focused on students’ grades and academic success rather than fostering talent and potential (Swan, 2016). While academic achievement and talent development are not mutually exclusive, the emphasis on standardized outcomes and summative assessment often overshadows efforts to cultivate students' broader strengths and long-term growth. Strengths-based education, a constructive approach that aligns with contemporary inclusive education, prioritizes recognizing and nurturing each student's unique strengths, talents, and potentials rather than solely addressing their deficiencies (Lo et al., 2019; Lopez & Louis, 2009; Mottron, 2017). This shift in focus supports a more holistic educational experience, where students are encouraged to grow based on their existing talents and interests (Clifton et al., 2006). Strengths-based education conceptualizes strengths from a developmental perspective, viewing them not as static traits but as dynamic 4 qualities that can be cultivated over time (Lopez & Louis, 2009). This approach also aligns with the talent development paradigm, which aims to cultivate various strengths and interests from a broader perspective and to support students in excelling in their chosen fields (Dai, 2005). In this dissertation, I consider how intelligence theories might inform teachers’ classroom practice, particularly in relation to strengths-based pedagogy. By integrating intelligence theories, the strengths-based approach may provide educators with a comprehensive understanding of how to support and develop each student’s intelligences and strengths. According to Ackerman (2011), intelligence theories can serve as an appropriate framework to better understand the construct of multiple potentiality in each child. First, modern theories of intelligence, such as Sternberg's SI and Gardner's MI theories, which focus on person-environment interaction, allow us to understand what underlies intelligent performance in academic subjects rather than simply assuming that performance on tasks and test items is indicative of intelligence (Bruer, 1994). Namely, when a teacher is attempting to comprehend the minds of students in their classroom, a good theory can aid in making sense of the actions and behaviours that students reveal (Krechevsky & Seidel, 1998). In this way, teachers can recognize each student's specific needs and provide enriched opportunities for fostering their talents in an inclusive classroom (Lo et al., 2019). Second, offering multiple approaches towards the structure of intelligence, a multiple intelligences perspective has significant implications for professional development and training, in contrast to a single intelligence perspective (Gardner, 2012; Krechevsky & Seidel, 1998; Wagner & Sternberg, 1984). This multiple perspective has improved teachers' knowledge of various types of learners and assisted them in creating strategies to suit the diverse needs of their students (Gardner et al., 2018). Last but not least, a theory can serve as a framework for how we think about the students we teach, how to best 5 engage them in learning, and how to encourage educators to reflect more deeply and openly on their pedagogical decisions (Krechevsky & Seidel, 1998). Therefore, intelligence theories, especially those encouraging the development of multiple forms of intelligences, can inform educators in the preparation the affordances of a learning environment (Lo et al., 2019). 1.2. Smart Context: Intelligence-Friendly Classrooms According to Fogarty (1998), an intelligence-friendly classroom is a classroom where the environment is friendly to various forms of intelligences, friendly to the growth of patterns of human intellect, and friendly to the fostering of critical-creative-analytical thinking, problem solving and decision making. Therefore, the intelligence-friendly classroom can be characterized as an educational environment focusing on the intelligence of students, the implementation of intelligence theories, and seeking to cultivate intelligence potentials of learners. Moreover, in an intelligence-friendly classroom, teaching and learning processes are governed by the proper perspectives of intelligence theories (Fogarty, 1998). For instance, an intelligence-friendly classroom can be fostered into an inclusive of mutual emotional awareness between each member of the class by facilitating self-awareness, self-regulation, motivation, empathy, and social skills (Fogarty, 1998). Further, the intelligence-friendly classroom as a smart context is a classroom that has been enhanced physically, materially, intellectually, and instructionally in order to stimulate students' interest and keep their mind active (Lo et al., 2019). As a result, Meyer and Plucker (2021) suggested that educators, academics, and policymakers should seek out constructive and effective ways to create intelligence-friendly contexts conducive to the emergence of talents and the development of intelligences. Renzulli and Reis’s School-wide Enrichment Model (SEM) (1985) is a good example of how the school (and classroom) as a context can foster students’ talents. The SEM proposes that 6 schools should be designed as "smart contexts" where students are encouraged to engage in a variety of activities at different skill levels. In this model, school administrators and teachers collaborate to enrich the learning environment by identifying each student’s unique profile of intelligences, talents, and learning interests, while providing a continuum of learning opportunities that address the diversity of abilities (Renzulli & Reis, 2021). The classroom, therefore, should be seen as a context for talent socialisation, where students can get familiar with the many and diverse human potentials and efforts (Lo et al., 2019). Ultimately, adopting the idea of establishing a smart context (i.e., a setting with affordances to boost intelligence, abilities, and strengths) exposes students to more opportunities and promotes the pursuit of excellence (Lo et al., 2019; Plucker et al., 2021). Leveraging the concept of smart contexts, the intelligence-friendly classroom framework introduced by this dissertation represents an initial conceptual model aimed at cultivating students' strengths and intelligences through focusing on four key aspects of intelligence. First, the aim of the cognitive-processing aspect, grounded in Sternberg’s SI theory, is to teach three types of intelligence (analytical, creative, and practical) while recognizing students' unique strengths (Sternberg, 1999; Sternberg et al., 2006). For example, teachers may design activities that include real-world problem-solving exercises for practical intelligence, open-ended projects for creative intelligence, and logical reasoning tasks for analytical intelligence to cater the various cognitive-processing inclinations among the students. Alternatively, they can teach a topic by using three types of questions frequently in the classroom: what (analytical intelligence), how (practical intelligence), and why (creative intelligence) (Lo et al., 2022). Second, the sociocultural aspect, based on Gardner’s MI theory, highlights the importance of recognizing diverse intelligences shaped by cultural and environmental factors (Diaz & Heining-Boynton, 7 1995; Gardner, 2006). Using the MI theory framework, activities such as collaborative group work, project-based learning, and creative presentations allowing students to demonstrate their unique strengths and intelligences in many ways (Hickey, 2004; Setiawan et al., 2020). Third, the developmental aspect, informed by Cattell’s Fluid and Crystallized Intelligence (FCI) theory, aims to foster the development of both forms of intelligence across the lifespan (Horn & Cattell, 1967). While fluid intelligence, which involves adapting to new situations and solving novel problems, tends to decline with age (Davidson & Kemp, 2011; Rose & Fischer, 2011), crystallized intelligence reflects accumulated knowledge and skills, which develop and strengthen with age and experience (Hertzog, 2011; Horn et al., 1981; Nisbett et al., 2012). Teachers can design tasks that support both aspects, encouraging adaptability and reinforcing learned concepts over time, such as increasing students' familiarity with a subject (Rudebeck et al., 2012). Finally, the aim of the motivational aspect, using Dweck’s ITI, is to foster growth mindset in students (Dweck, 2000; Mrazek et al., 2018; Zeeb et al., 2020). Teachers can create supportive environments that encourage students to see challenges as opportunities for growth (Molway & Mutton, 2019). Such an emphasis on growth mindset has the potential to help students in exploring more possibilities, making decisions based on their values or interests rather than their fear of failing, and persisting in achieving their goals (Elliott & Dweck, 1983; Yeager et al., 2019). In conclusion, smart contexts with a focus on the cognitive, sociocultural, developmental, and motivational aspects of intelligence through applying these four theories (FCI, MI, SI, and ITI) can help the construction of intelligence-friendly classrooms that enrich the classroom environment and promote talent development (Barab & Plucker, 2002). Although a large body of research has been conducted on the structure of intelligence and intelligence theories (Renzulli & 8 Reis, 1997), there is little research focusing on educators’ self-perceived knowledge, skills, attitudes, and efficacy towards using these four theories to make the classroom a smart context. Therefore, this study aims to take an initial step towards developing an intelligence-informed competency framework by investigating educators’ self-perceived knowledge, skills, attitudes, and efficacy in constructing intelligence-friendly classrooms. 1.3. Purpose of the Study Scholars have emphasized the increasing importance of an inclusive and egalitarian ideology to construct an education that is gifted for all students by recognizing, celebrating, and cultivating various human intelligences, talents, and endeavours (Barab & Plucker, 2002; Lo et al., 2022; Lo & Porath, 2017). While these concepts have been emphasized, there is a gap of knowledge with regards to how to actualize them in classroom settings. The present study aims to add to the body of literature on elaborating the construct of an intelligence-friendly classroom and understanding educators’ knowledge, skills, attitudes, and self-efficacy towards constructing an intelligence-friendly classroom via looking into four theories (FCI, MI, SI, and ITI) that all-together address the cognitive, sociocultural, developmental, and motivational aspects of intelligence within educational settings. 1.4. Key Terminology Strengths: According to Saleebey (2006), almost anything—such as potential, talent, or intelligence—can be considered as a strength. This term is defined as a set of personal competencies and characteristics valued by both the individual and the society in which the individual exists (Rawana & Brownlee, 2009). In this study, strengths refer to students' unique cognitive, motivational, developmental, and sociocultural capacities, potentials, talents, and intelligences, informed by intelligence theories within the IFC framework. 9 Educators’ knowledge: There are various types of knowledge identified in the literature, including subject knowledge, knowledge of learners, knowledge of teaching methodology, curriculum knowledge, general pedagogical knowledge, contextual knowledge, and knowledge of self (Liakopoulou, 2011). This study defines educators’ knowledge as a form of subject knowledge, characterized by factual information and conceptual understanding (Zhu et al., 2019). Specifically, it refers to educators’ understanding and familiarity with the four intelligence theories (i.e., FCI, MI, SI, ITI) within the IFC framework. Educators’ skills: Skills are defined as the ability to apply knowledge in practice (Zhu et al., 2019. In the present study, this refers to educators’ competencies to apply four intelligence theories (i.e., FCI, MI, SI, ITI) within the IFC framework to create intelligence-friendly classroom settings. Educators’ attitudes: Attitudes are defined as the beliefs, values, and dispositions individuals hold toward a concept or practice (Zhu et al., 2019), as well as predispositions to evaluate various issues as favorable or unfavorable (Andronache et al., 2014). In this study, the term refers to educators’ dispositions toward creating intelligence-friendly classrooms through the implementation of the IFC framework. Educators’ self-efficacy: Self-efficacy refers to individuals’ beliefs in their own capabilities to organize and execute the courses of action required to manage prospective situations (Bandura, 1997). This concept is closely related to agency (Min, 2019), as it enhances individuals’ belief in their capabilities (Frost, 2006). In this study, educators’ self-efficacy refers to their confidence, willingness, and perceived capacity to create and sustain intelligence-friendly classroom environments. 10 Chapter 2: Literature Review The aim of the study is to investigate educators' self-perceived knowledge, skills, attitudes, and efficacy towards constructing intelligence-friendly classrooms through utilizing FCI, MI, SI, and ITI theories in order to address the cognitive, sociocultural, developmental, and motivational aspects of intelligence. Understanding educators' current professional competencies is critical for effectively translating the theoretical underpinnings of these theories into practical classroom strategies that promote talent development in all students. In line with this, in this chapter, strengths-based education is first discussed to establish the conceptual foundation of the study. Next, to provide a comprehensive understanding of intelligence as a construct, a theoretical foundation is established by examining the historical development and major approaches of intelligence. Following this, the intelligence-friendly classroom framework is introduced as an initial conceptual consisting of four intelligence-related theories (FCI, MI, SI, and ITI) to address four aspects of intelligence: the developmental aspect (FCI), sociocultural aspect (MI), cognitive aspect (SI), and belief-based/motivational aspect (ITI). The theoretical assumptions and educational implementations of each theory are examined to deepen understanding and explore their applications in classrooms. Utilizing the theoretical frameworks of all the aforementioned theories for the intelligence-friendly classroom framework, a table summarizing their fundamental principles is then provided. To understand the current educational environment in British Columbia (BC) in relation to the concept and the practices of intelligence-friendly classrooms, some teacher training programs in BC are examined to enhance the researcher’s understanding of the research context. Finally, the research questions of the study are presented at the end of this chapter. 11 2.1. Strengths-Based Thinking In this section, the historical process for the development of strengths-based approaches is outlined, followed by a summary of key research findings. After that, the strengths-based education approach is defined and related concepts are examined, including how theories of intelligence can contribute to students' strengths in educational contexts. 2.1.1. Historical Context of Strengths-Based Approaches Over the last twenty years, the focus on strengths-based approaches has emerged in multiple disciplines, including counseling, psychology, and education for example (Gordon & Gucciardi, 2011). Its emergence reflects a gradual but significant shift from deficit-focused models, which center on correcting problems, toward frameworks that emphasize human potential, assets, and capabilities (Smith, 2006). The philosophical foundations of strengths-based thinking can be traced back to early humanistic psychologists (Maslow, 1954; Rogers, 1961). Both Rogers and Maslow opposed the medical model of diagnosis and the idea of 'fixing' problems. For example, Abraham Maslow (1954), in his work on motivation and self-actualization, criticized conventional psychology for being too pathologizing, pointing out that psychology had been paying a lot of attention to human "shortcomings" but not enough to "potentialities," "virtues" and "full psychological height" of human beings. This call to look at what is right with people served as the basis for further development of positive psychology. From the mid-1950’s, Donald O. Clifton, acknowledged by the American Psychological Association (APA) as the “Father of Strengths Psychology and the grandfather of positive psychology” (McKay & Greengrass, 2003), spent five decades studying what is right about people, focusing on the positive instead of the negative. To better explain the perspective in this approach, he stated that strengths science answers questions about what's right with people rather 12 than what's wrong with them. Moreover, Drucker (2018) argued that “the effective executive” leverage their own strengths, the strengths of colleagues and superiors, and the situation’s strengths. This means it is more effective to build teams around strengths rather than job descriptions. The emergence of positive psychology in the late 1990s with Martin Seligman (Seligman & Csikszentmihalyi, 2000), further formalized the scientific study of human strengths and flourishing. Seligman’s collaboration with Christopher Peterson led to the development of the VIA Inventory of Strengths (Peterson & Seligman, 2004), offering a taxonomy of core virtues and character strengths, as well as the development of other assessment tools such as StrengthsFinder (Rath, 2007), Realise2 Strengths (Linley et al., 2010b), and Devereux Student Strengths assessment (LeBuffe et al., 2009). These tools contributed to a growing empirical base that validated the psychological and behavioral benefits of focusing on strengths. In his book Flourish, Seligman (2011) also introduced the PERMA model—standing for Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment—as a framework for conceptualizing well-being and flourishing. Research has demonstrated that character strengths are positively associated with all five PERMA dimensions (Wagner et al., 2020), which indicates a strong alignment between the PERMA model and strengths-based approaches. Alongside these developments, implementing strengths-based approaches in various settings has shown considerable benefits for individuals, children, and adolescents. Research highlights a range of positive outcomes, including increased autonomy, self-esteem, life satisfaction, and resilience, as well as decreased substance use, absenteeism, and behavioral issues (Madden et al., 2020; McCammon, 2012; Proctor et al., 2011). Linley et al. (2010a) found that individuals who actively use their strengths tend to experience greater happiness, higher self- 13 esteem, lower stress levels, and enhanced workplace engagement and performance. Similarly, Park and Peterson (2008) emphasized that academic achievement is shaped not only by cognitive ability but also by other strengths like character strengths such as perseverance, gratitude, and hope. Therefore, they suggested that these strengths should be identified, nurtured, and systematically developed through individual and group-based interventions. Despite growing evidence supporting strengths-based practices, some critiques persist regarding their limited empirical grounding, especially in field of social work (Gambrill, 2014; Gray, 2011). Some researchers (Biswas-Diener et al., 2011) also observed that many healthcare professionals adopting a strengths-based approach often rely on a simplistic “identify and use” method, which reflects limited guidance on effective implementation. In this context, Taylor (2006) cautioned against using only a strengths-based approach without medical treatment approaches in healthcare settings. Moreover, critics have pointed out that a strong emphasis on strengths can sometimes lead to a reluctance to receive constructive criticism or address areas of weakness, which are necessary for growth and performance improvement (Chamorro-Premuzic, 2016; Sandage & Hill, 2001). Without opportunities for critical feedback, individuals may over-rely on their strengths and overlook important skill development or behavioral changes. From a social constructionist and systemic perspective, Wilding and Griffey (2021) argue that applying this approach without critically reflecting on its underlying assumptions may overlook the complex, contextual nature of personal strengths. Additionally, the expression of strengths is shaped by cultural context; what is viewed as a strength in one culture may not be perceived similarly in another. For instance, autonomy is often valued in individualistic cultures, whereas collectivist cultures prioritize social harmony and interdependence (Smith, 2006). Nonetheless, a growing body of research continues to demonstrate the effectiveness of strengths-based strategies. 14 Regularly applying one’s strengths has been linked to enhanced well-being, resilience, and performance (Govindji & Linley, 2007; Linley et al., 2010a). In school contexts, strengths-based and positive psychology interventions that encourage students to identify and apply their strengths have been associated with increased life satisfaction, improved self-esteem, and reductions in behaviour issues (Proctor et al., 2011; McCammon, 2012). Overall, the strengths-based approach represents a dynamic and evolving paradigm. Its foundation is rooted in an interdisciplinary journey, with applications across fields such as psychology, management, social work, and education. At its core, it emphasizes the importance of recognizing and leveraging individuals' strengths to help them thrive. 2.1.2. Strengths-Based Education The strengths-based education approach is both a philosophical stance and a teaching-learning philosophy that emphasizes "the positive aspects of student effort and achievement, as well as human strengths" (Lopez & Louis, 2009, p. 1). In this approach, “strength” is seen as a natural ability or area of interest where individuals experience enjoyment, motivation, and repeated success with relative ease (Bozic et al., 2018; Clifton et al., 2006; Linley, 2008). According to Saleebey (2006), almost anything can be viewed as a strength, including intelligence, talent, and potential. A fundamental tenet of this approach is that each person has resources they can use to succeed in a variety of aspects of their lives (Clifton et al., 2006; Lo et al., 2019). For example, all students have potential wherein educators can do well to catalyze by implementing learning experiences that can assist their students in understanding and realizing this potential (Lopez & Louis, 2009). Furthermore, the strengths-based philosophy assumes that leveraging one's strongest qualities will increase the likelihood of success in overcoming one's own weaknesses or shortcomings (Clifton & Harter, 2003; Lopez & Louis, 2009). Hence, it 15 investigates ways to draw out individuals' strengths (Lopez & Louis, 2009) and to empower them to flourish rather than just survive (Liesveld et al., 2005). For example (Brownlie & Schnellert, 2009; McGlynn-Stewart, 2015), teachers can engage students in discussions about the idea that everyone has both strengths and challenges. This can be done by valuing students’ prior experiences, abilities, and knowledge. Teachers can also empower students metacognitively to work on their stretches by supporting the development of their thinking and learning processes. In this way, students begin to concentrate on their assets—their knowledge, skills, experiences, and interests. The core assumptions of the strengths-based approach also align with the concept of "funds of knowledge" (Gonzalez et al., 2005; Hogg, 2011; Moll et al., 1992). The concept of funds of knowledge asserts that individuals possess competence and knowledge as a result of their life experiences (Clifton & Harter, 2003; Hogg, 2011; Liesveld et al., 2005; Moll et al., 1992). This idea is closely related to what is commonly referred to as an asset-based approach (Flint & Jaggers, 2021). Asset-based approaches emphasize the cultural, familial, and community resources that learners bring with them, framing these as valuable foundations for learning (MacLeod & Emejulu, 2014). While both strengths-based and asset-based approaches aim to move away from deficit perspectives, they differ in emphasis: asset-based approaches focus more on external and contextual resources, whereas strengths-based approaches emphasize internal traits such as talents, character strengths, and personal resilience (Lopez & Louis, 2009). Asset-based pedagogies value students’ languages, cultural practices, and insights, while also aiming to critique social injustices and systemic oppression (MacLeod & Emejulu, 2014). Put more simply, the practices and knowledge individuals develop throughout their lives hold value, regardless of their social status, ethnicity, language, or financial situation. These abilities and 16 insights can be used as cultural resources for educational purposes in schools (Esteban-Guitart, 2014; Gonzalez et al., 2005; Schnellert et al., 2015). Furthermore, the funds of knowledge become the funds of identity when people utilize them to define themselves (Saubich & Guitart-Esteban, 2011). This idea of "funds of identity" allows us to describe the knowledge, talents, skills, and ways of life embedded in a community's everyday practices, which are influenced by broader cultural factors (Esteban-Guitart & Moll, 2014). For example, understanding and acknowledging a community's "funds of identity" empowers us to purposefully tap into and build upon these aspects, thereby enhancing the well-being of all its members. Despite its strengths, there is also potential limitations of the funds of knowledge perspective. While it highlights the value of lived experiences, critics warn that it may overlook structural inequities such as economic (e.g., poverty) and education-related injustices (Lubienski, 2003; Oughton, 2010; Zipin, 2009). For instance, Lubienski (2003) states that conflating funds of knowledge with cultural capital can obscure the realities of educational inequality by implying that all students have equally recognized and valued forms of capital. Nevertheless, it is critical to emphasize people's talents, knowledge, and life experiences as resources (i.e., as their funds of knowledge and funds of identity) so that we can think of them in terms of their strengths and resources rather than their supposed weaknesses (Esteban-Guitart, 2014). Building on this foundation, this dissertation contributes to the strengths-based approach by focusing on the diverse forms of intelligences in the society and by helping students realize their potentials and talents through the promotion of FCI, MI, SI, and ITI theories. Accepting intelligence as a multifaceted construct rather than a singular entity (Gardner, 1983; Horn & Cattell, 1966; Sternberg, 1985), the first three theories (FCI, MI, and SI) contend that every individual has their own unique set of talents and gifts (Glaveanu & Kaufman, 2021). This 17 perspective aligns with strengths-based education, which focuses on recognizing and nurturing the potential of each person (Clifton & Harter, 2003; Lopez & Louis, 2009). Further, ITI theory, concentrating on the motivational aspects of intelligence, can help students develop a positive self-evaluation, fostering beliefs in their own talents and potentials (Passarelli et al., 2010). Accepting that every student comes to the classroom as an individual with a unique form of intellect (Ackerman, 2011), these theories can inform teachers about their students' potential and strengths by offering a thorough understanding of the nature and growth of human potentials and capabilities (Lo et al., 2019). For example, these theories may assist teachers in identifying various forms of intelligence—such as cognitive, practical, or motivational—as student strengths or assets (Gardner, 1983; Passarelli et al., 2010; Sternberg, 1985). Specifically, teachers can use a variety of classroom assessment approaches such as classroom observations, student work, and student self-assessment and reflections to recognize specific strengths aligned with these intelligences and adjust their teaching methods accordingly. Consequently, FCI, MI, SI, and ITI theories have the potential to contribute to strengths-based education by focusing on the multiple intelligences in society and by informing teachers on how to recognize and cultivate students' unique potentials (Lo et al., 2022). In the context of this dissertation, strengths-based education refers to an educational philosophy and pedagogical approach that uses a framework of multidimensional intelligence theories to recognize and develop each student's unique abilities, intelligences, talents, and potentials. Within this framework, strengths are not limited to academic abilities but also include a wide spectrum of cognitive, practical, creative, and motivational traits. This definition positions FCI, MI, SI, and ITI as beneficial tools in identifying these strengths and informing responsive, inclusive teaching strategies within intelligence-friendly classrooms. The major advantage of emphasizing 18 multidimensional intelligence theories and constructing an intelligence-friendly classroom is that it helps students understand their personal talents/gifts, which speaks to the personal dimension of strengths-based education. While strengths-based education includes various dimensions—including personal, family, community, and societal—(Clifton & Harter, 2003), this thesis specifically focuses on intellectual abilities as part of the personal dimension, highlighting the role of FCI, MI, SI, and ITI theories in fostering individual growth. In this context, developing one's talents is the first step in realizing one's full potential (Clifton et al., 2006; Clifton & Harter, 2003; Lopez & Louis, 2009). Once dominant talents and intelligences are identified and capitalized with knowledge and skills, they may develop into strengths (Clifton & Harter, 2003; Passarelli et al., 2010). Simply stated, the strengths-based approach emphasizes the need to transform talents into strengths in order to operate at the levels of self-actualization (Yılmaz & Akpınar, 2020). By exploring the historical context and major approaches of intelligence, this dissertation seeks to provide a deeper understanding of how intelligence theories can be integrated into the intelligence-friendly classroom framework within the concept of strengths-based education. 2.2. Historical Context and Major Approaches of Intelligence There has been curiosity about the human intellect at least as far back as Plato and Aristotle (Plucker & Esping, 2014). However, the first formal research on intelligence appeared in the late 19th and early 20th centuries. Francis Galton, a pioneer in intelligence research (Kaufman, 2009), investigated the relationship between intelligence and heredity through genealogical studies (Galton, 1869). He found a higher frequency of successful relatives compared to the general population. Additionally, Galton's twin studies (Galton, 1876) demonstrated that identical twins exhibited more similarities in cognitive abilities than fraternal 19 twins. All of these studies led him to believe that intelligence was hereditary. Galton's ideas laid the groundwork for the development of intelligence testing and significantly influenced subsequent researchers, such as Alfred Binet and Lewis Terman. Alfred Binet, at the request of the French government, developed the first practical IQ test in 1905 to distinguish and identify students who required special educational assistance (Cohen & Swerdlik, 2002; Fancher, 1996). Binet's test was designed to measure various cognitive abilities and provided a more systematic way to assess intellectual capacity (Binet & Simon, 1948). In 1916, Lewis Terman adapted Binet's test for use in the United States, creating the Stanford-Binet Intelligence Scales (Terman, 1948). However, controversy was not absent with such developments in the scientific study of intelligence. For example, the eugenics movement influenced by Galton's work promoted the idea of inherited intellectual superiority and contributed to discriminatory policies based on race and class (Winston, 2020). Similar to this, adaptation by Terman of US IQ testing served educational tracking and exclusionary immigration laws, usually marginalizing minority and working-class populations (Katz, 2022). These early intelligence tests were frequently biased in favor of white (i.e., Western cultural norms), which led to misrepresentation of intellectual ability in diverse student populations. Recognizing these critiques is crucial to understand the broader historical and ethical implications of the study of intelligence. In these early years, Charles Spearman (1904) also laid the groundwork for the psychometric approach with his work on general intelligence (g-factor). He employed statistical methods to analyze individual differences in cognitive skills, providing evidence for a single underlying factor that contributes to performance across various mental tasks. In later years, scholars influenced by this psychometric approach accepted the concept of general intelligence 20 but suggested that specific areas of intelligence could range from 2 to as many as 180 (Carroll, 1993; Guilford, 1988; Horn & Cattell, 1966; Spearman, 1904). Since the mid-20th century, there have been significant transformations in the concept of intelligence. Alternative frameworks have challenged the dominance of the psychometric approach by highlighting the impact of the environment, development, and belief systems on intelligence (Davidson & Kemp, 2011; Sternberg, 1985, 1997). For example, Piaget (1964) and Vygotsky (1978) supported the idea that intelligence develops through interactions with the environment and social experiences. In 1983, Gardner introduced the theory of multiple intelligences, stating that intelligence can manifest in at least seven independent ways. He later added two more areas to his theory: naturalistic intelligence and existential intelligence (Gardner, 1999). Additionally, Sternberg (1985) also opposed the psychometric approach with his triarchic theory of intelligence and emphasized the importance of environmental and cultural components. Currently, research is increasingly focused on emotional intelligence (Fernández-Berrocal & Extremera, 2016; Martins et al., 2010; Penrose et al., 2007) and social intelligence (Dolev & Leshem, 2017; Hen & Sharabi-Nov, 2014), with a growing conversation about practical intelligence (Ferkany, 2020; Lievens & Chan, 2010; Sternberg et al., 2021). There is also research on the interaction between intelligence and the environment for talent development (Gentry, 2021; Plucker et al., 2021), the malleability of intelligence throughout life (Blackwell et al., 2015; Lou & Noels, 2020; Molway & Mutton, 2019), and the development of more culturally-sensitive intelligence perspectives (Ford, 2008). Additionally, it is important to acknowledge that the psychometric approach, particularly IQ testing based on the Cattell-Horn-Carroll (CHC) theory of intelligence, continues to hold considerable influence, especially within 21 the field of psychology (Bartholomew et al., 2009; Conway et al., 2021). This reflects the ongoing tension in the field between traditional and alternative frameworks. In this study, to provide an understanding of how intelligence theories conceptualize and investigate intelligence as a construct, the approaches that these theories belong to are organized and discussed in four main categories: (1) the psychometric approach, (2) the cognitive-contextual approach, (3) the belief-based (motivation-based) approach, and (4) the biological approach. Each approach has its own hypotheses, concepts, and research methods for comprehending the structure of intelligence, reflected in the categorization (Davidson & Kemp, 2011). While some approaches, such as the biological approach, do not directly influence the intelligence-friendly classroom framework proposed in the study, they are included to provide a comprehensive historical context and demonstrate the diverse perspectives on intelligence. Psychometric Approach. The psychometric approach, originating with the work of Charles Spearman (1904, 1923), focuses on individual differences in cognitive abilities through statistical analysis, aiming to identify latent factors underlying variations in test performance (Davidson & Kemp, 2011; Fagan, 2011; Gardner, 2011; Wagner & Sternberg, 1984). The main assumption is that the overall structure of intelligence can be revealed by the interrelationship of test scores (Davidson & Kemp, 2011). Psychometric theories have been dominated by questions regarding the structure of human intelligence, especially the role of general intelligence (Gardner, 2011). In a typical psychometric study, several cognitive ability tests, such as vocabulary, number series, general knowledge, and so on, are conducted to a large number of people. After scoring the tests, the correlation matrix is examined using mathematical techniques to uncover latent dimensions of cognitive ability. Different psychometric theories have utilized different sets of factors to elucidate the formation of cognitive abilities (Wagner & Sternberg, 22 1984). For instance, Spearman (1904, 1923) claimed that success on all mental ability tests was pervaded by a single general intelligence (the g-factor). On the other hand, Thurstone (1938) described intelligence as a set of seven primary mental abilities. Similarly, Guilford (1967, 1982) suggested that there are at least 180 factors of intelligence, derived from the interaction of five operational categories, six product categories, and five content categories. This approach has informed the foundation of contemporary intelligence tests, such as the Stanford-Binet and Wechsler Intelligence Scales, which are widely utilized in educational settings (Rose & Fischer, 2011). The most well-known theories in this approach are Spearman’s general intelligence or g factor (1904), Thurstone's Primary Mental Abilities Theory (1938), Cattell’s Theory of Fluid and Crystallized intelligence (1971), and Carroll’s Three-Stratum Factor Analytic Theory of Cognitive Abilities (1997). Cognitive-Contextual Approach. Cognitive-contextual theories seek to explain intelligence based on the context, as well as to embed intellectual processing in environmental or cultural milieu (Gardner, 2011). Going beyond the individual test performances used by psychometric theorists, cognitive-contextual theories have strong and diverse claims. Gardner (1983), for example, declared that there are at least nine different types of intelligence, some of which can be measured using psychometric tests (e.g., mathematical-logical intelligence, linguistic intelligence, or some aspects of spatial intelligence). They attempted to approach the concept of intelligence from a socio-cultural perspective, as opposed to the previously developed intelligence theories that disregarded the influence of sociocultural contexts. Therefore, due to environmental or cultural conditions, they claim that there may not be a single and universal intelligence (Gardner, 2011). Furthermore, these theories emphasize the importance of the development of learning ability underlying intelligence; therefore, they argue that testing 23 procedures should be designed to measure such learning ability in new situations (Fagan, 2011; Grigorenko & Sternberg, 1998). This approach features the mind’s function in actively perceiving and interpreting the environment (Gardner, 2011). The most well-known theories in this category are Gardner's Multiple Intelligence Theory (1983) and Sternberg's Successful Intelligence Theory (1985) – or Triarchic Intelligence Theory (the original name of Sternberg's theory). Notably, Gardner's theory is more focused on the contextual aspects of intelligence, emphasizing how cultural and environmental conditions shape the development of different intelligences. In contrast, Sternberg's theory is more cognitive, focusing on mental processes and how the three aspects of intelligence interact in real-world situations (Kaufman, 2018). Belief-Based (Motivation-Based) Approach. The belief-based approach to intelligence focuses on the role of personal beliefs and motivation in shaping how individuals perceive their intellectual abilities. It suggests that one's belief about the malleability of intelligence can influence their learning and development (Molway & Mutton, 2019). Carol Dweck, a key proponent of this approach, proposed that intelligence is not a fixed trait but can be developed through effort and hard work (Dweck, 1991). She introduced two different mindsets related to intelligence beliefs: (1) fixed mindset or the entity theory of intelligence and (2) growth mindset or the incremental theory of intelligence. Intelligence is viewed as an unchangeable, stable, and global feature in the entity theory, whereas it is viewed as flexible and can be improved by effort in the incremental theory (Dweck & Bempechat, 2017). According to research (Blackwell et al., 2007), these two implicit theories of intelligence are widely held among lay people, with some holding a fixed entity view of intelligence (called entity theorists) and others holding a malleable and increasable view of intelligence (called incremental theorists). Therefore, the Implicit Theories of Intelligence can be considered as the most well-known theory in this category. 24 Biological Approach. The biological approach consists of biological theories that investigate the concept of intelligence by emphasizing the relationship between intelligence and brain functions. In this approach, researchers study the brain and human body (e.g., hormones) to understand intelligence (Mcdaniel, 2005; Witelson et al., 2006). Recent studies in cognitive neuropsychology have sparked expectations that we may be able to identify the specific biological and physiological systems that underpin intelligence (Gardner, 2011). Research into the biological processes of the brain and human body, such as cerebral glucose metabolism, nerve conduction velocity, and changes in brain anatomy, may provide new insights into what intelligence is (Neisser et al., 1996). For example, using Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) scans to explain individual differences in intellect. This approach has the potential to provide a deeper understanding of the structure of intelligence because the brain is such a complicated organ and our knowledge of it is still in a nebulas stage (Gardner, 2011). The relations of intelligence with brain size (Vernon et al., 2000), EEG String Length (Liu et al., 2008), and Glucose Metabolic Rate (Haier, 2003) are among the major studies in this category. As shown, the focus of the study on intelligence has evolved and been enriched over time. While traditional theories in the psychometric approach have often sought to define intelligence through singular dimensions, contemporary perspectives highlight its complexity, focusing on the contextual and intellectual processes within environmental or sociocultural settings. This shift emphasizes the pedagogical value of multifaceted intelligence constructs and the recognition and nurturing of multiple-dimensional intelligence in classrooms (Barab & Plucker, 2002; Lo et al., 2022). Therefore, the next section explores this multidimensional nature of intelligence, laying the groundwork for integrating intelligence-related theories that can 25 address the developmental, sociocultural, cognitive, and motivational aspects of intelligence in educational contexts. Before proceeding to the next section, it is important to clarify how the term intelligence is used throughout this study. Although alternative terms such as cognitive ability, capacity, or mental ability are frequently used in educational literature (Dweck, 2012; Galloway et al., 2020; Kaufman, 2018; Li & Bates, 2019), this thesis intentionally retains the term intelligence to reflect both its historical significance and theoretical depth, as well as to highlight the integration of intelligence theories within IFC framework. The word intelligence originates from the Latin intelligentia or intellegentia, meaning “to understand” or “the faculty of understanding” (Davidson & Kemp, 2011; Carson, 2015) which aligns with this study’s focus on understanding learners' capacities through multiple theoretical lenses. 2.3. Intelligence as a Multidimensional Construct Pedagogically speaking, recognizing intelligence as multidimensional can allow for a more comprehensive understanding of human cognitive capabilities, which further supports educational implications that speak to the nexus of individual capabilities and a sociocultural context (Gardner, 1999; Krechevsky & Seidel, 1998; Lo et al., 2019). This ever-expanding understanding that intelligence is modifiable, multiple, and multifaceted has changed the way how teaching is taking place in classrooms (Bellanca, 1998). Assessments, for example, can be developed to measure a broader range of cognitive abilities by acknowledging the multidimensional nature of intelligence (Coppola et al., 2019; Grigorenko & Sternberg, 1998). This view provides an accurate and comprehensive assessment of an individual’s strengths and weaknesses, which facilitates individualized learning and development programs (Connor et al., 2018). In addition, a multidimensional understanding of intelligence encourages the design of 26 instructional programs that take students’ strengths and learning strategies into account (Gardner, 2006). For instance, discovering that some students excel in spatial concepts, while others have stronger language skills, can provide differentiated instruction and increase student engagement and achievement (Armstrong, 2009). Therefore, tailoring education to address various cognitive abilities helps maximize each student's potential (Barab & Plucker, 2002). Furthermore, this multifacet approach to intelligence promotes a more inclusive understanding by valuing different talents that are difficult to measure, such as art, music, and interpersonal skills (Gardner et al., 2018; Krechevsky & Seidel, 1998). Thus, the recognition and development of a wide range of human potential promote a more equitable and supportive environment for all individuals. Consequently, we can comprehend intelligence more deeply by considering its various and evolving facets (Nisbett, 2009). This multidimensional view can guide the development of more efficient evaluation techniques (Ford, 2008), educational programs that cater to diverse strengths (Gardner et al., 2018), and a more inclusive definition of intelligence that takes into account a larger spectrum of human potential (Sternberg, 1997). Asserting intelligence as a multifaceted construct rather than a singular entity (Gardner, 1983; Horn & Cattell, 1966; Sternberg, 1985), multidimensional intelligence theories contend that every individual has their own unique set of talents and gifts (Glaveanu & Kaufman, 2021) and lead to a unique set of pedagogical beliefs and practices. 2.4. Theoretical Foundations of the Intelligence-Friendly Classroom Framework: An Integrative Approach The Intelligence-Friendly Classroom (IFC) Framework introduced in this dissertation is presented as an initial conceptual proposal and integrative approach that aims to explore how inclusive and enriched learning environments might support students' diverse strengths and 27 intelligences. Grounded in the application of intelligence-related theories as a strengths-based educational approach (Lo et al., 2022), the framework addresses the inclusion of four key aspects of intelligence: developmental, sociocultural, cognitive, and motivational. The aim of constructing such an inclusive framework is to highlight the possibility of having students’ potentials being recognized and developed in a supportive and dynamic educational context (Lo et al., 2022). As shown in Figure 1, each aspect is represented by a specific theory: the developmental aspect by Cattell’s Fluid and Crystallized Intelligence (FCI) Theory (Cattell, 1971), the sociocultural aspect by Gardner's Multiple Intelligence (MI) Theory (Gardner, 1983), the cognitive aspect by Sternberg's Successful Intelligence (SI) Theory (Sternberg, 1985), and the motivational aspect by Dweck’s Implicit Theories of Intelligence (ITI) (Dweck, 1991). Figure 1 Intelligence-Friendly Classroom Framework The developmental aspect emphasizes the dynamic nature of intelligence development, recognizing that skills and knowledge evolve over time, particularly through experiences and age-related growth (Horn et al., 1981; Horn & Cattell, 1967), which supports a lifelong learning 28 perspective (Zaval et al., 2015). Cattell’s FCI theory addresses this aspect by highlighting the interplay between fluid and crystallized intelligence as age increases (Brooks, 2022; Cattell, 1963, 1971). Although it is a theory grounded in the psychometric approach, its focus on the development of crystallized intelligence throughout the lifespan distinguishes it from other psychometric intelligence theories. The sociocultural aspect highlights the role of environment and cultural diversity in shaping the construct of intelligence (Esteban-Guitart & Moll, 2014). Gardner’s MI theory aligns with this aspect, recognizing diverse intelligences shaped by cultural and environmental factors (Gardner, 2006). Additionally, the cognitive aspect is crucial as well because it encompasses the mental processes necessary for problem-solving, decision-making, and adapting to new challenges (Gubbels et al., 2014; Sternberg, 2011). Sternberg’s SI theory addresses this aspect by emphasizing the teaching of analytical, creative, and practical thinking skills while taking real-world situations into account (Sternberg et al., 2006, 2009) . Finally, the belief-based (motivational) aspect centers on fostering a growth mindset, where students view intelligence as improvable through effort and perseverance (Dweck, 2000; Mrazek et al., 2018; Zeeb et al., 2020). This includes supporting students’ self-efficacy and perseverance by acknowledging the critical role of mindset in unlocking potential and sustaining effort in the face of challenges (Zander et al., 2018). Dweck’s ITI theory informs this aspect since it encourages the cultivation of a growth mindset towards the development of individual intelligences. The practical application of the IFC framework involves designing classrooms that integrate the four aspects of intelligence, each informed by a specific theory. This perspective aligns with calling on cultivating multiple intelligences, talents, and strengths (Guardino & Fullerton, 2010; Plucker et al., 2021). By addressing multiple aspects of intelligence, this framework encourages educators to design learning environments that recognize and cultivate 29 the unique strengths and intelligences of all students (Fogarty, 2008; Krechevsky & Seidel, 1998; Lo et al., 2019). Therefore, this new framework advocates for “an education that is gifted” in which all students are encouraged and supported in becoming gifted in an enriched learning environment (Lo et al., 2019). The FCI, MI, SI, and ITI theories within the IFC framework were selected based on three key criteria: (1) their provision of multiple perspectives on intelligence; (2) their potential for educational implementation; and (3) their widespread recognition (popularity), which allows for a comprehensive understanding of the current application of these theories in classrooms, particularly in relation to educators' knowledge, skills, attitudes, and self-efficacy. The general features, aims, and examples of each aspect of the framework were concisely introduced in Chapter 1.2 and further elaborated upon in the earlier paragraphs and the figure in this section. The following subsections, therefore, focus on the foundational theories that inform this framework to provide a deeper understanding of their applications. 2.4.1. The Theory of Fluid and Crystallized Intelligence (Developmental Aspect) The developmental aspect of the Intelligence-Friendly Classroom Framework is informed by The Fluid and Crystallized Intelligence (FCI) Theory, developed by Raymond Cattell (1963) and later expanded by his graduate student John Horn (1965). FCI is one of the most widely accepted and influential psychometric theories of intelligence (Gardner, 2011; Willis et al., 2011). It is also known as "Cattell's theory of fluid and crystallized intelligence" due to fact that Cattell has been associated to the concepts of fluid and crystallized intelligence (Brown, 2016). Cattell (1963) proposed that spearman’s g (i.e., general single intelligence) should be divided into two distinct but correlated factors: fluid intelligence (Gf) and crystallized intelligence (Gc) (Mackintosh, 2011). The goal of this separation was to account for people's cognitive 30 development as they progressed through adolescence and adulthood (Davidson & Kemp, 2011) because according to this theory, these two types of intelligence (Gf and Gc) show different developmental patterns with age (Brown, 2016; Cattell, 1963; Rose & Fischer, 2011). Intelligence within this theory can be described mathematically as follows: I = Gf + Gc (I) represents the total intelligence score of the person whereas (Gf) and (Gc) are expressing fluid ability and crystallized ability respectively. Gf, according to Cattell (1941), is derived from genetic and biological impacts, whereas Gc is mostly influenced by environmental factors such as education and socioeconomic status. Fluid intelligence tends to increase until adulthood and then decline and diminish over the course of a lifetime, whereas crystallized intelligence prone to largely maintain or improve until old age (Hertzog, 2011; Horn et al., 1981; Horn & Cattell, 1967; Nisbett et al., 2012; Rose & Fischer, 2011). Therefore, crystallised and fluid IQ are facets of intelligence where educators can draw much on their developmental implications into their classroom (Nisbett et al., 2012). Although Cattell's theory of fluid and crystallized intelligence has significantly contributed to our understanding of cognitive development, it has also received criticism. For example, some critics (Ackerman, 2000; Keith & Reynolds, 2010; Schroeders et al., 2021) argue that crystallized intelligence (Gc) is conceptually unclear because the dispute over whether Gc exists beyond being a function of factors such as Gf, learning interest, learning time, and material quality remains unresolved. In line with this criticism, Kan et al. (2011) highlighted a paradox in interpreting Gc, suggesting that if Gf fully accounts for the shared variance among crystallized abilities, then Gc may be a statistical artifact rather than a distinct psychological construct. Supporting this ambiguity, Ferrer and McArdle (2004) found no developmental relationship 31 between Gf and Gc from childhood to early adulthood, although a separate analysis using longitudinal data revealed a positive association between general intelligence (g) and reading achievement (Ferrer et al., 2007). Despite these criticisms, Cattell's theory continues to serve as a foundational framework in the study of intelligence. Connection to the CHC Model. Building upon the foundational principles of the fluid and crystallized intelligence theory, the Cattell–Horn–Carroll (CHC) model represents a more comprehensive and integrative framework (McGrew, 2009). It emerged through McGrew (1997, 2012)’s synthesis of Cattell and Horn’s Gf-Gc theory with Carroll’s (1993) three-stratum model of cognitive abilities, resulting in a hierarchical structure that organizes cognitive functioning across general, broad, and narrow levels (McGrew, 2005; McGrew & Flanagan, 1998; Schneider & McGrew, 2012, 2018). While fluid (Gf) and crystallized (Gc) intelligences remain core components, the CHC model expands to include a range of broad abilities such as visual-spatial processing, auditory processing, processing speed, quantitative reasoning, and long-term retrieval (Flanagan et al., 2014). These dimensions offer educators and psychologists a more nuanced understanding of learners’ intellectual profiles, and are widely applied in diagnostic tools such as the Woodcock–Johnson IV and Wechsler scales (Taub et al., 2008). In addition, Schneider and McGrew (2018) proposed incremental revisions to the CHC theory based on nascent signals in Carroll’s original work, framing the model as a flexible and evolving conceptual structure. This openness to revision has gained further momentum with more recent theoretical developments. More recently, McGrew et al. (2023) advocated for a multidimensional reconceptualization of the CHC framework, encouraging researchers and practitioners to revisit the structural assumptions of CHC theory through methods like psychometric network analysis 32 (PNA), which offer a more flexible and interconnected understanding of cognitive abilities. Using psychometric network analysis (PNA), McGrew’s findings raise questions about the traditional emphasis on a singular general intelligence (g), and instead highlight the interconnectedness and potential distinctiveness of multiple broad CHC abilities. This shift is particularly relevant in educational contexts aiming to support diverse learners and align with inclusive pedagogical principles. In this regard, the CHC model, especially in its evolving multidimensional formulation, provides a valuable cognitive foundation for the Intelligence-Friendly Classroom (IFC) Framework by promoting differentiated instruction and recognizing the diverse cognitive potentials of all students (McGrew, 2023; Meyer & Reynolds, 2022). While the present study adopts a more basic fluid and crystallized intelligence (i.e., FCI theory) perspective due to its focus on integrating three other major theories (i.e., MI, SI, and ITI) into the IFC framework introduced in this study, future research may benefit from incorporating this expanded CHC model to further enrich the theoretical and practical understanding of intelligence in educational settings. The two types of intelligence in the FCI theory are discussed in more detail below. Fluid Intelligence. Gf, or fluid intelligence, refers to the ability to solve problems through reasoning and mental activities (Cattell, 1971; Horn & Cattell, 1966; Mackintosh, 2011; Willis et al., 2011). Cattell (1971) defined Gf as the ability to solve new problems on the spot and to learn something new. Comprehensively, Gf can be described as the ability to think, reason, and process information (Horn & Cattell, 1967); to solve unexpected or unfamiliar problems (Hunt, 2010); to deduce relationships between stimuli, interpret consequences, and draw inferences (Horn & Noll, 1997); and to inductive, deductive, and quantitative reasoning with new materials and processes (Willis et al., 2011). It is also associated with the speed of 33 thinking and short-term memory (Shahabi et al., 2014). Individuals with higher fluid abilities can think and act more rapidly, handle new challenges more resourcefully, and encode short-term memories more swiftly (Willis et al., 2011). Therefore, Gf involves tackling new tasks and experiences (Kvist & Gustafsson, 2008; Rose & Fischer, 2011) and processing novel knowledge efficiently (Davidson & Kemp, 2011). Hence, fluid intelligence is sometimes linked to creativity because creative activities are fluid in nature and requires people to address challenges that are unknown or innovative (Rose & Fischer, 2011). Additionally, Gf is dependent on the proper functioning of the central nervous system (Davidson & Kemp, 2011) and requires an integration of verbal and nonverbal thinking when solving new tasks (Willis et al., 2011). In the original account of Cattell, Gf was significantly influenced by Spearman's theory of g (Horn & McArdle, 2007; Mackintosh, 2011). Similar to the general intelligence theory, Gf initially was thought to be the biological foundation of intelligence, and Gc as the manifestation of the capacity in the accumulated information gained as a result of cultural experience (Mackintosh, 2011). However, studies (Carroll, 2003; Kvist & Gustafsson, 2008) have shown that the g = Gf discovery has not been repeated in all research, while others argue (e.g., Johnson et al., 2008) that g and Gf are the same construct. Debates about whether g and Gf represent distinct constructs or are fundamentally the same constructs remain unresolved (Kyllonen & Kell, 2017). Crystallized Intelligence. According to researchers, crystallised intelligence (Gc) is the knowledge gained through the use of one's fluid ability in a variety of situations (Hertzog, 2011; Nisbett et al., 2012), the application of that knowledge to solve problems that are broadly familiar (Willis et al., 2011), the acculturation of knowledge representing the dominant culture (Horn & Noll, 1997), and the ability to apply skills, knowledge, and experience learned (Neugnot-Cerioli et al., 2017). Simply, Gc means acquired knowledge and learned skills, as well 34 as their application (Cattell, 1971; Horn & Cattell, 1966; Mackintosh, 2011; Willis et al., 2011). Crystallised intelligence consists of various acquired skills that people collect and retain in their memories throughout their lives (Davidson & Kemp, 2011). Further, Gc is showed up in some tests of knowledge, general information, and use of vocabulary (Horn & Cattell, 1966). Although the majority of intelligence verbal subtests are classified as largely measuring crystallised intelligence; however, some subtests, such as Wechsler's similarities, obviously necessitate both fluid reasoning (Gf) and crystallised knowledge (Gc) in order to achieve high scaled scores (Willis et al., 2011). Educational Implementations. The hypothesis of fluid and crystallized intelligence is among the most influential factorial theories related to intelligence development (Willis et al., 2011). According to the Gf-Gc theory, crystallized intelligence (Gc) increases with age by accumulating more knowledge and skills through, allowing adults to channel their knowledge in specialized fields (Davidson & Kemp, 2011). In contrast, fluid intelligence (Gf) peaks early and declines with age, following different developmental paths (Kyllonen & Kell, 2017). This section will discuss the impact of schooling and acculturated learning on Gf and Gc development, as well as some educational implementations such as familiarity, intellectual engagement, and cognitive interventions that can enhance these intelligences within intelligence-friendly classrooms. Schooling and acculturated learning have an impact on the development of both Gf and Gc (Kyllonen & Kell, 2017). Specifically, Gc can be notably enhanced through formal education and acculturated learning (Hunt, 2010). For example, individuals who invest in developing Gc can make significant contributions to their society, which helps to explain why intellectual leaders in numerous professions are frequently beyond 40 years old (Davidson & Kemp, 2011). 35 Additionally, school education plays a crucial role in supporting the development of Gf and Gc in children, especially less able children, due to having an impact on both educational achievement and cognitive abilities (Schroeders et al., 2016). Using the Berlin Test of Fluid and Crystallized Intelligence, Schroeders et al. (2016) conducted research on 3rd grade pupils. What they found is that teacher-related variables such as teacher-student relationships (Cornelius-White, 2007) or quality of teaching (Klassen & Tze, 2014) have a significant impact on school learning. Furthermore, engaging in extensive and well-structured activities in specific domains has a potential to develop cognitive abilities relevant to proficiency, primarily sourced from Gf intelligence (Davidson & Kemp, 2011). For instance, training with the abacus can improve Gf (Irwing et al., 2008). Over time, tasks initially requiring fluid intelligence may crystallize into automatic skills (Rose & Fischer, 2011). An architecture student practicing visual-spatial challenges or a pupil memorizing times tables initially uses fluid intelligence, which later becomes automatic and instinctive (Rose & Fischer, 2011). Another study (Kaufman et al., 2009) reported that Gc, Gf, and the three academic skills of math, reading, and writing are all influenced by educational attainment and the number of years spent in formal schooling. Surprisingly, Gf was just as strongly associated with education as school-related Gc in the study, which shows there is a high interdependence between fluid and crystallized intelligence (Schroeders et al., 2016). Furthermore, several educational implementations can also contribute to the development of Gf and Gc. Familiarity with tests (Rudebeck et al., 2012), for example, increases test-specific skills, allowing individuals to improve their scores with repeated testing. Additionally, intellectual engagement, or the enjoyment of intellectual activities, correlates with levels of crystallized intelligence but not with fluid ability (Hülür et al., 2018). However, Neugnot-Cerioli 36 et al. (2017) found that game-based cognitive interventions, combined with the enjoyment of intellectual activity, can enhance not only Gc but also Gf, particularly in adolescents with below-average IQ. For instance, Gf-related games focus on planning, inhibition, fluid reasoning, and integration, while Gc-related games emphasize verbal elaboration, word generation, mental imaging, and grammar concepts. Finally, studies such as working memory training (Jaeggi et al., 2008) and attention control (Wang et al., 2013) have been found to also improve fluid ability tasks by training non-cognitive skills such as self-regulation and self-discipline. Consequently, longer educational programs (e.g., schooling) that focus on comprehensive skill development can significantly improve Gc, while shorter programs targeting test-taking skills primarily enhance Gf (Hunt, 2010). In conclusion, both fluid (Gf) and crystallized (Gc) intelligence can be enhanced through various educational strategies. Formal schooling and acculturated learning significantly improve Gc, while shorter, targeted programs benefit Gf (Kaufman et al., 2009). This highlights the need for a multifaceted educational approach that incorporates implementations like game-based learning to nurture Gf skills (e.g., problem-solving), and a curriculum that emphasizes critical thinking and knowledge application to strengthen Gc. 2.4.2. The Theory of Multiple Intelligences (Sociocultural Aspect) Considering classroom experiences, many teachers are aware that there are numerous ways for students both to perceive the world and to make meaning of their experiences (Krechevsky & Seidel, 1998). The theory of multiple intelligence (MI), proposed by Howard Gardner (1983), emerged as a response to the limitations of traditional IQ tests and the desire to acknowledge a broader range of human capabilities (Gardner, 1999). Gardner initially proposed seven distinct intelligences: linguistic, logical-mathematical, musical, bodily-kinesthetic, spatial, 37 interpersonal, and intrapersonal (Gardner, 1983). According to Gardner (1999), his theory of multiple intelligences (MI) is shaped by the cultural and educational values of different times and places. For instance, the growing importance of environmental awareness in the 1990s influenced Gardner, and decided the addition of the naturalistic intelligence (Gardner, 1995a) and existential intelligence (Gardner, 1999) into the theory. Due to its focus on recognizing individual strengths, the theory has been adapted and applied in various educational systems worldwide (Armstrong, 2009). Despite its popularity, the theory has faced substantial criticism, particularly regarding the lack of empirical evidence supporting the existence of distinct intelligences (Callahan et al., 1995; Waterhouse, 2006, 2023). Some critics argue that Gardner's intelligences overlap significantly and may not represent separate cognitive abilities as claimed (Shearer, 2020; Visser et al., 2006). In addition, although educators are favorably impressed with the theory's inclusivity, some researchers would argue that it does not represent a scientifically validated model of intelligence and is merely a pedagogical tool (White, 2008). These criticisms raise concerns about the theory’s ability to guide interventions in classroom settings. While selecting this theory, the aim is not to dismiss these issues; rather, it is to recognize the pedagogical use of the theory and its impact on educational practice today, especially in shaping inclusive and intelligence-friendly learning environments (Gardner & Moran, 2006). For example, CHC theory (Carroll, 1997), one of the psychometric theories, provided significant knowledge on understanding the structure of human cognitive abilities. However, Gardner's theory encourages educators take into account the sociocultural influences on intelligence, while thinking about intelligence (Gardner, 1999). For instance, whether a person's bodily-kinesthetic or spatial intelligences were developed through swimming or marine navigation are affected not 38 only by their natural ability, but also by contextual factors like the availability of water, time to practice, and a culture that allows for and values these capacities (Davies et al., 2011). Consequently, the two theories are not mutually exclusive with each other: one examines the structure of intelligence and the other focuses on more socio-ecological factors that can shape intelligence. In addition, the theory states that education needs to encourage a more personalized approach and curricula that are suitable for the diverse strengths of students (Armstrong, 2009; Krechevsky & Seidel, 1998). This is in line with contemporary educational values that prioritize a more inclusive and equitable understanding of human potential, supporting the idea that intelligence is multifaceted and culturally dependent (Gardner, 2006). Therefore, the study incorporates Gardner's theory into the IFC framework, not as a definitive or final scientific understanding of intelligence, but as a conceptual lens for creating learning environments that respect cognitive diversity. The nine intelligences in the theory are discussed below. Linguistic intelligence. Linguistic intelligence refers to the ability to understand, manipulate, and use language effectively, including reading, writing, speaking, and listening skills (Gardner, 1983; Saidi & Khosravi, 2021). Beyond mere communication, it includes sensitivity to the nuances of language, the capacity to learn new languages, and the strategic use of language for specific goals (Gardner, 1999). Additionally, the core elements of linguistic intelligence also include phonological and syntactic analysis, which emphasizes lexicon, syntax, and semantics (Brualdi Timmins, 1996; Yogatama et al., 2019). For example, rhetoric (using language to persuade others to take a particular action), mnemonics (using language to recall information), explanation (using language to convey detailed information), and metalanguage 39 (using language to talk about itself) are some indicators of linguistic intelligence (Armstrong, 2009). Logical-mathematical intelligence. Logical-mathematical intelligence is characterized by the capacity to analyze problems logically, to perform mathematical operations, and to investigate issues scientifically (Davis et al., 2011; Gardner, 1983). It involves reasoning, pattern recognition, abstract thinking, and quick problem-solving, all of which are essential cognitive skills (Campbell et al., 2006). According to Gardner (Gardner et al., 2018; Gardner, 1983), individuals with strong logical-mathematical intelligence excel at solving puzzles, performing complex calculations, and conducting experiments. Cognitive processes associated with logical-mathematical intelligence include categorization, classification, inference, generalization, calculation, and hypothesis testing (Armstrong, 2009). Spatial intelligence. Spatial intelligence refers to a person’s capacity to accurately comprehend the visual world and transform initial visual images in their minds, involving re-creating or reshaping these images (Davis et al., 2011; Gardner, 1983). Also known as visual-spatial intelligence, it encompasses interrelated skills such as visual discrimination, mental reasoning, and image manipulation (Campbell et al., 2006). A key characteristic of spatial intelligence is the ability to manipulate patterns of space and forms, utilized by professionals like navigators, sculptors, chess players, and e-sport players (Gardner, 2006; Gardner, 1999). Spatial intelligence involves sensitivity to color, line, shape, form, space, and the relationships between these elements, including the capacity to visualize, represent visual or spatial ideas graphically, and orient oneself appropriately in a spatial matrix (Armstrong, 2009). Bodily-kinesthetic intelligence. Bodily-kinesthetic intelligence refers to the ability to use one's entire body to create something new or solve problems (Gardner, 2004; Schewe, 2002). 40 This type of intelligence manifests in a variety of activities, from the physical effort required in sports, driving, piloting, and surgery to the expressive movements of dance, acting, and athleticism (Armstrong, 2009; Gardner, 2006). Bodily-kinesthetic intelligence encompasses a range of physical skills, including coordination, balance, dexterity, strength, flexibility, and speed, as well as proprioceptive, tactile, and haptic capacities (Armstrong, 2009). Simply put, it can be characterized by the control and purposeful use of one's body to achieve tasks (Fleetham, 2006). Individuals with strong bodily-kinesthetic intelligence excel in sports, dancing, acting, and using their hands to create or fix things (Gardner, 2006; Gardner, 1999). Musical intelligence. Musical intelligence includes the capacity to perceive, discriminate, transform, and express musical forms, with sensitivity to rhythms, tones, and melodies (Armstrong, 2009; Gardner, 1983). Since an endeavor to create music involves the composition of various musical components, this type of intelligence is closely associated with creativity (Gardner, 2006; Gardner, 1999). Individuals with high musical intelligence often excel in singing, playing instruments, composing music, and appreciating musical patterns. Ultimately, musical intelligence allows individuals to use music as a powerful tool for self-expression, storytelling, and emotional connection (Gardner, 2006). Interpersonal intelligence. Interpersonal intelligence refers to the ability to understand and interact effectively with others (Gardner, 2006). This intelligence involves perceiving and making distinctions in the moods, intentions, motivations, and feelings of other people (Armstrong, 2009; Davis et al., 2011). It includes sensitivity to facial expressions, voice, and gestures, and the capacity to discriminate among various interpersonal cues and respond effectively to them in a pragmatic way, such as influencing a group of people to follow a certain course of action (Armstrong, 2009). Understanding others, a key aspect of interpersonal 41 intelligence, involves not only perceiving verbal communication but also nonverbal communication like body language and facial expressions (Goleman, 2006). This understanding enables individuals to excel at reading emotions, empathizing with others' perspectives, and comprehending social cues (Gardner, 1983). People with strong interpersonal intelligence are good at building relationships, resolving conflicts, and fostering cooperation within groups. This natural ability to connect with others often leads them towards leadership roles, where they can motivate and inspire their followers (Gardner, 1983; Goleman, 2006). Intrapersonal intelligence. Intrapersonal intelligence involves the capacity to understand oneself, develop an effective self-model—including one's desires, fears, and abilities—and use this understanding to regulate one's life effectively (Davis et al., 2011; Gardner, 1999). Simply stated, it is the self-knowledge and the ability to act adaptively based on that knowledge (Armstrong, 2009). Individuals with high intrapersonal intelligence excel at setting personal goals, understanding their strengths and weaknesses, and maintaining their mental and emotional health (Armstrong, 2009; Gardner, 1983). Naturalist intelligence. Naturalist intelligence, a later addition to Gardner's theory of multiple intelligences, focuses on understanding and appreciating the natural world (Armstrong, 2009; Davis et al., 2011; Gardner et al., 2018). This intelligence involves the ability to recognize and classify the numerous species—flora and fauna—within an individual's environment (Davis et al., 2011; Gardner, 1999; Wirdianti et al., 2019). It also extends beyond plants and animals, including sensitivity to other natural phenomena like cloud formations (Armstrong, 2009; Gardner, 1999). Individuals with strong naturalist intelligence excel at observing nature, understanding ecological relationships, and engaging in activities related to biology, geology, and environmental science (Gardner, 1995b; Gardner, 1999). 42 Existential Intelligence. Existential intelligence refers to the capacity for a concern with ultimate life issues (Gardner, 1999). It includes sensitivity to value human culture and develop religious, mystical, metaphysical, aesthetic, philosophical, and scientific systems to address existential concerns (Armstrong, 2009). People with high existential intelligence are interested in philosophical thinking and understanding complex existential concepts. Although existential intelligence has not been fully endorsed as a distinct intelligence by Gardner (1999), he considered this intelligence as an important aspect of human cognition (Armstrong, 2009). Building on the nine intelligences we've explored above, MI theory advocates that people who exhibit a certain ability in one intelligence may not always exhibit a similar aptitude in another intelligence (Davis et al., 2011; Gardner, 2006). In today's modern schools, the language-logic combination, which we might refer to as “academic” or “scholar intelligence” (which, according to Gardner's theory, is linguistic and logical-mathematical intelligence), has been prized and tested for (Davis et al., 2011). Therefore, the MI theory may be used to change and broaden people's perceptions on intelligence potential, particularly those of teachers. Educational Implementations. The Multiple Intelligence (MI) theory has gained great attention internationally and has exerted a significant impact in education even though it was not originally intended to be a framework for school system (Gardner, 2006; Davidson & Kemp, 2011). Thus far, the theory has versatile influence on education globally, affecting areas such as personal development, curriculum development, teaching strategies, classroom management, and measurement and evaluation of intelligence (Armstrong, 2009). For example, it has shifted the focus on leadership from IQ scores to diverse sources that can shape up leadership (Gardner, 1995b). It has also been applied in adult literacy education and school curricula (Setiawan et al., 2020). As a result, this section will discuss the general effectiveness of applying MI theory in 43 classrooms and explore approaches such as individualized learning and project-based learning that enhance diverse intelligences within intelligence-friendly classrooms. MI theory offers students opportunities to explore and express their intelligences in culturally relevant and creative ways, further emphasizing the integration of sociocultural factors into the learning process (Krechevsky & Seidel, 1998). Hence, MI theory provides a framework for individualizing education by promoting the development of socially desirable abilities and utilizing personalized approaches to each student (Davidson & Kemp, 2011; Krechevsky & Seidel, 1998). Traditional schooling often values only math and language abilities (Gardner, 1999). However, MI theory posits that every individual can be intelligent in their own way by recognizing diverse intelligence areas, and therefore, an individualized approach is necessary to determine the differences of each student (Gardner, 1983, 1999). Research has shown that adopting MI principles helps foster a more student-centered and flexible learning environment (Callahan et al., 1995; Rumfot et al., 2024). Callhan et al. (1995) observed that MI-informed practices challenge traditional assessment limitations and highlight students’ non-traditional strengths, particularly benefiting low-income and minority students. Similarly, Campbell and Campbell (1999) found that MI theory’s application enabled teachers to identify students’ varied strengths, leading to personalized instruction and improved educational outcomes. Research by Akkuzu and Akcay (2011) supports the importance of tailoring instructional methods to students’ intellectual strengths, indicating that such approaches not only enhance academic performance but also cultivate positive attitudes toward learning. Therefore, it is crucial for educators to understand students' interests, strengths, and weaknesses, which may allow them to adapt teaching strategies accordingly (Armstrong, 2009). 44 Furthermore, research by Baş and Beyhab (2017) indicated that incorporating project-based learning supported by multiple intelligences into foreign language instruction improved students' achievement and fostered more positive attitudes toward the subject compared to traditional teaching methods. According to Mokhtar et al. (2008), project-based learning, guided by Gardner's MI theory, significantly enhanced students' information literacy skills and project performance by aligning the learning process with their dominant intelligences, in comparison to traditional methods. Therefore, project-based learning incorporating multiple intelligences (MI) principles engages various intelligences together, such as interpersonal and intrapersonal intelligences, while addressing the sociocultural aspects of learning through real-world applications (Krechevsky & Seidel, 1998). In conclusion, the Intelligence-Friendly Classroom Framework, informed by MI theory, incorporates a sociocultural perspective, recognizing the significant role of environmental factors in intelligence development. MI theory acknowledges that intelligence is not solely an innate attribute but is shaped by social interactions, cultural values, and environmental contexts (Gardner, 2006; Gardner, 2012). Therefore, integrating this framework into schools can help educators recognize and nurture students' diverse strengths within their sociocultural contexts. 2.4.3. The Theory of Successful Intelligence (Cognitive Aspect) The cognitive aspect of the Intelligence-Friendly Classroom Framework is informed by The Theory of Successful Intelligence (SI), which is the academically acknowledged example of intelligence that predicts success in real life (Eysenck, 1998). Proposed by Robert Sternberg and formerly known as the Triarchic Intelligence theory, this theory is contextual in nature in that it attempts to understand how cognitive-progressing related intelligences function in relation to its context (Eysenck, 1998; Gardner, 2011). Based on Sternberg's (2011) definition, intelligence has 45 three key characteristics: (1) strategic self-awareness, (2) contextual understanding, and (3) a balanced ability triad. Strategic self-awareness requires individuals to identify their strengths and weaknesses within a specific sociocultural context. This characteristic emphasizes self-awareness, and the strategic utilization of strengths while addressing weaknesses (Sternberg, 1997, 1999; Willis et al., 2011). Second, contextual understanding acknowledges that intelligence is not a one-size-fits-all concept. This characteristic highlights the ability to not only adapt to, but also shape and select environments to optimize personal achievement (Davidson & Kemp, 2011; Sternberg, 1997). Third, the characteristic of balanced ability triad emphasizes the importance of a balanced approach to intelligence, involving the utilization of a combination of analytical, creative, and practical abilities depending on the situation (Sternberg, 1997, 1999; Willis et al., 2011). How the successful intelligence theory works is shown in the Figure 2. Figure 2 How the Successful Intelligence Theory Works Note: Created from the definition of Sternberg (2011) Similar to Gardner's theory discussed previously, Sternberg's theory has been criticized for not having strong empirical validation. According to Gottfredson (2003) and Brody (2003), Sternberg's Triarchic theory provides a good conceptual framework, but there is insufficient evidence to support all claims of the theory in large-scale or standardized settings. Many of these 46 critiques arise from the research tradition of psychometric scholars who depend on factor-analytic methods and expect evident statistical relationships between constructs (Sternberg, 2003). Sternberg (2003), however, stated that he is not dismissing the idea of g (general intelligence); he is arguing that the notion of g is only one component, the analytical part, of a more complete picture of intelligence. In Triarchic Theory, he is suggesting two additional and equally important parts: creative and practical intelligence (Sternberg, 1985). Furthermore, he emphasizes the significance of gene–environment interactions, noting that environmental factors can shape how genetic potentials manifest, and vice versa (Sternberg, 2003). In addition to concerns about theoretical validity, other critics like Hunt (2008) also state that the theory’s practical applications, especially in educational settings, require more empirical examination to validate their effectiveness. However, Grigorenko et al (2002) demonstrated that triarchically based instruction and assessment was more effective than conventional instruction in improving student reading achievement. Other research has indicated that the theory has helped students to achieve academic success by encouraging them to use not only analytical but also practical and creative abilities (Gubbels et al., 2014; Sternberg et al., 2006). Although the theory may lack broad empirical validation, it has demonstrated potential in specific educational interventions and remains a valuable tool for understanding intelligence in real-world contexts. With this in place, Sternberg (1997) argued that successful intelligence is not a fixed entity but is the ability to accomplish goals within a specific sociocultural context. This ability is manifested through the interplay of three core abilities, analytical, creative, and practical facets of intelligence (Kaufman & Singer, 2004), which also supports the cognitive aspect of intelligence within the Intelligence-Friendly Classroom (IFC) framework. 47 Analytical intelligence. Analytical intelligence refers to the ability to think logically, analyze information critically, and solve problems effectively (Sternberg, 1985, 1997, 2005). It consists of skills such as analyzing, judging, evaluating, comparing, and contrasting. Sternberg (2011) argues that analytical intelligence is crucial both in school and later in life. It is primarily assessed by traditional intelligence tests and is intertwined with memory skills, which require recalling and recognizing information (Sternberg, 2005, 2011). However, memory alone is insufficient for these processes to occur, as individuals must also be able to analyze and critique information to achieve academic and real-life success (Sternberg, 2011; Wagner & Sternberg, 1984). Creative intelligence. Creative intelligence refers to the ability to generate new ideas, think outside of the box, and come up with innovative solutions (Sternberg, 1985, 1999, 2005). The creative type of intelligence is made up of abilities like creating, inventing, discovering, imagining, or supposing. The successful intelligence theory states that assessing how well an individual can cope with relative novelty is particularly good indicators of creative intelligence (Sternberg, 2005, 2011). Creative intelligence allows individuals to think differently and approach problems in innovative ways, which is essential for adapting to new and changing environments (Kaufman & Singer, 2004; Sternberg, 2015). According to Sternberg (1997), creative intelligence can sometimes conflict with analytical intelligence, especially in situations that require strict adherence to rules or established procedures. This highlights the importance of balancing all three aspects of intelligence for optimal performance (Sternberg, 1997, 2005; Sternberg & Grigorenko, 2004). Practical intelligence. Practical intelligence is the ability to apply one's skills to difficulties that arise in daily life, such as at work or at home (Sternberg, 1985, 1997, 2005). In 48 other words, practical intelligence is talents that allow people to apply and implement concepts in the "real" world by creating an optimal match between their skills and their external settings (Sternberg et al., 2000). It is consisting of skills used to apply, put into practice, implement, or use. (Davidson & Kemp, 2011; Sternberg, 1985, 1997, 2005). People need to employ practical intelligence to learn tacit or implicit information about other people, themselves, and situational contexts (Sternberg, 2004; Sternberg et al., 2000). Individuals strong in practical intelligence can navigate complex social and practical situations successfully (Sternberg et al., 2000). According to Sternberg (1997, 2005, 2015), successfully using three aspects of intelligence mentioned above (i.e., analytical, practical, and creative) and their combination leads to real-world success. Individuals who are strong in one of these three characteristics of intelligence are not necessarily be strong in the others, as the three are largely independent (Davidson & Kemp, 2011). Successful intelligence exists in all cultures when people leverage their strengths and compensate for their weaknesses in order to achieve their life goals (Davidson & Kemp, 2011; Sternberg, 1997). The strategy involves effectively combining the three parts of intelligence (analytical, practical, and creative) to adapt to, shape, or select their environments (Sternberg, 2011). Educational Implementations. Studies have shown that instruction incorporating analytical, creative, and practical thinking skills can enhance students' performance in both academic and real-world contexts (Gardner et al., 1994; Sternberg, 1999; Sternberg et al., 2009; Sternberg & Grigorenko, 2004; Williams & Sternberg, 1998). For instance, Grigorenko et al. (2002) found that SI-based instruction was more effective than traditional methods in improving reading achievement among students from lower socioeconomic backgrounds. Similarly, another study found that culturally-based, triarchic instruction was more effective than conventional 49 textbook-based instruction in improving students' performance in memory, analytical, creative, and practical aspects of mathematics achievement (Sternberg et al., 2006). Therefore, Sternberg (1999) suggested teachers to teach with all three types of intelligence while also recognizing students' unique areas of developing expertise, as some students have strong analytical abilities, others have strong creative abilities, and still others have strong practical abilities. Additionally, research indicates that when instructional methods are matched to students' unique intelligence profiles, as advocated by Sternberg (1998, 2011), students are better equipped to tackle real-life challenges. For example, high school students performed better when their strengths—whether analytical, creative, or practical—aligned with the teaching methods used in their classes (Sternberg, 2008). Similarly, Gubbels et al. (2014) found that an enrichment program based on Sternberg's Triarchic Theory can enhance cognitive and socioemotional development by adapting lessons to students’ needs. Such findings suggest that the SI framework not only enhances academic performance but also prepares students for practical, real-world problem-solving. Furthermore, SI-based assessments, which engage all three intelligence types, offer a more holistic approach to evaluating students’ ability to apply their knowledge in real-life scenarios, thereby improving their readiness for future challenges (Sternberg, 1998, 2011). 2.4.4. Implicit Theories of Intelligence (Belief-Based/Motivational Aspect) The Implicit Theories of Intelligence (ITI), proposed by Carol Dweck, focus on individuals' implicit beliefs about intelligence. Its main argument consists of two different mindsets—entity and incremental—and describes how the two mindsets influence children's beliefs in themselves and their growth in developing intelligence (Dweck, 2000, 2013; Dweck & Bempechat, 2017). The theory originated from Dweck’s early research on the psychological aspects of motivation and achievement (Dweck, 1986), specifically how self-perceptions affected 50 students' responses to failure (Dweck & Leggett, 1988). Over time, her work evolved to explore broader applications, offering the well-known constructs of fixed (entity) and growth (incremental) mindsets (Dweck, 2000). Research (Komarraju & Nadler, 2013; Molway & Mutton, 2019; Mrazek et al., 2018) suggests that individuals with a growth mindset believe that intelligence can be developed through effort and perseverance, a view that aligns with the dynamic nature of intelligence emphasized in the Intelligence-Friendly Classroom Framework. Although Dweck does not explicitly address the different types of intelligence, her approach indirectly supports the idea that students can grow in different areas depending on their mindset and the learning opportunities available to them (Macnamara & Burgoyne, 2023). Additionally, this theory explains how an individual's implicit theories (mindsets) establish a motivational and cognitive framework that affects their learning engagement and success (Blackwell et al., 2007; Liu, 2021). In addition, implicit theories of intelligence have been associated with metacognitive awareness and engagement (Limpo & Alves, 2014). Dweck's growth mindset theory fosters metacognitive development through the encouragement of reflection on learning processes and consideration of how effort and strategy enhance performance (Dweck, 2012; Hertel & Karlen, 2020). As an example, Karlen and Compagnoni (2016) found that university student who held an incremental view of writing ability used more effective learning strategies and reported greater metacognitive knowledge than student with an entity view. Further, other studies suggest that implicit theories of intelligence are related to cognitive, metacognitive, motivational, and behavioral components of self-regulated learning (Blackwell et al., 2007; Burnette et al., 2013). Therefore, students who viewed intelligence as malleable are more likely to be metacognitive sophisticated learners, since they frequently engage in self-testing and revisiting previous course 51 material compared to those with a fixed mindset (Karlen & Compagnoni, 2016). By supporting the belief that intelligence can develop, Dweck’s approach may also encourage students to take a more active role in their learning and developing through metacognitive strategies. For example, the motivational mechanisms described in Dweck's theory are closely related to self-efficacy, which is a learner's belief in their capacity to succeed in particular tasks (Bandura, 1997). Students with an incremental mindset tend to exhibit higher levels of self-efficacy, as they attribute learning success to effort and strategy rather than fixed traits (Komarraju & Nadler, 2013; Lou & Noels, 2020). In this way, self-efficacy supports persistence, adaptive help-seeking, and mastery-oriented behaviors—all essential elements of self-regulated learning (Schunk & Zimmerman, 2007). As a result, Dweck’s emphasis on the malleability of intelligence supports the development of self-efficacy beliefs by encouraging learners to interpret their setbacks as opportunities for growth rather than signs of low ability. Even though Dweck’s implicit theory of intelligence has played an important role in educational psychology, it has been criticized for its empirical consistency and generalizability (Li & Bates, 2019; Sisk et al., 2018). Although meta-analyses indicate that some mindset interventions appear to assist certain groups, such as low socioeconomic status students or academically at-risk students, the overall effect is often weak or inconsistent (Macnamara & Burgoyne, 2023; Sisk et al., 2018). Other studies have demonstrated that students' beliefs regarding mindsets have no statistically significant relationships with their academic progression, which suggest that such beliefs might not be a reliable predictor of persistence and achievement (Li & Bates, 2019). More recently, Macnamara and Burgoyne (2023) concluded that many reported effects of growth mindset interventions are likely inflated due to methodological weaknesses, such as poor study design and publication bias. 52 These criticisms focus mostly on the applicability of mindset theory and whether or not it has strong effects across contexts. However, it is essential to note that Dweck's perspective does not suggest mindsets alone drive achievement (Dweck, 2000, 2012). Rather, the theory highlights how beliefs about intelligence shape students' motivation and engagement, which can influence learning outcomes under certain conditions (Dweck, 2012; Yeager & Dweck, 2012). Dweck and colleagues have emphasized that mindset interventions should be context-sensitive and carefully implemented to be effective (Dweck, 2000; Yeager et al., 2016; Yeager & Walton, 2011). This alternative position sees mindset theory not as a universal solution, but as a potentially powerful tool when strategically used in supportive educational contexts. While there are different perspectives on the effectiveness of the theory (Burgoyne et al., 2018, 2020; Macnamara & Burgoyne, 2023; Sisk et al., 2018), recent research has shown the theory can be used in a variety of education contexts and has significance in developing lifelong learning (Dweck, 2012; Komarraju & Nadler, 2013; Liu, 2021; Lou & Noels, 2020; Mofield & Parker Peters, 2019). Additionally, Dweck's argument for implicit theory in the malleability of intelligence (Yeager & Dweck, 2012) makes it an important addition to the Intelligence-Friendly Classroom Framework by addressing the motivational aspect of intelligence. Entity mindset. Individuals with an entity (fixed) mindset, or entity theorists, believe that intelligence is unchangeable and stable (Dweck & Bempechat, 2017). These individuals frequently evaluate their fixed level of intellect based on performance feedback. They often make broad generalizations about their incapacity, give up too soon, and become demoralized in response to unfavorable performance feedback (Blackwell et al., 2007; Dweck & Bempechat, 2017). Entity theorists conclude that they are intelligent if they perform well on academic tasks and believe they lack intelligence if they perform poorly (Dweck, 2013). According to Dweck 53 and Bempechat (1983), this belief canalizes children towards competence judgments—such as trying to look smarter—instead of focusing on competence building. Consequently, entity mindset is linked to performance goals, motivating individuals to act in ways that make them appear competent and avoid criticism, aligning with the aim of "looking smart" (Dweck, 2000; Elliott & Dweck, 1983). Incremental mindset. Individuals with an incremental mindset, or incremental theorists, view intelligence as malleable and improvable through effort (Dweck & Bempechat, 2017). The primary goal for incremental theorists is to acquire mastery through learning. They often utilize performance results as feedback to assess their commitment to the task and their learning strategies (Liu, 2021). Unlike entity theorists, incremental theorists are driven by mastery goals, which motivate them to work and engage in activities to develop new skill sets and increase their intelligence, in the direction of "becoming smarter" (Dweck, 2000; Elliott & Dweck, 1983). This belief in the malleability of intelligence encourages incremental theorists to embrace challenges, persist in the face of setbacks, and view effort as a crucial component of learning and growth (Dweck, 1986; Lou & Noels, 2020). Educational Implementations. According to Dweck (2000), ITI theory has the potential to help children come up with more possibilities, make decisions based on their values or interests rather than their fear of failing, and work harder to achieve their goals (Dweck & Elliott, 1983). This theory has been the foundation for various educational interventions aimed at fostering a growth mindset, with research showing that it can positively influence academic performance, motivation, and resilience in students across different subjects and age groups. Several studies have explored the effectiveness of mindset interventions in educational settings. Zeeb et al. (2020) found that mindset-friendly instructional practices, applied in a 54 physics classroom, had a positive and lasting effect on students’ growth mindset, preventing demotivation and highlighting the importance of teacher involvement in sustaining these interventions over time. Similarly, Molway and Mutton (2019) also support that interventions related to growth mindset can have a lasting impact on students’ intelligence theories (i.e., mindsets). Additionally, Yeager et al. (2019) showed that a brief online growth mindset intervention, which taught that intellectual abilities can be developed, improved grades for lower-achieving students and encouraged enrollment in advanced math courses in U.S. secondary schools. This highlights the potential of growth mindset interventions to promote academic engagement, especially in subjects that students might traditionally find challenging. In the realm of language learning, Nou and Noels (2020) demonstrated that a growth mindset toward language ability positively influenced self-assessed English proficiency, reduced anxiety, and increased language use among recently arrived migrants. This result indicates that growth mindsets can support students in adapting to intercultural communication and succeed in language development. The effectiveness of growth mindset interventions is further supported by studies on praise and feedback. Mueller and Dweck (1998) found that praising students for their effort and perseverance, rather than their innate ability, encouraged a growth mindset and led students to embrace challenges. For example, providing specific, actionable feedback, such as "I can see you're working really hard on this problem. Keep trying different strategies!" (Hattie & Timperley, 2007). Furthermore, Blackwell et al. (2007) conducted a longitudinal study that showed adolescents with a growth mindset experienced significant academic improvements over two years, while those with a fixed mindset saw stagnant performance. These findings underline 55 the importance of mindset interventions in boosting motivation and reversing declines in academic performance. In conclusion, the integration of growth mindset principles in educational practices has demonstrated benefits in enhancing student motivation, academic outcomes, and resilience. By fostering effort, perseverance, and the belief in the potential for growth, educators can create supportive learning environments where students are encouraged to embrace challenges and continue striving toward their goals. This encouragement of a growth mindset may support the motivational aspect of intelligence development, which is one of the key dimensions of the Intelligence-Friendly Classroom Framework introduced in this study. 2.5. Key Principles of Four Theories in the Intelligence-Friendly Classroom Framework The assumptions and implementations of four intelligence-related theories—Fluid and Crystallized Intelligence Theory (Cattell, 1971), Multiple Intelligence Theory (Gardner, 1983), Successful Intelligence Theory (Sternberg, 1985), and The Implicit Theories of Intelligence (Dweck, 1991)—were investigated and discussed. To help students develop their strengths and talents, it is necessary to understand the key principles of these four intelligence-related theories and their strategy philosophies. Table 1 in this article illustrates the fundamental assumptions for talent development of all four intelligence theories by reviewing pertinent literature. Table 1 Principles of Four Intelligence Theories for Talent Development Intelligence Theories Principles/Fundamental Assumptions Fluid and Crystallized Intelligence Theory • Two facets of intelligence: Fluid (Gf) and Crystallized (Gc) intelligences (Cattell, 1963) • Gf and Gc show different developmental patterns with age (Rose & Fischer, 2011) • Intelligence (i.e., specifically Gc-crystallized intelligence) increase through getting more life experiences • Adults can frequently channel their knowledge and intellectual aptitude into specialised fields (Davidson & Kemp, 2011). 56 Intelligence Theories Principles/Fundamental Assumptions Multiple Intelligences Theory • The theory places stronger focus on sociocultural domains of intelligence and less on mental processes (Davidson & Kemp, 2011) • Each human can show their capacity in at least 9 relatively independent intelligences (Gardner & Hatch, 1989) • Linguistic, logical-mathematical, spatial, bodily-kinesthetic, musical, interpersonal, intrapersonal, naturalist, and existential (Armstrong, 2009) • All humans are capable to learn from world with different ways (Gardner, 1995b) Successful Intelligence Theory • Triarchic components of intelligence: analytical, creative, and practical intelligences (Sternberg, 1985) • Intelligence is the ability to adapt to, shape, and select environments (Sternberg, 1997; Willis et al., 2011) • Successful intelligence exists in all cultures when people leverage their strengths and compensate for their weaknesses in order to achieve their life goals (Sternberg, 1997; Davidson & Kemp, 2011). Implicit Theories of Intelligence • Two types of human mindsets about intelligence: entity (fixed) and incremental (growth) • Individual's implicit theories (mindsets) establish a motivational and cognitive framework that affects their learning engagement and success (Dweck, 2000) • The belief that intelligence is unchangeable (entity theory) directs children towards focusing on demonstrating their competence, such as looking smarter (Dweck & Bempechat, 1983) • The belief that intelligence is malleable directs children towards developing their competence, such as becoming smarter (Dweck & Bempechat, 1983) 2.6. Teacher Education in British Columbia The standards of teacher training in British Columbia (BC) are set by the BC Teachers' Council (BCTC) (British Columbia Ministry of Education, 2022). To teach in the public and private school systems in BC, teacher candidates must have completed a 4-year post-secondary degree and at least a year of teacher education training, including a 10-week practicum in K-12 school settings. Further, teacher education programs must meet the Professional and Certification Standards outlined in the Teachers Act (British Columbia Ministry of Education, 2022). These standards require a minimum of 60 credits covering various areas including human development and learning, teaching theories and practices, effective teaching methods, diversity studies (such as gender equity and inclusion), anti-racist education, indigenous pedagogies, special education, 57 and ethics in teaching. Even though it is specified that pre-service teachers must complete three credits in special education studies or the equivalent, there may be some variations in the program's structure depending on the teacher education program (Gacoin, 2020; George et al., 2020; Milford et al., 2022). Recent educational reforms, such as British Columbia’s re-designed curriculum (British Columbia Ministry of Education, 2015) and the development of a new Individualized Education Plan (IEP) (British Columbia Ministry of Education, 2016), emphasise personalised learning and support the competencies and skills that students need. They also speak of core competencies, concept-based and competency-driven learning approaches, flexibility, diversity, and inclusiveness. As the reform stated (2015), "...not all students learn successfully at the same rate, in the same learning environment, and in the same ways. It involves the provision of high-quality and engaging learning opportunities that meet the diverse needs of all students." This demonstrates that the reform aims to create an open system that values, appreciates, and promotes the growth of multiple forms of intelligences, strengths, and talents (Lo et al., 2019). In addition, the policy of "Diversity in BC Schools: A Framework" (2017) highlights an approach that every child can achieve their own potential, by addressing needs related to their intelligence, talents, strengths, and interests (Lo et al., 2019). This policy reflects that every student will find themselves prepared as a critical thinker and lifelong learner (Milford et al., 2022). Having the reforms considered, however, there are still unaddressed problems regarding how educators might better prepare themselves to meet the different needs of students in today's classrooms (Florian & Camedda, 2020; Gacoin, 2020; Hill et al., 2020; McCrimmon, 2015). Hence, it may be a good idea to expose teachers to the concept of intelligence-friendly classrooms from four various aspects to nurture students’ intellectual potentials. This may help teachers turn a 58 traditional classroom into a smart context where individual differences and personal growth and actualization are emphasized (Lo et al., 2019). Despite the potential advantages of intelligence theories, there is currently a gap in teacher training programs regarding the integration of these theories into practice. Teacher candidates at the University of British Columbia (UBC), for instance, can enroll in an 11-month program that requires 60 credits of coursework. The principal researcher in the current study looked into all of the courses (e.g., EPSE 317-Development and Exceptionality in the Regular Classroom; EDST 403-Education, Knowledge, and Curriculum; EPSE 310A-Assessment and Learning in the Classroom; EDST 401- Education, School, and Social Institutions; LLED 351- Literacy Practices and Assessment: Elementary; etc.), their syllabuses, and content that UBC offers in terms of its curriculum. After reviewing each course, the principal researcher realized that only EPSE 308 (Development, Learning, and Culture in the Classroom) covered intelligence and intelligence theories. However, only one module out of 12 modules in this course is focused on examining intelligence and intelligence concepts. In contrast, Simon Fraser University (SFU) offers a 16-month teacher training program, but intelligence and its theories are neither offered as a standalone course nor integrated into any existing courses within the program. Therefore, there are gaps and unaddressed issues regarding how to recognize students' strengths and support educators in building intelligence-friendly classrooms within the BC teacher training programs. The Intelligence-Friendly Classroom Framework and its Correspondence with Educational Reforms in BC. The Intelligence-Friendly Classroom Framework is grounded in the principles of inclusivity and personalized learning, which closely align with the goals of British Columbia's educational reforms (British Columbia Ministry of Education, 2015). This framework integrates four complementary theories of intelligence—FCI, MI, SI, and ITI—to 59 create a holistic approach to nurturing students’ diverse cognitive, developmental, sociocultural, and motivational needs. The four theories collectively highlight these needs by focusing on the multifaceted and dynamic nature of intelligence, which supports the reforms' vision of meeting the unique needs of all students (British Columbia Ministry of Education, 2015). Additionally, theories in the framework advocate that intelligence is malleable and can be developed over time (Blackwell et al., 2015; Lo et al., 2019; Nickerson, 2011; Schroeders et al., 2016), aligning with the goal of fostering students as lifelong learners and critical thinkers, as stated by the reforms (British Columbia Ministry of Education, 2015). For example, encouraging a growth mindset in classrooms can help students view challenges as opportunities for growth (Mrazek et al., 2018), further supporting the reforms' vision of preparing students for success in an increasingly complex world. Therefore, incorporating the Intelligence-Friendly Classroom Framework into teacher training programs could empower educators to support students by welcoming multiple intelligences and nurturing diverse intelligences. 2.7. Research Questions of the Study The present study investigated educators' self-perceived knowledge, skills, attitudes, and efficacy towards constructing intelligence-friendly classrooms by looking at the applications of FCI, MI, SI, and ITI theories, which address the developmental, sociocultural, cognitive, and motivational aspects of intelligence, respectively. Specifically, the following four main research questions drove this study: 1. What is the extent of educators' self-perceived knowledge regarding FCI, MI, SI, and ITI theories? 2. What is the extent of educators' self-perceived skills in applying FCI, MI, SI, and ITI theories? 60 3. What attitudes do educators hold towards creating an intelligence-friendly classroom? 4. What level of self-efficacy do educators demonstrate in constructing an intelligence-friendly classroom? This study also investigated demographic factors (age, gender) and other variables (first language, ethnicity, education level, years of teaching experience, and experience related to FCI, MI, SI, and ITI theories during their bachelor’s degree and career) that may impact educators' knowledge, skills, attitudes, and self-efficacy. Given that the purpose of this study is exploratory, we aimed with the demographic variables to identify potential patterns that could guide future research and inform practical applications in educational settings. 61 Chapter 3: Methodology The goal of this study is to examine educators' self-perceived knowledge, skills, attitudes, and efficacy towards constructing an intelligence-friendly classroom via the application of FCI, MI, SI, and ITI theories by using a survey. These theories address the developmental, sociocultural, cognitive, and motivational aspects of intelligence. Quantitative methods were adopted in the current study to address the research questions (Creswell, 2012; Creswell & Creswell, 2017; Fraenkel et al., 2012). Collecting quantitative data revealed overall patterns in educators' perceived knowledge, skills, attitudes, and self-efficacy with regard to the application of the aforementioned intelligence-related theories. This chapter outlines the research design, participants, data collection procedures, instrument development and validation, pilot study, and data analysis methods. 3.1. Cross-Sectional Survey Design This study used a cross-sectional survey design (Creswell, 2012; Fraenkel et al., 2012; Mills & Gay, 2019) for data collection. Generally speaking, survey research is a systematic way to collect data from populations in order to understand the characteristics of a population such as their attitudes, traits, beliefs, or past experiences (Brewer, 2009; Leedy & Ormrod, 2005; Price et al., 2015). Within survey methods, a cross-sectional survey method speaks to a process of gathering data from a sample of a population at a single point in time (Creswell & Creswell, 2017; Fraenkel et al., 2012). The cross-sectional survey method is descriptive in nature and usually conducted in a naturalistic context wherein researchers do not have the means to manipulate variables (Burns & Grove, 2005). Additionally, research participants are typically more willing to collaborate in a one-time survey research study rather than in a multi-time survey 62 research study (Brewer, 2009; Lavrakas, 2008). This method results in using less economic resources and time (Creswell & Creswell, 2017; Mills & Gay, 2019). 3.2. Participants The participants for this study included 143 educators from British Columbia, Alberta, and Ontario. Criterion for participating in this study included: being an educator, broadly defined as in-service teachers, pre-service teachers, or a graduate student in an Education-related field since the study requires familiarity in classroom teaching and teacher training. The recruitment methods involved reaching out to the above-mentioned groups through multiple channels. Since graduate students and pre-service teachers are affiliated with the university, we first contacted six departments within the UBC Faculty of Education. To reach in-service teachers, we then attempted to reach out to 17 teacher federations across Canada, but the efforts did not receive positive responses. Later, we successfully established connections with the BC Social and Emotional Learning organization, and various schools through personal networks where teacher participants were recruited. Detailed information is provided under the next heading of Data Collection Procedures. Peng et al. (2012) suggested the sample size should be large enough to identify meaningful differences while avoiding the production of statistically significant but practically trivial results. Therefore, the appropriateness of sample size for this study was determined by a priori power analysis using G*Power, which aimed to ensure the final sample's adequacy for achieving a representative composition (Erdfelder et al., 1996). By setting the Type-1 error α = .05, a power level of .80 (i.e., Type-2 error of 0.20), and an effect size γ of 0.49 calculated from the pilot study, the analysis indicated that a minimum sample size of 116 was required (see Figure 3). Since the effect sizes from the pilot study varied (across comparisons of educators' 63 knowledge, skills, attitudes, and self-efficacy by demographic variables), a moderate effect size estimate (γ = 0.49), near the median of observed effects, was selected to estimate sample size for the main study (Cohen, 1988; Field, 2009). Consequently, the final sample size (n = 143) exceeded this minimum requirement. Figure 3 Necessary Sample Size For the Statistical Power in the Study Table 2 shows the frequency of personal demographic characteristics of the participants, including gender, birth year, first language, state of residence, and ethnicity. It reveals that the majority of the sample identified as female (71.3%, n = 102), with 25.2% identifying as male and 2.1% as non-binary. Most participants were between the age of 24 and 30 years with generation Z being the largest sub-population (45.5%, n = 65), and resided in British Columbia (75.5%, n = 108). English was reported as the first language by 73.4% (n = 105) of participants. Meanwhile European background was identified as the most common ethnicity (43.4%, n = 62). 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95050100150200Total sample size = 0.49Effect size dt tests - Means: Wilcoxon-Mann-Whitney test (two groups)Tail(s) = One. Parent distribut ion = Normal. Allocation rat io N2/N1 = 1.4. α err prob = 0.05. Effect size d = 0.49Power (1-β err prob) 64 Table 2 Frequencies of Personal Demographic Characteristics of the Participants Demographic Variables N % Gender Female 102 71.3 Male 36 25.2 Non-Binary Person 3 2.1 Prefer not to answer 2 1.4 Birth Year 1961-1981 (Generation X) 21 14.7 1982-1994 (Generation Y) 56 39.2 1995-2012 (Generation Z) 65 45.5 First Language English 105 73.4 Other 38 26.6 State of Residence Alberta 22 15.4 British Columbia 108 75.5 Manitoba 1 0.7 Ontario 12 8.4 Ethnicity Indigenous origins (for example, First Nations, Inuit, Métis) 1 0.7 African 3 2.1 Asian (China, Mongolia, North Korea, South Korea, Japan, Hong Kong, Taiwan, etc.) 25 17.5 South Asian (Afghanistan, Bangladesh, India, Nepal, Pakistan, Sri Lanka, etc.) 12 8.4 South East Asian (Brunei, Burma (Myanmar), Indonesia, Malaysia, Singapore, Thailand, Vietnam, etc.) 3 2.1 European 62 43.4 Hispanic or Latina/Latino (Latin, Central and South American) 4 2.8 Middle Eastern (Arabic, Iranian, Kuwaiti, Persian, Israeli, etc.) 7 4.9 Other (tell us) 10 7 I don’t know 3 2.1 I choose not to disclose 9 6.3 65 Table 3 shows the educational backgrounds of the participants, including the highest level of education, graduate students’ status, status of being teacher or teacher candidate, years of teaching experience, and areas of undergrad major. Overall, the data reveal that nearly all (97%) of the participants have received a bachelor degree, and nearly half (44.8%) of the participants have received a graduate degree. Additionally, 49.3% (n = 70) of participants identified as in-service teachers, with 40.6% (n = 58) being pre-service teachers. Examining years of teaching experience, a considerable number of participants reported 0-2 years (46.2%, n = 66), followed by 3-5 years (19.6%, n = 28), 6-10 years (18.2%, n = 26), and over 10 years (16.1%, n = 23). The distribution of undergraduate majors encompassed Arts and Humanities (42.7%, n = 61), Social Sciences (35.7%, n = 51), and Science and Technology (21%, n = 30). Table 3 Frequencies of Education-related Demographic Characteristics of the Participants Demographic Variables N % Highest Level of Education PhD/EdD 8 5.6 Masters 56 39.2 Bachelors 74 51.8 Associate Degree and Other 5 3.4 Graduate Students’ (GS) Status GS with Canadian Teaching Experience 52 36.4 GS with International Teaching Experience 27 18.9 GS without Teaching Experience 5 3.5 Not GS 58 40.6 Status of Being Teacher or Teacher Candidate Teacher / In-service Teacher 71 49.7 Teacher Candidate / Preservice Teacher 58 40.6 Not Applicable (e.g., international graduate students) 14 9.7 66 Demographic Variables N % Years of Teaching Experience 0-2 years 66 46.2 3-5 years 28 19.6 6-10 years 26 18.2 +10 years 23 16.1 Undergrad Major Arts and Humanities 61 42.7 Social Sciences 51 35.7 Science and Technology 30 21 The demographic characteristics related to having course in their bachelor’s programs or professional sessions in their teaching careers regarding multidimensional intelligence theories are reported in Table 4. A significant portion of participants (35.6%, n = 51) reported never having encountered courses on multidimensional intelligence theories during their bachelor’s program. In contrast, 21.7% (n = 31) reported having exposure to one course, 23.8% (n = 34) reported two courses, and 11.2% (n = 16) reported three or more courses on multidimensional intelligence theories. Regarding professional development, a considerable 45.5% (n = 65) reported never attending professional sessions on multidimensional intelligence theories during their teaching career. On the other hand, 14.6% (n = 21) had attended one session, 16.8% (n = 24) had attended two sessions, and another 16.8% (n = 24) had participated in three or more sessions on the multidimensional intelligence theories. 67 Table 4 Demographic Characteristics Related to Multi-Dimensional Intelligence Theories Demographic Variables N % Number of Courses on Multi-Dimensional Intelligence Theories in Your Bachelor's Program Never 51 35.6 1 course 31 21.7 2 courses 34 23.8 3 or more courses 16 11.2 Not Applicable 11 7.7 Professional Sessions Attended on Multi-Dimensional Intelligence Theories in Your Teaching Career Never 65 45.5 1 Session 21 14.6 2 Sessions 24 16.8 3 or more Sessions 24 16.8 Not Applicable 9 6.3 3.3. Procedures After receiving an approval from the UBC Research Ethics Board (BREB) to conduct the study, the researcher contacted units and departments within the Faculty of Education at UBC. These included the Department of Educational and Counselling Psychology, and Special Education (ECPS), the Department of Educational Studies (EDST), the Department of Curriculum and Pedagogy (EDCP), the Department of Language and Literacy Education (LLED), the School of Kinesiology, and the Teacher Education Office (TEO). Additionally, the researcher reached out to 17 teacher federations across Canada, though only three responded, stating that they preferred not to burden their members with external research due to ongoing internal projects. The researcher also contacted the BC Social and Emotional Learning organization through a peer connection, which agreed to distribute the recruitment flyer. Furthermore, the researcher utilized personal networks to distribute the survey among educators, 68 resulting in its circulation in one school in Ontario and one school in Alberta. At the onset of the research study, initial contact email (see Appendix F) and participant recruitment flyer (see Appendix D) was circulated among those groups. After one week, a reminder was sent those groups via the same way (email). Participants who expressed interest in the study were directed to the Qualtrics survey system via the provided link in the email. Upon arrival, they encountered the consent form (See Appendix E), which outlined details about the study and its procedures. Participants who agreed to the terms proceeded to the main survey on the Qualtrics survey system (See Appendix E). Consequently, the recruitment resulted in 143 educators, including teachers, teacher candidates, or graduate students. The participation in the study was entirely voluntary and participants had the opportunity to enter a draw for a chance to win one of five $100 Amazon gift cards. 3.4. Instrument The researcher developed a survey questionnaire that pertains to assessing educators' competence in creating intelligence-friendly classrooms using FCI, MI, SI, and ITI theories. Major components of the survey include educators' (1) knowledge, (2) skills, (3) attitudes, and (4) self-efficacy towards constructing intelligence-friendly classrooms. Further, some variables that could potentially influence their competence were included as demographic items in the survey. These demographic variables include: gender, ethnicity, education level, years of teaching experience, undergrad major, training on multidimensional intelligence theories. The survey instrument was developed following a thorough examination of the relevant literature, supervisory committee and peer feedback, and a pilot study. In this section, processes regarding its development, pilot study, and the steps taken to ensure the survey’s clarity and quality are shared. 69 3.4.1. Development As appropriate to the literature (Creswell, 2012; Fraenkel et al., 2012; Tsang et al., 2017; Yaghoubi, 2017), the following steps (see Figure 4) were taken for developing the instrument: literature review, identifying the domains of the instrument, determining of the format of the instrument, preparing questionnaire items, generating an item pool, gaining feedback from experts, conducting a pilot study, and finalizing the instrument. Figure 4 Instrument Development Process 70 First Iteration. As Rea and Parker (2014) suggested, designing a set of unbiased, well-structured questionnaire items is critical to the survey developments. To achieve this, it is important to ensure that items are clear, concise, and free from any leading or loaded language that could bias responses (Price et al., 2015; Rea & Parker, 2014). Each item should be relevant to the research objectives and designed to capture the intended topics accurately (DeVellis & Thorpe, 2021). In addition, it is critical to conduct a thorough literature review in order to generate pertinent questions for a survey (Mills & Gay, 2019). As Lawshe (1975) stated that the content domains of an instrument must be determined through a comprehensive literature review before moving on to the next steps such as the content relevance and clarity. In designing our survey, a web-based questionnaire structure was chosen for its accessibility. With regard to the format of the survey, closed-ended question format was chosen to increase response rates. Items were grouped by categories predetermined, namely knowledge, skills, attitudes, and self-efficacy. A 5-point Likert scale (1-Strongly Disagree, 2-Disagree, 3- Neither Agree or Disagree, 4-Agree, and 5-Strongly Agree) was chosen so that the questionnaire would be simple to interpret, internally consistent, and suited for analysis (Price et al., 2015; Rea & Parker, 2014). Demographic questions were presented at the end of the instrument to prevent participants tired or bored in responding to the questionnaire (Price et al., 2015). The entire questionnaire was designed with an anticipation that a participant could complete the survey within 15 to 20 minutes. Questionnaire items were developed by using the BRUSO model (Peterson, 2000) that stands for "brief," "relevant," "unambiguous," "specific," and "objective." According to the BRUSO Model, effective questionnaire items are (1) brief rather than long to ensure easier comprehension and speedier completion; (2) relevant to the aim of research and research 71 question; (3) unambiguous so the item can only be interpreted in one way; (4) specific so that the participants can make clearer responses; and (5) objective in the sense that the wording does not influence participants' opinions in any manner (Price et al., 2015). A pool of items was generated after these procedures and careful iterations. Thus, a first draft of the survey was created after a thorough literature review and was guided by the principles enlisted in the BRUSO model to ensure clarity and effectiveness. A measurement specialist was invited to give feedback on the structure and appropriateness of the question items. Additionally, a teacher, who is also native English speaker, was consulted to ensure that the language could be easily understood by educators and teachers. Lastly, the draft was also consulted with the research committee, a team consisting of experts in gifted education, inclusive education, and psychometrics and survey design, to ensure the survey’s content was clear, relevant, and appropriate (Fraenkel et al., 2012). Second Iteration. A panel of experts was selected to review the instrument to enhance the clarity and quality of the survey (Creswell & Creswell, 2017; Zamanzadeh et al., 2015). The selection criteria for the experts were as follows: (1) the expert should be in the field of gifted and talented education, or (2) the experts’ expertise should reflect an expertise in intelligence theories. By using these criteria, the researcher reached out to five scholars through a convenience sampling. Consequently, two professors and one PhD candidate accepted the invitation to provide expert feedback on the survey. Among these experts, one professor and the PhD candidate are specialized in the field of Gifted and Talented Education, while the other professor holds a keen research interest in intelligence theories. Using an expert review form structured by following Lawshe's (1975) approach (see Appendix A), the experts classified each item as "essential," "useful, but not essential," or "not necessary" for the instrument's content domain. Additionally, space was provided for each question, allowing experts to provide detailed 72 feedback on an item-specific basis. Consequently, the panel of experts provided revisions to improve language use for greater effectiveness. They generally agreed that the survey items were well-prepared for the study's aim. After incorporating the experts' feedback, the second draft of the survey was created. Third Iteration. The final iteration of the survey design involved a pilot study. The purpose of the pilot study was to ensure the instrument worked as anticipated and to identify and resolve potential issues (Johanson & Brooks, 2010; Price et al., 2015; Thabane et al., 2010). After revising the items based on feedback from field experts, a draft survey was sent to 10 people for a pilot study. Subsequent to the pilot study, the last adjustments were made based on participant feedback. Suggestions included numbering questions for easier navigation, indenting items with sub-options (Q2, Q5, and Q8 in the skills section) for visual distinction, and refining wording for clarity and grammatical accuracy. The ultimate survey included 42 items and 10 demographic questions (see Appendix E). Structurally, the questionnaire encompasses four primary dimensions that educators’ knowledge, skills, attitudes, and self-efficacy with regard to implementing FCI, MI, SI, and ITI theories in classroom. Within the knowledge and skills dimensions, there are four sub-dimensions, each representing a theory of intelligence. The knowledge section uses the template "I am aware that...," while the skills section uses the template "I am able to..." In the attitude section, the template is "I believe that..." The self-efficacy section does not use a specific template since self-efficacy can be expressed through a variety of different actions. For example, in the knowledge section, one item states, "I am aware that there are two types of intelligence (fluid and crystallized) in Cattell’s theory" (Q1), and "I am aware that everyone has their unique combination of the nine intelligences" (Q5). In the skills section, an example item is "I am able to explain Cattell's theory of fluid and crystallized 73 intelligence to students" (Q1) and "I am able to design assessments that cater to successful intelligence" (Q9). In the attitude section, items include "I believe that intelligence is a multi-dimensional construct rather than a single-dimensional construct" (Q1) and "I believe that the classroom should provide every student with opportunities to celebrate their intelligences" (Q11). Finally, in the self-efficacy section, examples include "I actively seek advice from teachers/educators who have experience in enhancing various types of intelligence in the classroom" (Q2) and "I work effectively with parents to help students develop their intelligences" (Q6). For a more detailed understanding of the survey used in this study, refer to Appendix A. The organization of these sections in the survey, along with their Cronbach’s alpha reliability coefficients, is presented in Table 5 below. Table 5 Organization of Survey Sections Dimensions Sub-dimensions Items Cronbach’s Alpha Knowledge (12 items) 1. Fluid & Crystallized Intelligence Theory Q1 – Q3 .85 2. Multiple Intelligence Theory Q4 – Q6 .72 3. Successful Intelligence Theory Q7 – Q9 .80 4. Implicit Theory of Intelligence Q10 – Q12 .79 Skills (12 items) 1. Fluid & Crystallized Intelligence Theory Q1 – Q3 .91 2. Multiple Intelligence Theory Q4 – Q6 .96 3. Successful Intelligence Theory Q7 – Q9 .94 4. Implicit Theory of Intelligence Q10 – Q12 .84 Attitudes (11 items) NA Q1 – Q11 .84 Self-Efficacy (7 items) NA Q1 – Q7 .92 74 3.4.2. Pilot Study Procedure Based on the same inclusion/exclusion standards as the primary study (Price et al., 2015; Thabane et al., 2010), 10 participants, distinct from the main study cohort, was recruited through convenient sampling. The pilot study recruitment email (See Appendix B) was distributed to these graduate students at Special Education Program (SPED) via students' email list. Additionally, every participant interested in joining the pilot study was provided with information regarding our expectations (see Appendix C). Each participant was also given a $15 Amazon gift card as compensation for their time in the pilot study. The pilot study allowed us to receive feedback on concerns related to unclear, repeated, or incomprehensible items, as well as insights into the study's duration and readiness for the main study. Pertinent modifications and minor adjustments were made based on participants' feedback. 3.4.3. Validity and Reliability Content validity. Content validity can be defined as the degree to which a measure covers the relevant constructs (Price et al., 2015; Tsang et al., 2017), or the capacity of reflecting the variables of the constructs (Zamanzadeh et al., 2015). This type of validity examines how well an instrument's items adequately represent the content domain of the theory. Consulting subject-matter experts is a required step for valid results from the instrument and to obtain content validity (Ayre & Scally, 2014). Therefore, the survey items were examined by subject-matter experts, including two professors, one PhD candidate, and the supervisory committee. The content validity of the survey was ensured by making necessary adjustments based on expert opinions. Reliability. The internal consistency of the survey was assessed using Cronbach’s alpha coefficients for each dimension and sub-dimension. These ranged from 0.72 to 0.96 across the 75 four dimensions—Knowledge, Skills, Attitudes, and Self-Efficacy —indicating acceptable to excellent reliability (see Table 5 above for details). This aligns with Peers’s (2006) recommendation that Cronbach’s alpha values of 0.70 or higher demonstrate adequate reliability for research instruments. This suggests that the items within each section consistently measure their intended constructs (Maxwell, 2017). 3.5. Data Analysis In the initial phase of the study, quantitative analysis was conducted using the Statistical Package for the Social Sciences (SPSS), version 28 (IBM Corp., 2021). The preliminary step involved organizing and transforming the raw data to facilitate subsequent analyses. For example, in the gender variable, while the majority of participants selected the male (71.3%) and female (25.2%) gender options, three people opted for the non-binary category (2.1%). Moreover, two participants chose not to disclose their gender (1.4%). Consequently, analyses were conducted by considering only those who indicated the male and female options in the gender variable. In the case of first language, English was predominant (73.4%), while smaller groups specified other languages such as French (3.8%) and Chinese (5.1%), among others. Since the numbers in other languages were insufficient for meaningful group comparisons (Fraenkel et al., 2012), the variable of the first language was dichotomized into two groups: English and other languages. For the independent variable "Highest Level of Education," participants were similarly categorized into two groups for clarity: graduate (master's or PhD) and undergraduate (bachelor's or associate degree). Finally, the two independent variables ("Number of Courses on Multi-Dimensional Intelligence Theories in Your Bachelor's Program" and "Professional Sessions Attended on the 76 Multi-Dimensional Intelligence Theories in Your Teaching") each had six options: Never (35.6% - 45.5%), 1 course/session (21.7% - 14.6%), 2 courses/sessions (23.8% - 16.8%), 3 courses/sessions (5.6% - 8.2%), 4 or more courses/sessions (5.6% - 8.6%), and not applicable (7.7% - 6.3%). Responses marked as "not applicable" for these variables were omitted from the analyses. Additionally, since the options of 3 courses/sessions and 4 or more courses/sessions represent a low number of participants (both less than 10%), they were combined into one category as “3 or more courses/sessions.” Thus, these two independent variables were organized into four groups, ranging from "never" to “3 or more courses/sessions.” After transforming the raw data, missing values were examined across both dependent variables (educators' knowledge, skills, attitudes, and self-efficacy in constructing intelligence-friendly classrooms via the application of FCI, MI, SI, and ITI theories) and independent variables (i.e., demographic variables in the survey). To address the four main research questions, descriptive analyses (i.e., M, SD, and range) were conducted to capture trends in educators' self-perceived knowledge, skills, attitudes, and efficacy. Descriptive statistics were also applied to the subdimensions of theory-based dimensions (knowledge and skills) to assess specific theories (see Table 5 for survey structure). For example, both general knowledge (General Knowledge Level – GKL) and theory-based knowledge were examined to identify overall and theory-specific trends. Subsequently, the Shapiro-Wilk test (Hanusz & Tarasińska, 2015; Wijekularathna et al., 2022; Yap & Sim, 2011) was employed to assess the assumption of normality, thereby evaluating the validity of this assumption (Field, 2009). Based on the results of the Shapiro-Wilk test, nonparametric analyses were selected to explore whether demographic factors contributed to differences in educators' responses to the main research questions and the subdimensions of 77 knowledge and skills. The Mann-Whitney U test was utilized for variables with two subgroups (i.e., gender, first language, highest level of education, and status of being teacher or teacher candidate), and the Kruskal-Wallis H test was employed for variables with more than two subgroups (i.e., years of teaching experience, undergrad major, number of courses on multi-dimensional intelligence theories in the bachelor's program, and professional sessions attended on the multi-dimensional intelligence theories in their career). Following this, the Dunn-Bonferroni post-hoc test was used to further explore and compare subgroups within the Kruskal-Wallis H tests. Additionally, Spearman rank correlation analyses were conducted to examine potential relationships between educators' professional competencies (knowledge, skills, attitudes, and self-efficacy) and two key variables regarding multi-dimensional intelligence theories exposure. 78 Chapter 4: Results This chapter presents the results of the study. The results are organized under four main headings, each corresponding to a research question: (1) educators' self-perceived knowledge of FCI, MI, SI, and ITI theories, (2) educators' self-perceived skills in applying FCI, MI, SI, and ITI theories, (3) educators' attitudes towards constructing an intelligence-friendly classroom, and (4) educators' self-efficacy in constructing an intelligence-friendly classroom. 4.1. Data Preparation and Preliminary Analyses With an exploratory purpose, this study employs a survey research approach aimed at understanding educators' self-perceived knowledge, skills, attitudes, and efficacy in constructing intelligence-friendly classrooms. Analyses were conducted following the statistical procedures outlined in Section 3.5 to explore existing data patterns that may guide future research and practical applications, with no prior assumptions about specific outcomes. While filling the survey, each participant was required to answer all questions (except demographic questions) within the Qualtrics system. Hence, no missing values were identified in the main survey section of the study. However, minimal missing data (i.e., one missing response) were observed in three demographic questions: birth year, graduate student status, and undergraduate major. Given the minimal amount of missing data, the analyses proceeded with the available data without excluding these cases. This approach was chosen as the negligible percentage of missing data is unlikely to impact the overall results (Garson, 2015; Jakobsen et al., 2017). Following, descriptive statistics, including mean (M), median (Mdn), and standard deviation (SD), were presented under relevant headings below. The reason why we opted to present both the mean and median values is that the data in the study exhibited varying degrees of skewness— 79 from a relatively low skewness of -0.436 (e.g., for the knowledge of the SI theory) to a high skewness of -1.622 (e.g., for the knowledge of the ITI theory). This dual presentation can help ensure a more useful and informative interpretation of central tendency of the data, particularly in cases of non-normality (Norman, 2010). Regarding the calculation of mean values, we employed a weighted mean that was preplanned due to the structure of our survey. For example, each theory in the skills section consists of varying numbers of questions (e.g., some subdimensions have 9 questions, while others have 4 or 6). The use of weighted means was necessary to ensure that each subdimension was fairly represented in the overall analysis (i.e., General Skills Level – GSL), preventing any single subdimension with more questions from disproportionately influencing the results (Rea & Parker, 2014). To determine whether to conduct parametric or non-parametric analyses, the normality assumption was assessed using the Shapiro-Wilk test, recognized as a powerful normality test (Hanusz & Tarasińska, 2015). The results indicated a violation of the normality assumption (refer to Table 6), as evidenced by a significant p-value (p < .05). Therefore, non-parametric tests were chosen for subsequent analyses, including (1) the Mann-Whitney U test for comparing differences between two independent groups, and (2) the Kruskal-Wallis H test for comparisons of three or more independent groups. If a Kruskal-Wallis H test yielded a significant result, a Dunn test with Bonferroni adjustment was then conducted as an additional analysis to identify the specific groups contributing to the difference. More details regarding comparisons based on demographic variables are shared in the relevant tables in this chapter: Table 8 and Table 10 for knowledge and its subdimensions; Table 15 and Table 17 for skills and its subdimensions; Table 23 and Table 24 for attitudes; and finally, Table 27 and Table 28 for self-efficacy. 80 To determine the magnitude of differences, we calculated effect sizes in this study using the rank-biserial correlation (r) for Mann-Whitney U tests and the eta-squared (η2) for Kruskal-Wallis H tests, along with their corresponding 95% confidence intervals (CIs) (Kerby, 2014). The interpretation of r values was based on benchmarks at 0.1, 0.3, and 0.5, representing small, medium, and large effects, respectively (Cohen, 1988; Field, 2009). Likewise, the interpretation of eta-squared (η2) was based on benchmarks where η2=0.01, η2=0.06, and η2=0.14 indicate small, medium, and large effects, respectively (Cohen, 1988). Table 6 Shapiro-Wilk Test Results for the Normality of Dependent Variables' Distribution Dependant Variables Shapiro-Wilk Statistic df p Knowledge .905 143 < .001 Fluid & Crystallized Intelligence (FCI) Theory .938 143 < .001 Multiple Intelligence (MI) Theory .904 143 < .001 Successful Intelligence (SI) Theory .968 143 < .01 Implicit Theory of Intelligence (ITI) .846 143 < .001 Skills .922 143 < .001 Fluid & Crystallized Intelligence (FCI) Theory .933 143 < .001 Multiple Intelligence (MI) Theory .937 143 < .001 Successful Intelligence (SI) Theory .927 143 < .001 Implicit Theory of Intelligence (ITI) .907 143 < .001 Attitudes .892 143 < .001 Self-Efficacy .925 143 < .001 Additionally, Spearman rank correlation analyses were conducted to examine potential relationships between the educators' professional competencies (knowledge, skills, attitudes, and self-efficacy) and the key variables regarding multidimensional intelligence theories exposure: (1) the number of bachelor's degree courses on multidimensional intelligence theories and (2) the number of professional development sessions attended on multidimensional intelligence theories. Consistent with the exploratory purpose of this study, only analyses with statistically significant results are reported in the tables to provide a focused overview of key outcomes related to 81 educators' knowledge, skills, attitudes, and self-efficacy in applying FCI, MI, SI, and ITI theories. 4.2. Educators' Knowledge of FCI, MI, SI, and ITI Theories We initiated our inquiry with the fundamental question: "What is the extent of educators' self-perceived knowledge regarding FCI, MI, SI, and ITI theories?" To address this inquiry, the knowledge dimension of the survey examines these four theories as part of the intelligence-friendly classroom framework: (1) Fluid and Crystallized Intelligence (FCI) Theory, (2) Multiple Intelligence (MI) Theory, (3) Successful Intelligence (SI) Theory, and (4) Implicit Theory of Intelligence (ITI). This approach provides insight into the layers of educators' understanding and awareness of these theories. Descriptive statistics (M, SD, etc.) for these theories are presented in Table 7. The use of both mean and median values accounts for the non-normal distribution of the data, offering a more comprehensive view of central tendency (Lewis, 1993; Norman, 2010). We then assessed whether perceived knowledge levels of each theory varied based on demographic variables. Table 7 Descriptive Statistics for Participants’ Perceived Knowledge of Theories within the Intelligence-Friendly Classroom Framework Theories within the Intelligence-Friendly Classroom Framework Descriptive statistics M Mdn SD Range Potential Observed Knowledge (Total) 3.67 3.83 .601 1.00 – 5.00 1.00 – 4.58 Fluid & Crystallized Intelligence (FCI) Theory 3.17 3.33 .923 1.00 – 5.00 1.00 – 5.00 Multiple Intelligence (MI) Theory 3.96 4.00 .809 1.00 – 5.00 1.00 – 5.00 Successful Intelligence (SI) Theory 3.31 3.33 .820 1.00 – 5.00 1.00 – 5.00 Implicit Theory of Intelligence (ITI) 4.23 4.33 .745 1.00 – 5.00 1.00 – 5.00 82 Table 7 presents descriptive statistics for the knowledge dimension and its sub-dimensions related to FCI, MI, SI, and ITI Theories. The overall knowledge level, represented by the mean (M) for the total knowledge score, is 3.67 with a median (Mdn) of 3.83 and a low standard deviation (SD) of .601. This indicates a moderate to high understanding among participants with relatively low variability. The total knowledge score reflects participants' perceived knowledge of intelligence-related theories in general. It was calculated by taking the mean of all items in the knowledge section because all theories have the same number of items (3 items per theory). If we examine specifically the perceived knowledge across specific intelligence theories, participants exhibit a moderate understanding of FCI Theory (M = 3.17, Mdn = 3.33, SD = .923) with notable variability. In contrast, MI Theory shows a relatively high understanding (M = 3.96, Mdn = 4.00) with moderate variability (SD = .809). Participants demonstrate a moderate understanding of SI Theory (M = 3.31, Mdn = 3.33) with a moderate level of variability (SD = .820). Notably, ITI has the highest mean score (M = 4.23, Mdn = 4.33) with moderate variability (SD = .745). These findings collectively indicate a trend in which ITI appears as the theory with the highest level of self-reported knowledge among respondents. Subsequently, the second theory with which participants are most aware is the Theory of Multiple Intelligences (MI). It was then examined whether the educators' overall knowledge level, and specifically their understanding of the theories, varied based on their demographic characteristics. A Mann-Whitney U test was performed to evaluate whether participants' perceived knowledge level about the related theories differed by their (1) gender, (2) first language, (3) highest level of education, and (4) status of being teacher or teacher candidate (see Table 8). 83 Table 8 Comparison of Participants' Perceived Knowledge Levels Across Demographic Variables using Mann-Whitney U Test Theories within the Intelligence-Friendly Classroom Framework Demographic variables Gender First Language Highest Level of Education Teacher Status Knowledge (Total) .695 .440 .260 .048* Fluid & Crystallized Intelligence (FCI) Theory .038* .402 .964 .940 Multiple Intelligence (MI) Theory .874 .002* .001* .020* Successful Intelligence (SI) Theory .170 .350 1.00 .875 Implicit Theory of Intelligence (ITI) .002* .155 .319 .001* Note. Reported values are p-values from the Mann-Whitney U Test. * p < .05. In terms of gender, significant differences were observed in the perceived knowledge levels of the FCI Theory and ITI. Specifically, males have a higher level of perceived knowledge on the FCI Theory than females (z = -2.080, p = .038) while females exhibited a notably higher level of perceived knowledge about ITI compared to males (z = -3.076, p = .002). Regarding the first language variable, the results reveal a significant difference only for the MI Theory. Participants whose first language is not English exhibited a higher level of perceived knowledge about the MI Theory compared to those whose first language is English (z = -3.033, p = .002). Concerning the highest level of education variable, the results indicate a significant difference just for the MI Theory once more. Graduate students exhibited a higher level of perceived knowledge about the MI Theory compared to participants who are not graduate students (z = -3.316, p < .001). Finally, regarding the variable of Status of Being Teacher or Teacher Candidate, in-service teachers exhibited a higher level of general perceived knowledge about the four theories together (z = -1.981, p = .048), specifically the MI Theory (z = -2.325, p = .020), and ITI (z = -3.656, p < .001), compared to pre-service teachers (teacher candidates). The results of Mann Whitney U test are given in Table 9 below. 84 Table 9 The Results of Mann-Whitney U Tests Dimensions Demographic Variables & Groups N Mean U z p Effect size (r) 95% CI Gender FCI Theory (1) Female 101 64.83 1397.00 -2.080 .038 .20 [.04, .36] (2) Male 36 80.69 ITI (1) Female 101 75.12 1200.00 -3.076 .002 .31 [.16, .46] (2) Male 36 51.83 First Language MI Theory (1) English 105 65.35 1297.00 -3.033 .002 .26 [.11, .42] (2) Other 37 88.95 Highest Level of Education MI Theory (1) Graduate 64 84.00 1696.00 -3.316 .001 .27 [.12, .42] (2) Undergraduate 78 61.24 Status of Being Teacher or Teacher Candidate GKL (1) Teacher / In-service Teacher 70 70.40 1617.00 -1.981 .048 .18 [.01, .35] (2) Teacher Candidate / Preservice Teacher 58 57.38 MI Theory (1) Teacher / In-service Teacher 70 71.36 1549.50 -2.325 .020 .21 [.04, .38] (2) Teacher Candidate / Preservice Teacher 58 56.22 ITI (1) Teacher / In-service Teacher 70 75.21 1280.00 -3.656 .001 .31 [.15, .47] (2) Teacher Candidate / Preservice Teacher 58 51.57 Note. Abbreviations: CI, Confidence Interval; FCI Theory, Fluid and Crystallized Intelligence Theory; ITI, Implicit Theory of Intelligence; MI Theory, Multiple Intelligence Theory; and GKL, General Knowledge Level. Kruskal-Wallis H tests were conducted to explore potential differences in participants' perceived knowledge levels regarding the four theories across various demographic variables, including (1) years of teaching experience, (2) undergraduate major, (3) number of courses on the multidimensional intelligence theories in their bachelor's program, and (4) number of 85 professional sessions attended on the multidimensional intelligence theories in their teaching career (see Table 10). Table 10 Comparison of Participants' Perceived Knowledge Levels Across Demographic Variables using Kruskal-Wallis H test Theories within the Intelligence-Friendly Classroom Framework Demographic variables (1) Years of Teaching Experience (2) Undergraduate Major (3) Number of Courses on Multi-Dimensional Intelligence Theories in Bachelor's Program (4) Number of Prof. Sessions in Teaching Career Knowledge (Total) .054 .018* .002* .001* Fluid & Crystallized Intelligence (FCI) Theory .180 .003* .011* .003* Multiple Intelligence (MI) Theory .905 .266 .004* .001* Successful Intelligence (SI) Theory .051 .041* .012* .001* Implicit Theory of Intelligence (ITI) .063 .392 .898 .065 Note. Reported values are p-values from the Kruskal-Wallis H test. * p < .05. When participants' perceived knowledge levels of FCI, MI, SI, and ITI theories were examined based on their years of teaching experience, no significant difference was found. However, when evaluated with other variables, a significant difference was found in the levels of perceived knowledge about at least two theories of intelligence. For instance, when the participants were assessed according to their undergraduate major, significant differences emerged in their general perceived knowledge levels of the related theories (χ² = 8.058; p = .018, η2 = .04), their perceived knowledge levels of the FCI Theory (χ² = 11.792; p = .003, η2 = .07), and their understanding of the SI Theory (χ² = 6.376; p = .041, η2 = .03). Results are shown in Table 11 below. 86 Table 11 The Results of Kruskal-Wallis H Tests Regarding Undergraduate Major Dimensions Demographic Variables & Groups N Mean χ² df p Effect size (η2) 95% CI Undergrad Major GKL (1) Arts and Humanities 61 61.20 8.058 2 .018 .04 [-.10, .18] (2) Social Sciences 51 83.29 (3) Science & Technology 30 72.38 FCI Theory (1) Arts and Humanities 61 58.20 11.792 2 .003 .07 [-.15, .29] (2) Social Sciences 51 83.71 (3) Science & Technology 30 77.78 SI Theory (1) Arts and Humanities 61 64.21 6.376 2 .041 .03 [-.09, .15] (2) Social Sciences 51 82.95 (3) Science & Technology 30 66.85 Note. Abbreviations: CI, Confidence Interval; SI Theory, Successful Intelligence Theory; GKL, General Knowledge Level; FCI Theory, Fluid and Crystallized Intelligence Theory. Since a significant difference was found as a result of the Kruskal-Wallis H analysis for the variable of Undergrad Major concerning the General Knowledge Level, the FCI Theory, and the SI Theory dimensions, Dunn's pairwise tests were performed as a complementary analysis to examine specific group differences, with adjustments made using the Bonferroni correction. The results indicate a significant difference between the Arts and Humanities major group and the Social Sciences major group regarding all three dimensions of the general perceived knowledge level, the FCI Theory, and the SI Theory (Adj p = .014; Adj p = .003; and Adj p = .046 respectively). However, no significant differences were observed between Arts and Humanities majors versus Science and Technology majors, or Science and Technology majors versus Social Sciences majors in all dimensions. These findings underscore the potential influence of undergraduate major on responses, particularly distinguishing between Arts and Humanities majors and Social Sciences majors, with no significant disparities noted between other major groups. 87 Additionally, based on the evaluation of the variable "Number of Courses on the Multidimensional Intelligence Theories in their Bachelor's Program," significant differences were found in participants' perceived general knowledge (GKL) about the four theories (χ² = 14.601; p = .002) (see Table 12). Specifically, significant differences were observed in three intelligence theories: Fluid and Crystallized Intelligence Theory (χ² = 11.134; p = .011), Multiple Intelligence Theory (χ² = 13.145; p = .004), and Successful Intelligence Theory (χ² = 10.919; p = .012). Dunn-Bonferroni post-hoc test was subsequently conducted for multiple comparisons to investigate specific group differences. A significant difference was found between participants who reported "Never" being exposed to courses and those who reported exposure to "2 courses" (Adj p = .008), as well as between "Never" and "3 courses" (Adj p = .018) in the GKL. Similarly, in the FCI Theory dimension, participants reporting "Never" exposure to courses exhibited significant differences compared to those reporting exposure to "2 courses" (Adj p = .012). In the MI Theory dimension, a significant difference was observed only between participants reporting "Never" exposure and those reporting exposure to "2 courses" (Adj p = .003). For the SI Theory dimension, participants reporting "Never" exposure exhibited significant differences compared to those reporting exposure to "3 courses" (Adj p = .014). These results suggest that educators who participated in more courses during their bachelor's degree program on multidimensional intelligence theories reported a stronger understanding of these theories. 88 Table 12 The Results of Kruskal-Wallis H Tests for Number of Courses on Multidimensional Intelligence Theories in Bachelor's Program Dimensions Demographic Variables & Groups N Mean χ² df p Effect size (η2) 95% CI Number of Courses on Multidimensional Intelligence Theories in Your Bachelor's Program GKL (1) Never 50 52.15 14.601 3 .002 .09 [.02, .16] (2) 1 course 31 64.44 (3) 2 courses 34 79.15 (4) 3 or more courses 16 84.38 FCI Theory (1) Never 50 53.16 11.134 3 .011 .06 [.01, .12] (2) 1 course 31 67.06 (3) 2 courses 34 79.01 (4) 3 or more courses 16 76.41 MI Theory (1) Never 50 52.87 13.145 3 .004 .08 [.01, .15] (2) 1 course 31 65.37 (3) 2 courses 34 82.16 (4) 3 or more courses 16 73.91 SI Theory (1) Never 50 56.82 10.919 3 .012 .06 [.01, .12] (2) 1 course 31 61.31 (3) 2 courses 34 72.62 (4) 3 or more courses 16 89.72 Note. Abbreviations: CI, Confidence Interval; GKL, General Knowledge Level; FCI Theory, Fluid and Crystallized Intelligence Theory; MI Theory, Multiple Intelligence Theory; SI Theory, Successful Intelligence Theory. Considering the results, there appears to be an upward trend between the number of courses taken during the bachelor’s program and the overall perceived knowledge of the related theories. Therefore, the Spearman rank correlation analysis was conducted to determine if there is a significant positive relationship between the number of courses exposing students to multidimensional intelligence theories and various dimensions of perceived knowledge. As a result, a significant positive correlation with overall knowledge (rs = .30, p < .001) was found. This suggests that exposure to multidimensional intelligence theories enhances general perceived 89 knowledge. Additionally, significant correlations were found with specific dimensions of perceived knowledge: FCI theory (rs = .22, p < .05), MI theory (rs = .31, p < .001), and SI theory (rs = .27, p < .001). These findings indicate that coursework about multidimensional intelligence theories contributes to a deeper understanding of these areas of intelligence. However, there was no significant correlation with perceived knowledge of ITI (rs = -.009, p = .917), suggesting that this dimension might not be as influenced by coursework exposure as the others. Similarly, significant differences were found in participants' general perceived knowledge based on the variable "Professional Sessions Attended on the multidimensional intelligence Theories in their Teaching career" (χ² = 30.658; p < .001). Significant differences were also observed in three intelligence theories: Fluid and Crystallized Intelligence Theory (χ² = 14.029; p = .003), Multiple Intelligence Theory (χ² = 35.780; p < .001), and Successful Intelligence Theory (χ² = 15.826; p = .001) (refer to Table 13 below). Following that, the Dunn-Bonferroni post-hoc test was utilized to examine group distinctions across multiple comparisons. A significant difference was observed between participants who reported "Never" being exposed to sessions and those who reported exposure to "1 session" (Adj p = .002), "2 sessions" (Adj p < .001), and "3 sessions" (Adj p < .001) in the GKL dimension. Similarly, in the FCI Theory dimension, significant differences were found between participants reporting "Never" exposure and those reporting exposure to "3 sessions" (Adj p = .004). In the MI Theory dimension, significant differences were noted between participants reporting "Never" exposure and those reporting exposure to "1 session" (Adj p < .001), "2 sessions" (Adj p < .001) and "3 sessions" (Adj p = .002). Regarding the SI Theory dimension, participants reporting "Never" exposure showed significant differences compared to those reporting exposure to "1 session" (Adj p = .042), "2 sessions" (Adj p = .022), and "3 sessions" (Adj p 90 = .022). These findings suggest that educators who participated in more professional development sessions related to multidimensional intelligence theories in their career reported a stronger understanding of these theories. Table 13 The Results of Kruskal-Wallis H Tests for Professional Sessions Attended on the Multidimensional Intelligence Theories in Teaching Careers Dimensions Demographic Variables & Groups N Mean χ² df p Effect size (η2) 95% CI Professional Sessions Attended on Multidimensional Intelligence Theories in Your Teaching Career GKL (1) Never 64 47.87 30.658 3 .001 .21 [.11, .31] (2) 1 session 21 82.93 (3) 2 sessions 24 84.31 (4) 3 or more sessions 24 86.77 FCI Theory (1) Never 64 55.23 14.029 3 .003 .08 [.01, .15] (2) 1 session 21 77.29 (3) 2 sessions 24 69.92 (4) 3 or more sessions 24 86.48 MI Theory (1) Never 64 46.78 35.780 3 .001 .25 [.15, .35] (2) 1 session 21 91.43 (3) 2 sessions 24 86.77 (4) 3 or more sessions 24 79.77 SI Theory (1) Never 64 53.34 15.826 3 .001 .10 [.03, .17] (2) 1 session 21 79.19 (3) 2 sessions 24 79.83 (4) 3 or more sessions 24 79.92 Note. Abbreviations: CI, Confidence Interval; GKL, General Knowledge Level; FCI Theory, Fluid and Crystallized Intelligence Theory; MI Theory, Multiple Intelligence Theory; SI Theory, Successful Intelligence Theory. Based on the results, there seems to be a positive trend between the number of professional development sessions related to multidimensional intelligence theories attended 91 during one’s teaching career and the perceived knowledge of participants. The Spearman rank correlation analysis was performed to evaluate the significance of this relationship across various dimensions of perceived knowledge. The analysis revealed a notable positive correlation with overall knowledge (rs = .34, p < .001), indicating that increased exposure to professional development sessions enhances overall understanding in this area. Additionally, significant correlations were observed with specific perceived knowledge dimensions: FCI theory (rs = .22, p < .05), MI theory (rs = .34, p < .001), and SI theory (rs = .24, p < .05). These results indicate a correlation between participation in professional development sessions related to multidimensional intelligence theories and educators’ perceived understanding of these specific types of intelligence. In contrast to the previous variable (i.e., number of courses taken during bachelors' degree), there was a significant positive correlation with perceived knowledge of ITI or mindset theory (rs = .18, p < .05). This suggests that participation in professional development sessions may be associated with a positive influence on educators' mindsets. 4.3. Educators' Skills in Applying FCI, MI, SI, and ITI Theories Aligned with the study's aim, our investigation centered on the second fundamental question: "What is the extent of educators' self-perceived skills in applying FCI, MI, SI, and ITI theories?" Similar to the previously discussed knowledge section, the skills dimension of the survey examines the same four theories as part of the intelligence-friendly classroom framework: (1) FCI Theory, (2) MI Theory, (3) SI Theory, and (4) ITI. Employing a same approach and organizational structure, our objective was to ascertain educators' perceived competence levels in these theories. The skills section is structured around three key perceived abilities: the ability to explain the relevant theory, the ability to design activities that align with the intelligences described in the theory, and the ability to create assessments for those intelligences. Descriptive 92 statistics (M, Mdn, SD, etc.) for these theories are outlined in Table 14. Subsequently, we evaluated whether perceived skill levels in each theory exhibited variations based on demographic variables. Table 14 Descriptive Statistics for Participants’ Perceived Skills of Theories within the Intelligence-Friendly Classroom Framework Theories within the Intelligence-Friendly Classroom Framework Descriptive statistics M Mdn SD Range Potential Observed Skills (Total) 3.09 3.30 .915 1.00 – 5.00 1.00 – 4.52 Fluid & Crystallized Intelligence (FCI) Theory 2.41 2.50 1.02 1.00 – 5.00 1.00 – 5.00 Multiple Intelligence (MI) Theory 3.26 3.54 1.10 1.00 – 5.00 1.00 – 5.00 Successful Intelligence (SI) Theory 2.90 3.20 1.07 1.00 – 5.00 1.00 – 5.00 Implicit Theory of Intelligence (ITI) 3.73 4.00 .962 1.00 – 5.00 1.00 – 5.00 Table 14 illustrates descriptive statistics for the General Skills Dimension and its sub-dimensions. Likewise, as explained in the data preparation section above, the overall skills level calculated as the weighted mean of four subdimensions in the skills section since all theories have the different number of items. In practice, total scores (mean) are commonly used in research even with non-normally distributed data, especially when the primary focus is on overall trends rather than specific item-level responses (Lewis, 1993; Norman, 2010). Overall skills level reflects participants' perceived proficiency in the three skills mentioned above: the ability to explain the relevant theory, design activities, and create assessments within the frameworks of the related theory. The weighted mean for the overall skills dimension (M = 3.10), with a median of 3.30 and a standard deviation (SD) of .915, indicates an average perceived skill proficiency among educators with a notable of variability. Once delving into specific intelligence theories, educators show the lowest perceived proficiency in the FCI Theory with notable variability (M = 2.41, Mdn = 2.50, SD = 1.02). Conversely, the MI Theory exhibited a higher 93 average perceived skill level (M = 3.26, Mdn = 3.54) with moderate variability (SD = 1.10). The SI Theory demonstrates a slightly lower average perceived skill score based on the weighted mean (M = 2.90) and moderate perceived skill score based on median (Mdn = 3.20), akin to MI Theory in variability. Notably, the ITI has the highest mean skill score (M = 3.73, Mdn = 4.00) with relatively low variability (SD = .962). These findings suggest that while ITI or mindset theory is a theory that has the highest level of perceived proficiency among participants and that participants feel they are more proficient in, while they also feel they have the lowest proficiency in applying FCI Theory. It was then examined whether the educators' overall perceived skills level, and specifically their proficiency in applying the related theories, varied based on their demographic characteristics. A Mann-Whitney U test was performed to evaluate whether participants' perceived skills level about the four theories differed based on their (1) gender, (2) first language, (3) highest level of education, and (4) status of being a teacher or teacher candidate (see Table 15). Table 15 Comparison of Participants' Perceived Skills Levels Across Demographic Variables using Mann-Whitney U Test Theories within the Intelligence-Friendly Classroom Framework Demographic variables Gender First Language Highest Level of Education Teacher Status Skills (Total) .304 .400 .583 .144 Fluid & Crystallized Intelligence (FCI) Theory .321 .892 .215 .482 Multiple Intelligence (MI) Theory .252 .164 .080 .017* Successful Intelligence (SI) Theory .934 .450 .808 .458 Implicit Theory of Intelligence (ITI) .042* .334 .805 .008* Note. Reported values are p-values from the Mann-Whitney U Test. * p < .05. 94 In terms of gender, significant differences were observed in the perceived skills level of the ITI. Specifically, females have a higher level of perceived skills on the ITI than males (z = -2.031, p = .042). Regarding the variables of first language and highest level of education, no significant differences were found. Lastly, concerning the status of being a teacher or teacher candidate variable, it was found that in-service teachers demonstrated higher perceived proficiency levels in MI Theory (z = -2.393, p = .017) and ITI (z = -2.646, p = .008) compared to pre-service teachers (teacher candidates). The results of the Mann Whitney U test can be found in Table 16 below. Table 16 The Results of Mann-Whitney U Tests (Skills) Dimensions Demographic Variables & Groups N Mean U z p Effect size (r) 95% CI Gender ITI (1) Female 101 73.06 1408.00 -2.031 .042 .21 [.05, .38] (2) Male 36 57.61 Status of Being Teacher or Teacher Candidate MI Theory (1) Teacher / In-service Teacher 70 71.64 1530.50 -2.393 .017 .22 [.05, .39] (2) Teacher Candidate / Preservice Teacher 58 55.89 ITI (1) Teacher / In-service Teacher 70 72.30 1484.00 -2.646 .008 .24 [.07, .41] (2) Teacher Candidate / Preservice Teacher 58 55.09 Note. Abbreviations: CI, Confidence Interval; ITI, Implicit Theory of Intelligence; and MI Theory, Multiple Intelligence Theory. Kruskal-Wallis H tests were performed to investigate potential variances in participants' perceived skill levels related to FCI, MI, SI, and ITI theories across various demographic factors, including (1) years of teaching experience, (2) undergraduate major, (3) number of multidimensional intelligence theories courses taken during their bachelor's program, and (4) 95 number of professional sessions attended on multidimensional intelligence theories during their teaching career (refer to Table 17). Table 17 Comparison of Participants' Perceived Skills Levels Across Demographic Variables using Kruskal-Wallis H test Theories within the Intelligence-Friendly Classroom Framework Demographic variables (1) Years of Teaching Experience (2) Undergraduate Major (3) Number of Courses on Multidimensional Intelligence Theories Theories in Bachelor's Program (4) Number of Prof. Sessions in Teaching Career Skills (Total) .053 .006* <.001* <.001* Fluid & Crystallized Intelligence (FCI) Theory .251 .212 .013* <.001* Multiple Intelligence (MI) Theory .018* .004* <.001* <.001* Successful Intelligence (SI) Theory .081 .080 <.001* <.001* Implicit Theory of Intelligence (ITI) .038* <.001* .007* <.001* Note. Reported values are p-values from the Kruskal-Wallis H test. * p < .05. Regarding the variable of years of teaching experience (refer Table 18), the participants' perceived skills levels of the MI Theory and the ITI varied (χ² = 10.116; p = .018; χ² = 8.428; p = .038 respectively). Since a significant difference was found through the Kruskal-Wallis H analysis for the variable of years of teaching experience regarding the perceived skill levels of MI Theory and ITI, Dunn's pairwise tests were conducted as a supplementary analysis to explore specific group differences, with adjustments made using the Bonferroni correction. The results reveal a significant difference between educators with 0-2 years of teaching experience and those with +10 years of teaching experience in both MI Theory and ITI (Adj p = .014; Adj p =.028 respectively). No other pairwise comparisons yielded statistically significant differences. This suggests that the differences in perceived proficiency levels are most pronounced when comparing educators with limited teaching experience (0-2 years) to those with extensive 96 experience (10+ years). This finding indicates that the perceived proficiency in MI Theory and ITI may undergo significant variation during the early stages of teaching but stabilize as educators accumulate more experience over the course of their careers. Table 18 The Results of Kruskal-Wallis H Tests Regarding Years of Teaching Experience Dimensions Demographic Variables & Groups N Mean χ² df p Effect size (η2) 95% CI Years of Teaching Experience MI Theory (1) 0-2 years 66 64.27 10.116 3 .018 .05 [-.01, .10] (2) 3-5 years 28 65.63 (3) 6-10 years 25 76.00 (4) +10 years 23 94.50 ITI (1) 0-2 years 66 63.13 8.428 3 .038 .04 [-.01, .09] (2) 3-5 years 28 75.82 (3) 6-10 years 25 70.80 (4) +10 years 23 91.02 Note. Abbreviations: CI, Confidence Interval; MI Theory, Multiple Intelligence Theory; ITI, Implicit Theory of Intelligence. When the participants were evaluated based on their undergraduate major (refer Table 19), there were significant differences in the overall perceived proficiency levels (GSL) (χ² = 10.286; p = .006), MI Theory (χ² = 11.283; p = .004), and ITI theory (χ² = 18.728; p < .001). This necessitated Dunn-Bonferroni post-hoc tests to establish specific group differences. The overall perceived proficiency levels (GSL) differed significantly between Arts/Humanities and Social Sciences (Adj p = .031) as well as between Science/Technology and Social Sciences (Adj p = .012). However, no significant differences were found between Science/Technology and Art/Humanities. Similarly, the perceived proficiency levels for MI theory differed significantly between Arts/Humanities and Social Sciences (Adj p = .013) as well as between Science/Technology and Social Sciences (Adj p = .013). Concerning the ITI dimension, there 97 were two comparisons with significant outcomes: (1) Science/Technology versus Art/Humanities (Adj p < .030), and (2) Science/Technology versus Social Sciences (Adj p < .001). The results indicate that there are variations in participants’ perceived skill levels regarding GSL, MI theory and ITI theory according to their undergrad major fields. Additionally, they highlight differing skill levels across various academic disciplines. Table 19 The Results of Kruskal-Wallis H Tests Regarding Undergrad Major Dimensions Demographic Variables & Groups N Mean χ² df p Effect size (η2) 95% CI Undergrad Major GSL (1) Arts and Humanities 61 93.98 10.286 2 .006 .06 [.01, .12] (2) Social Sciences 51 72.39 (3) Science & Technology 30 62.40 MI Theory (1) Arts and Humanities 61 98.98 11.283 2 .004 .07 [.01, .13] (2) Social Sciences 51 73.61 (3) Science & Technology 30 59.75 ITI (1) Arts and Humanities 61 70.32 18.728 2 .001 .12 [.04, .20] (2) Social Sciences 51 87.34 (3) Science & Technology 30 46.97 Note. Abbreviations: CI, Confidence Interval; GSL, General Skills Level; MI Theory, Multiple Intelligence Theory; and ITI, Implicit Theory of Intelligence. Additionally, based on the evaluation of the variable "Number of Courses on the Multidimensional Intelligence Theories in their Bachelor's Program," significant differences were found in participants' perceived general skills (GSL) (χ² = 27.914; p < .001) (see Table 20). Specifically, significant differences were observed in all four intelligence theories: FCI Theory (χ² = 10.782; p = .013), MI Theory (χ² = 30.004; p < .001), SI Theory (χ² = 21.756; p < .001), and ITI theory (χ² = 12.111; p = .007). 98 Table 20 The Results of Kruskal-Wallis H Tests for Number of Courses on Multidimensional Intelligence Theories in Bachelor's Program (Skills) Dimensions Demographic Variables & Groups N Mean χ² df p Effect size (η2) 95% CI Number of Courses on Multidimensional Intelligence Theories in Your Bachelor's Program GSL (1) Never 50 46.16 27.914 3 <.001 .20 [.17, .23] (2) 1 course 31 68.23 (3) 2 courses 34 78.87 (4) 3 or more courses 16 96.34 FCI Theory (1) Never 50 52.89 10.782 3 .013 .06 [.01, .12] (2) 1 course 31 69.26 (3) 2 courses 34 75.56 (4) 3 or more courses 16 80.34 MI Theory (1) Never 50 45.76 30.004 3 <.001 .21 [.11, .31] (2) 1 course 31 69.19 (3) 2 courses 34 76.94 (4) 3 or more courses 16 99.81 SI Theory (1) Never 50 48.10 21.756 3 <.001 .15 [.06, .24] (2) 1 course 31 69.34 (3) 2 courses 34 77.26 (4) 3 or more courses 16 91.53 ITI (1) Never 50 56.82 12.111 3 .007 .07 [.01, .13] (2) 1 course 31 61.31 (3) 2 courses 34 72.62 (4) 3 or more courses 16 89.72 Note. Abbreviations: CI, Confidence Interval; GSL, General Skills Level; FCI Theory, Fluid and Crystallized Intelligence Theory; MI Theory, Multiple Intelligence Theory; SI Theory, Successful Intelligence Theory; and ITI, Implicit Theory of Intelligence. Subsequently, Dunn-Bonferroni post-hoc test was conducted for multiple comparisons to investigate specific group differences. A significant difference was found between participants who reported "Never" being exposed to courses and those who reported exposure to "2 courses" (Adj p < .001), as well as between "Never" and "3 courses" (Adj p < .001) in the GSL. In the FCI Theory dimension, a significant difference was observed only between participants reporting 99 "Never" exposure and those reporting exposure to "2 courses" (Adj p = .041). Regarding the MI Theory dimension, participants reporting "Never" exposure to courses exhibited significant differences compared to those reporting exposure to "1 courses" (Adj p = .041), compared to those reporting exposure to "2 courses"(Adj p < .001), and compared to those reporting exposure to "3 courses"(Adj p < .001). For the SI Theory dimension, a significant difference was found between participants who reported "Never" being exposed to courses and those who reported exposure to "2 courses" (Adj p = .003), as well as between "Never" and "3 courses" (Adj p < .001). Finally, in the ITI dimension, a significant difference was observed only between participants reporting "Never" exposure and those reporting exposure to "2 courses" (Adj p = .040). Based on these findings, a Spearman rank correlation analysis was conducted to explore whether there is a significant positive link between the number of courses on multidimensional intelligence theories and various perceived skill dimensions. The analysis revealed a notable positive correlation with overall skills (rs = .46, p < .001), indicating that more exposure to multidimensional intelligence theories during undergraduate studies may be associated with general skill development in the proficiency level of the related theories. Additionally, significant positive correlations were identified with specific perceived skill dimensions: skills related to FCI theory (rs = .28, p < .001), MI theory (rs = .46, p < .001), SI theory (rs = .40, p < .001), and ITI or mindset theory (rs = .29, p < .001). These results suggest that coursework involving multidimensional intelligence theories is positively associated with the development of skills related to the specific theories in the study (FCI, MI, SI, and ITI). Similarly, significant differences were detected in participants' perceived general skills (GSL) on the variable "Professional Sessions Attended on the Multidimensional Intelligence Theories in their Teaching career" (χ² = 52.670; p < .001). Once again, significant differences 100 were observed in all four intelligence theories: FCI Theory (χ² = 18.606; p < .001), MI Theory (χ² = 61.694; p < .001), SI Theory (χ² = 30.941; p < .001), and ITI (χ² = 24.686; p < .001) (refer to Table 21). Following that, the Dunn-Bonferroni post-hoc test was utilized to examine group distinctions across multiple comparisons. A significant difference was observed between participants who reported "Never" being exposed to sessions and those who reported exposure to "1 session" (Adj p < .001), "2 sessions" (Adj p < .001), and "3 sessions" (Adj p < .001) in the GSL dimension. Similarly, in the FCI Theory dimension, significant differences were found between participants reporting "Never" exposure and those reporting exposure to "1 sessions" (Adj p = .036), and those reporting exposure to "2 sessions" (Adj p = .015), and those reporting exposure to "3 sessions" (Adj p = .004). Likewise, in the MI Theory dimension, significant differences were noted between participants reporting "Never" exposure and those reporting exposure to "1 session" (Adj p < .001), "2 sessions" (Adj p < .001) and "3 sessions" (Adj p =.002). Regarding the SI Theory dimension, participants reporting "Never" exposure showed significant differences compared to those reporting exposure to "1 session" (Adj p = .002), "2 sessions" (Adj p =.003), and "3 sessions" (Adj p < .001). In the ITI dimension, significant differences were noted between participants reporting "Never" exposure and those reporting exposure to "1 session" (Adj p = .024), "2 sessions" (Adj p = .014) and "3 sessions" (Adj p < .001). These findings suggest that educators who participated in at least one professional session in their teaching career reported a stronger proficiency in applying the four theories (FCI, MI, Si, and ITI) in classrooms. 101 Table 21 Results of Kruskal-Wallis H Tests for Professional Sessions Attended on the Multidimensional Intelligence Theories in Teaching Careers (Skills) Dimensions Demographic Variables & Groups N Mean χ² df p Effect size (η2) 95% CI Professional Sessions Attended on Multidimensional Intelligence Theories in Your Teaching Career GSL (1) Never 64 42.27 52.670 3 <.001 .39 [.27, .51] (2) 1 session 21 96.52 (3) 2 sessions 24 81.69 (4) 3 or more sessions 24 92.44 FCI Theory (1) Never 64 52.30 18.606 3 <.001 .12 [.04, .20] (2) 1 session 21 84.90 (3) 2 sessions 24 77.50 (4) 3 or more sessions 24 80.02 MI Theory (1) Never 64 40.23 61.694 3 <.001 .45 [.33, .57] (2) 1 session 21 84.90 (3) 2 sessions 24 77.50 (4) 3 or more sessions 24 80.02 SI Theory (1) Never 64 48.08 30.941 3 <.001 .22 [.12, .32] (2) 1 session 21 81.33 (3) 2 sessions 24 81.10 (4) 3 or more sessions 24 90.81 ITI (1) Never 64 50.52 24.686 3 <.001 .16 [.07, .25] (2) 1 session 21 78.07 (3) 2 sessions 24 78.25 (4) 3 or more sessions 24 90.02 Note. Abbreviations: CI, Confidence Interval; GKL, General Knowledge Level; FCI Theory, Fluid and Crystallized Intelligence Theory; MI Theory, Multiple Intelligence Theory; SI Theory, Successful Intelligence Theory; and ITI, Implicit Theory of Intelligence. Based on the results, there again appears to be a positive trend between the number of professional development sessions related to multidimensional intelligence theories attended during one’s teaching career and the development of skills. The Spearman correlation analysis was conducted to determine if there is a significant positive relationship between the number of professional development sessions and various skill dimensions. The analysis revealed a 102 significant positive correlation with overall perceived skills level (GSL) (rs = .48, p < .001), indicating that increased participation in professional development sessions may enhance general skill development. Additionally, significant correlations were observed with specific skill dimensions: perceived skills related to FCI theory (rs = .29, p < .001), MI theory (rs = .53, p < .001), SI theory (rs = .38, p < .001), and ITI or mindset theory (rs = .36, p < .001). These findings indicate a correlation between participation in professional development sessions related to multidimensional intelligence theories and educators’ perceived skills of these specific types of intelligence. 4.4. Educators' Attitudes Towards Constructing an Intelligence-Friendly Classroom The attitude dimension, along with the subsequent self-efficacy dimension, aims to assess participants' attitudes and self-efficacy toward the intelligence-friendly classroom approach in general. Descriptive statistics for participants’ attitudes toward applying intelligence-friendly classroom are outlined in Table 22. Subsequently, it was evaluated whether the attitude level exhibited variations based on demographic variables. Table 22 Descriptive Statistics for Attitude Dimension towards Creating Intelligence-Friendly Classrooms Descriptive statistics M Mdn SD Range Potential Observed Attitude 4.34 4.36 .461 1.00 – 5.00 2.09 – 5.00 Table 22 presents descriptive statistics for the attitude dimension related to creating an intelligence-friendly classroom. The overall attitude level, calculated as the mean of all items in the attitude section, reflects participants' attitude towards creating intelligence-friendly classrooms via applying FCI, MI, SI, and ITI theories. The mean (M) for the total attitude score, 103 is 4.34 with a median (Mdn) of 4.36 and a low standard deviation (SD) of .461, indicating a high level of attitude among participants with relatively low variability. This finding demonstrates that the majority of educators hold a very positive attitude towards creating intelligence-friendly classrooms. It was then examined whether the educators' overall attitude level varied based on their demographic characteristics. A Mann-Whitney U test was performed to evaluate whether participants' attitudes toward creating intelligence-friendly classrooms differed by their (1) gender, (2) first language, (3) highest level of education, and (4) status of being a teacher or teacher candidate. No significant differences were found for all mentioned variables according to the results of the Mann-Whitney U test (see Table 23). This means that demographic characteristics, including gender, first language, highest level of education, and status as a teacher or teacher candidate, do not appear to influence participants' attitudes towards creating intelligence-friendly classrooms. Table 23 Comparison of Participants' Perceived Attitude Level Across Demographic Variables using Mann-Whitney U Test Demographic variables Gender First Language Highest Level of Education Teacher Status Attitude .178 .480 .119 .648 Note. Reported values are p-values from the Mann-Whitney U Test. * p < .05. Kruskal-Wallis H tests were conducted to explore potential differences in participants' attitudes toward creating intelligence-friendly classrooms across various demographic variables, including (1) years of teaching experience, (2) undergraduate major, (3) number of courses on 104 the multidimensional intelligence theories in their bachelor's program, and (4) number of professional sessions attended on the multidimensional intelligence theories in their teaching career (see Table 24). Table 24 Comparison of Participants' Perceived Attitude Level Across Demographic Variables using Kruskal-Wallis H test Demographic variables (1) Years of Teaching Experience (2) Undergraduate Major (3) Number of Courses on Multidimensional Intelligence Theories in Bachelor's Program (4) Number of Prof. Sessions in Teaching Career Attitude .291 .008* .058 .527 Note. Reported values are p-values from the Kruskal-Wallis H test. * p < .05. When participants' attitudes toward creating intelligence-friendly classrooms were examined based on their years of teaching experience, number of professional sessions attended on the multidimensional intelligence theories in their teaching career, and number of courses on the multidimensional intelligence theories in their bachelor's program, no significant difference was found. However, significant differences emerged when participants were assessed according to their undergraduate major in their general attitude level toward creating intelligence-friendly classrooms (χ² = 9.687; p = .008). Results are shown in Table 25 below. Since a significant difference was found as a result of the Kruskal-Wallis H analysis for the variable of Undergrad Major concerning the dimension of participants' attitude, Dunn's pairwise tests with the Bonferroni correction were performed as a complementary analysis in order to examine specific group differences. The results indicate a significant difference between science/technology and art/humanities (Adj p = .046). No other pairwise comparisons yielded statistically significant 105 differences. This suggests that individuals who studied science and technology tend to hold different attitudes compared to those who studied arts and humanities, meaning their perspectives or opinions on topics related to creating intelligence-friendly classrooms may differ significantly. These varying perspectives highlight the importance of considering educational backgrounds when discussing and implementing strategies for intelligence-friendly classrooms. Table 25 The Results of Kruskal-Wallis H Tests Regarding Undergraduate Major (Attitude) Dimensions Demographic Variables & Groups N Mean χ² df p Effect size (η2) 95% CI Undergrad Major Attitude (1) Arts and Humanities 61 82.61 9.687 2 .008 .05 [-.01, .10] (2) Social Sciences 51 67.86 (3) Science & Technology 30 55.10 Note. Abbreviations: CI, Confidence Interval. 4.5. Educators' Self-Efficacy in Constructing an Intelligence-Friendly Classroom The last crucial aspect of the study's goal is: "What level of self-efficacy do educators demonstrate in constructing an intelligence-friendly classroom?" Similar to the attitude dimension discussed earlier, this self-efficacy dimension does not have subdimensions related to theories of intelligence as well. Descriptive statistics pertaining to participants' perceived sense of self-efficacy were presented in Table 26. Table 26 Descriptive Statistics for Self-Efficacy Dimension towards Creating Intelligence-Friendly Classrooms Descriptive statistics M Mdn SD Range Potential Observed Self-Efficacy 3.39 3.57 .915 1.00 – 5.00 1.00 – 5.00 106 Table 26 presents descriptive statistics for the self-efficacy dimension related to creating an intelligence-friendly classroom. The overall self-efficacy level, calculated as the mean of all items in the self-efficacy section, reflects participants' self-efficacy in creating intelligence-friendly classrooms through the application of FCI, MI, SI, and ITI theories. The mean (M) for the total self-efficacy score is 3.39 with a median (Mdn) of 3.57 and high standard deviation (SD) of .915, indicating a moderate level of self-efficacy among participants with relatively high variability. This means that most educators feel moderately confident in creating intelligence-friendly classrooms, though some vary significantly. It was then investigated whether educators' self-efficacy varied depending on their demographic characteristics. A Mann-Whitney U test was conducted to assess whether participants' self-efficacy in creating intelligence-friendly classrooms differed based on their (1) gender, (2) first language, (3) highest level of education, and (4) status of being a teacher or teacher candidate. No significant differences were found for these variables (see Table 27). Table 27 Comparison of Participants' Perceived Self-Efficacy Level Across Demographic Variables using Mann-Whitney U Test Demographic variables Gender First Language Highest Level of Education Teacher Status Self-Efficacy .331 .987 .304 .106 Note. Reported values are p-values from the Mann-Whitney U Test. * p < .05. In addition, Kruskal-Wallis H tests were employed to investigate potential variances in participants' level of self-efficacy regarding the creation of intelligence-friendly classrooms across diverse demographic factors, encompassing (1) years of teaching experience, (2) undergraduate major, (3) number of courses on the multidimensional intelligence theories 107 completed during their bachelor's program, and (4) number of professional sessions attended on multidimensional intelligence theories during their teaching career (refer to Table 28). Table 28 Comparison of Participants' Perceived Self-Efficacy Level Across Demographic Variables using Kruskal-Wallis H test Demographic variables (1) Years of Teaching Experience (2) Undergraduate Major (3) Number of Courses on Multidimensional Intelligence Theories in Bachelor's Program (4) Number of Prof. Sessions in Teaching Career Self-Efficacy .210 .655 .144 .015* Note. Reported values are p-values from the Kruskal-Wallis H test. * p < .05. No significant difference was found in these variables except the variable, number of professional sessions attended on the multidimensional intelligence theories in their teaching career, in their general level of self-efficacy toward creating intelligence-friendly classrooms (χ² = 10.527; p = .015). Results are shown in Table 29 below. Since a significant difference was found as a result of the Kruskal-Wallis H analysis for the variable of number of professional sessions attended on the multidimensional intelligence theories in their teaching career concerning the dimension of participants' self-efficacy, Dunn's pairwise tests with the Bonferroni correction were performed as a complementary analysis in order to examine specific group differences. Significant differences were found between participants reporting "Never" exposure and those reporting exposure to "3 sessions" (Adj p = .010). No other pairwise comparisons yielded statistically significant differences. This states that individuals who never attended such sessions related to the multidimensional intelligence theories tend to hold different levels of self-efficacy compared to those who attended three sessions. 108 Table 29 The Results of Kruskal-Wallis H Tests Regarding the variable of number of professional sessions attended on the Multidimensional Intelligence theories in their teaching career (Self-Efficacy) Dimensions Demographic Variables & Groups N Mean χ² df p Effect size (η2) 95% CI Professional Sessions Attended on Multidimensional Intelligence Theories in Your Teaching Career Self-Efficacy (1) Never 64 57.20 10.527 3 .015 .06 [.01, .12] (2) 1 session 21 72.33 (3) 2 sessions 24 62.67 (4) 3 or more sessions 24 73.54 Note. Abbreviations: CI, Confidence Interval. Based on the findings, it appears that there is a positive trend in the relation between the number of professional development sessions focusing on multidimensional intelligence theories and self-efficacy. Therefore, a Spearman rank correlation analysis was conducted to explore whether there exists a significant positive association between the frequency of professional development sessions and the self-efficacy dimension. The analysis revealed a significant positive correlation between the number of professional development sessions related to multidimensional intelligence theories and self-efficacy (rs = .21, p < .05). This indicates correlation between participation in such sessions and sense of self-efficacy among educators. 4.6. Summary Overall, the study's findings presented in this dissertation demonstrated that educators reported a moderate to high level of familiarity with the four theories (FCI, MI, SI, and ITI) placed within the intelligence-friendly framework. Among these, ITI (mindset theory) and MI theories were found to be more commonly recognized. In terms of perceived skills, educators 109 reported feeling moderately proficient in applying the four theories overall. Specifically, similar to the knowledge dimension, educators felt proficient in applying ITI and MI theories, while reporting less proficiency in applying FCI and SI theories in educational settings. Regardless of demographic variables, almost every participant had very positive attitudes towards constructing intelligence-friendly classrooms. This finding also suggested that educators prioritized approaches that cultivate students' strengths and talents, moving beyond a focus on grades and weaknesses (Swan, 2016). Regarding their self-efficacy, participants demonstrated a moderate level of self-efficacy in building such classrooms. These findings contribute to a deeper understanding of the complex interplay between educator characteristics and the implementation of FCI, MI, SI, and ITI theories in educational settings. In addition, this study has contributed to the relevant literature by investigating the differences of educators' responses based on demographics such as gender, educational experience, professional training, and education level. For instance, findings indicate that female educators are more likely to integrate growth mindset (ITI) frameworks into their teaching practices than their male counterparts. Teaching experience emerged as a critical covariate, possibly positive influencing educators' confidence and ability to apply MI and ITI theories in the classroom. Furthermore, educators with advanced degrees (MA, PhD, etc.) and professional training in intelligence theories demonstrated higher self-efficacy in creating intelligence-friendly classrooms. The relevant trends in educators' responses are presented in more detail in the discussion chapter. Consequently, this study sheds light on educators' current knowledge, skills, attitudes, and self-efficacy in creating intelligence-friendly classroom environments through FCI, MI, SI, and ITI theories. 110 Chapter 5: Discussion The aim of this study was to gain an understanding of educators’ self-perceived knowledge, skills, attitudes, and efficacy on constructing intelligence-friendly classrooms by focusing on the cognitive, sociocultural, developmental, and motivational aspects of intelligence through the integration of the four theories (FCI, MI, SI, and ITI). To the best of the author’s knowledge, this study represents an initial empirical examination of the concept of intelligence-friendly classrooms through the integration of four intelligence theories into a single framework. Having an exploratory purpose, this study employed a cross-sectional survey design to provide insights into the application of FCI, MI, SI, and ITI theories and the construction of intelligence-friendly classrooms in promoting inclusive and strengths-based educational practices. In this chapter, I will begin by briefly highlighting the rationale for the present research and the concept of intelligence-friendly classrooms in 21st-century learning. Following, I will discuss the intelligence-friendly classroom framework introduced in this study, which integrates the applications of FCI, MI, SI, and ITI theories. I will then highlight systemic and structural issues influencing the recognition of students’ strengths and discuss how the IFC framework can contribute to mitigating educational inequalities as well as societal polarization. Next, I will provide a general discussion of the study's findings. This will be followed by educational implications of the research, suggestions for future research, and considerations of strengths and limitations of the study. Finally, I will conclude by emphasizing the study's importance and its contributions to the fields of inclusive education and strengths-based education. 5.1. Intelligence-Friendly Classrooms and 21st Century Learning The 21st century has redefined the goals of education by prioritizing inclusivity, diversity, and equity, as well as competency-based approaches that promote deeper learning and 111 transferable skills across contexts (British Columbia Ministry of Education, 2015; Patrick et al., 2013). While competency-based inclusive education has gained global traction in the 21st century, there is a need for more specific frameworks that integrate multiple aspects of strengths/talents/intelligence to create classrooms that focus on developing students' competencies. Intelligence-friendly classrooms may represent an innovative approach to bridging this gap by aiming to foster equity, recognize diverse intelligences, and cultivate each student’s potential. Such classrooms emphasize the importance of addressing multiple aspects of intelligence through teaching practices that incorporate the application of various intelligence theories (Fogarty, 1998). This inclusive approach ensures that education is not limited to academic achievement but also supports the holistic development of students, preparing them for the complexities of a real-world scenarios (Gardner et al., 2018; Sternberg, 2020). Therefore, the present research aims to contribute to this evolving field by examining educators’ perspectives on constructing intelligence-friendly classrooms that support competency development to meet the demands of 21st-century education. In addition, Davidson and Kemp (2011) emphasized the need for research to promote the use and application of intelligence theories wisely. Even though numerous studies have investigated the application of a single intelligence theory (Blackwell et al., 2007; Kyllonen & Kell, 2017; Rumfot et al., 2024; Sternberg et al., 2006), there are limited research examining the applications of intelligence theories together. Further, no research has examined educators' knowledge, skills, attitudes, and self-efficacy in constructing intelligence-friendly classrooms. Moreover, recent educational reforms in BC have emphasized the importance of competency-based approaches that empower every child to reach their potential. However, challenges remain regarding how educators might better prepare themselves to meet the different needs of students 112 in today's classrooms (Gacoin, 2020; Hill et al., 2020; McCrimmon, 2015). Additionally, while scholars have highlighted the importance of inclusive and egalitarian education environments in 21st century learning (Barab & Plucker, 2002; Lo et al., 2022; Lo & Porath, 2017), there remains a knowledge gap regarding how to translate these principles into classroom practices. Drawing from the results of the survey prepared for this research, this study contributes to addressing these limitations by examining educators' understanding and application of intelligence-friendly practices informed by four intelligence theories. 5.2. Intelligence-Friendly Classroom Framework in This Study The Intelligence-Friendly Classroom (IFC) Framework introduced in this study is an initial conceptual and integrative approach that highlights the developmental, sociocultural, cognitive, and motivational aspects of intelligence, represented by FCI, MI, SI, and ITI theories respectively. Its primary aim is to create inclusive and enriched learning environments where all students can thrive. The focus on recognizing and developing each student's unique strengths highlighted by the framework naturally speaks to the focus on strengths-based approach (Clifton et al., 2006; Lopez & Louis, 2009) in the 21st century learning. Sternberg's theory of successful intelligence (SI) (2020), for example, suggests that by taking into account students' learning preferences, teachers can create curriculum that include what (analytical intelligence), how (practical intelligence), and why questions (creative intelligence) to cater students’ various cognitive-processing strengths (Lo et al., 2022). Accordingly, the combination of analytical, practical, and creative intelligences allows teachers to create a strengths-based learning environment where students can utilize their unique cognitive processing inclinations to succeed. Another example is to apply Gardner's theory of multiple intelligences (MI) (2006), which might 113 offer students multiple ways of demonstrating their potentials through being attuned with their talents endorsed by the sociocultural environment where they situate. Additionally, this new framework promotes a multidimensional perspective on intelligence by emphasizing four key aspects: developmental, sociocultural, cognitive, and motivational. By integrating these aspects, the framework aims to offer a more comprehensive understanding of intelligence, recognizing it as modifiable, multiple, and multifaceted. Rather than viewing intelligence solely as the transmission of content knowledge (Bellanca, 1998), this perspective underscores the role of education in cultivating critical thinking, creativity, and adaptability. Through its focus on these four aspects, the framework seeks to ensure that students are equipped not only with knowledge but also with the skills and attitudes necessary for lifelong success, as stated by educational reforms (British Columbia Ministry of Education, 2015). In this manner, the enriched learning environment fostered by the IFC framework may provide students with opportunities to develop their intelligence holistically (Lo et al., 2022). Importantly, the framework is not intended to categorize students into fixed types; instead, it expands access to multiple pathways for engagement and expression, allowing for more responsive and inclusive pedagogy. By providing teachers information on recognizing and cultivating student strengths through these four aspects, this study contributes to the advancement of strengths-based education practices. While promoting a strengths-based and inclusive approach, it is also important to note that the IFC framework does not contradict psychometric intelligence theories, which focus on general intelligence (e.g., IQ). Instead, it offers an alternative view aimed at improving inclusion in classrooms. These perspectives are complementary rather than mutually exclusive: while psychometric approaches center on the structure of intelligence, the IFC framework is concerned 114 with the effect of environmental and educational factors on the development of intelligence. By introducing the IFC framework into the literature, the present study seeks to bring attention to the potential of multi-dimensional theories of intelligence and their applications in real-world educational contexts. At the same time, implementing any theory of intelligence in educational settings requires careful consideration of systemic and structural factors that can lead to unequal educational experiences, particularly for students from marginalized backgrounds. Socio-economic disadvantage, for example, remains a significant factor for poor educational access and success, particularly because it reduces the chances of enriched learning environments and the opportunity for identification of strengths, talents, and giftedness of students (Ford et al., 2021; Reynen-Woodward et al., 2023). Data analyses from the Programme for International Student Assessment (PISA) support this position by showing that there are significant differences between countries in the correlation between socioeconomic background and achievement (Avvisati, 2020; Montt, 2011; Pokropek et al., 2015). In addition, bias within assessment tools and teacher perceptions and expectations may systematically under-identify the strengths of the culturally and linguistically diverse student population, and students with disabilities (Trainor & Robertson, 2022). For example, students who have multiple exceptionalities—as they are often known, twice-exceptional (2e) learners—are particularly at risk of being missed, because their disabilities may hide their strengths, or their strengths may hide their disabilities, leading to misidentification, or lack of support (Carpenter, 2021). These inequities are often exacerbated by systemic discrimination and ableist assumptions, as well as the increasing polarization of a society where access to education is divided along racial, class, and other social identities. As a result of this, the individual from a group of people that are underrepresented or disadvantaged 115 will more often face additional difficulties in order to get his or her talents recognized and this further contributes to the existing inequalities. In this context, Hansen and Jóhannesson (2023) highlight that there are many concerns about social equality in education and the potential polarization of society. The Intelligence-Friendly Classroom (IFC) framework has a potential to contribute to dissipating these concerns by promoting a more inclusive and equitable view of intelligence. By recognizing and nurturing a broad range of student strengths and intelligences, the IFC framework could serve to minimize biases that often lead to the marginalization of students from different cultural, linguistic, or disability backgrounds. As an example, a teacher working with the IFC framework might: (1) employ differentiated activities that allow students to demonstrate their understandings through the use of multiple intelligences (e.g., musical, bodily-kinesthetic, interpersonal) to promote sociocultural inclusivity; (2) develop problem-based learning experiences that develop students' analytical, creative, and practical thinking skills to promote cognitive diversity; (3) provide feedback that is growth-oriented to further enhance students' motivational beliefs around their learning potential; and (4) provide a series of scaffolded opportunities for students to build crystallized knowledge through time, while recognizing differences in their developmental progress. Through these integrated perspectives and practices, teachers not only support new ways for all students to access meaningful learning opportunities, but they also shape a classroom culture that values equity and actively counters societal polarization. In addition, the framework's acceptance of intelligence as dynamic, multifaceted, and culturally responsive inspires teachers to design classrooms equitable spaces, where each student's potential is recognized and supported for success. By empowering teachers to recognize diverse manifestations of intelligence and challenge systemic biases, the IFC framework contributes to 116 the shift in educational practices that aim to promote social equality and remove the barriers regarding the potential polarization of society (Hansen & Jóhannesson, 2023). 5.3. General Discussion of Findings The results indicate that educators are generally more aware of and feel more comfortable addressing the motivational aspect, represented by the Implicit Theory of Intelligence (ITI, or mindset theory), and the sociocultural aspect, informed by Gardner's Multiple Intelligences (MI) theory. In contrast, the cognitive aspect (Sternberg’s Successful Intelligence (SI) theory) and the developmental aspect (Cattell’s Fluid and Crystallized Intelligence (FCI) theory) were less recognized, and their applications were perceived with less comfort by the participants. This disparity suggests that while certain aspects of the framework (i.e., motivational and sociocultural) have been already integrated into educational practices, others (i.e., cognitive and developmental) remain underutilized. The reason could be that MI and ITI theories have held a strong position in educators' knowledge base for the last two decades (Armstrong, 2009; Gardner et al., 2018; Gardner, 1999; Krechevsky & Seidel, 1998). In addition, both theories have been widely promoted by popular media, educational blogs, and other channels, which may have served to make them more available and popular. Their simplified representations (for example, portraying the idea that "everyone learns differently" for MI theory) and their inclusive ideas could have contributed to their widespread acceptance among educators. In contrast, the lower popularity of SI and FCI theories, compared to MI and ITI, may be due to their limited involvement in social media and teacher education. In the skills section, educators expressed confidence in explaining certain intelligence theories (ITI and MI), as well as in designing activities and assessments that align with the sociocultural and motivational aspects of intelligence via ITI and MI theories. This suggests that 117 educational reforms in BC (British Columbia Ministry of Education, 2015, 2017; Milford et al., 2022) may have already contributed to equipping educators with the ability to recognize and nurture diverse motivational and sociocultural strengths in the classroom. However, it also should be acknowledged that we did not measure everything regarding what teachers are doing in classrooms due to the nature of the survey research. It is possible that educators may already be applying certain strategies from various intelligence theories in their classrooms, which may not have been observed and reported in this study. Therefore, even though this study identified lower levels of perceived knowledge and skills regarding FCI and SI theories, educators may already be incorporating strategies from these theories in their classrooms. Surprisingly, the majority of the participants exhibited a high level of positive attitudes, regardless of demographic variables. The generally positive attitudes of participants reflect their belief in the importance of recognizing and cultivating students’ talents and strengths through the lens of the intelligence-friendly classroom framework. This finding also suggests that educators prioritize approaches that cultivate students' strengths and talents, moving beyond a focus on grades and weaknesses (Swan, 2016). However, their moderate level of self-efficacy in creating intelligence-friendly classrooms matches their perceived knowledge and skills in the study. In other words, having a moderate understanding and ability to apply the four theories together explains why their self-efficacy in creating these classrooms is also moderate. Among the demographic factors, teaching experience and education/training level were particularly influential in shaping educators' responses. The results showed that more experienced educators, as well as those with advanced degrees or specialized training, exhibited greater confidence and proficiency in applying theories within the framework. By identifying 118 these trends, this study provides insights for future research and professional training programs aimed at addressing gaps in educators' preparedness to meet the diverse needs of their students. Teaching Experience. Teaching experience emerged as a critical demographic influencing educators' confidence and ability to apply certain theories (MI and ITI theories for this demographic variable) in the classroom. More experienced educators in the study displayed greater perceived competence in utilizing these theories, likely due to their accumulated practical knowledge and experienced diverse teaching strategies (Klassen & Chiu, 2010; Podolsky et al., 2019; Schmidt, 2010). Research by Podolsky et al. (2019) shows that as educators gain experience, they are more likely to improve their ability to apply educational theories effectively, which contributes to student success beyond test scores. In line with this literature, educators in the study who were in-service teachers expressed feeling comfortable designing activities and assessing multiple intelligences/talents based on MI and ITI theories. The reason may be that pre-service teachers, as well as those with limited experience (1-2 years), could feel uncomfortable applying certain theories in the classrooms due to their lack of experience, which can lead to lower self-efficacy (Penrose et al., 2007). Research indicates that the length of teaching experience has a significant direct impact on teacher self-efficacy (Ben-Peretz et al., 2003; Penrose et al., 2007) and classroom quality (Mims et al., 2008). Considering research mentioned above, the results of the study are consistent with the literature, which suggests that teaching experience enhances educators' effectiveness in applying educational theories in the classroom. Training and Education Level. The study also found that educators with advanced degrees (MA, PhD, EdD) and/or specialized training in intelligence theories (through undergraduate courses and/or professional training sessions) perceived themselves as more 119 skilled at creating intelligence-friendly classrooms. This is consistent with research indicating that higher levels of education and specialized training equip educators with a deeper understanding of complex educational theories and their practical applications (Croninger et al., 2007; Mims et al., 2008; Sancassani, 2023). The reason could be that advanced degrees emphasize critical, creative, and practical thinking, while specialized training provides hands-on strategies adaptable to diverse classroom contexts, including collaboration, study clubs, mentoring, and professional learning communities (Garret et al., 2008; Van Veen et al., 2012). The findings of this study further reveal that educators with advanced qualifications and professional training demonstrated higher self-efficacy in constructing intelligence-friendly classrooms. This finding is consistent with Garet et al. (2001), who demonstrated that professional development activities significantly contribute to teachers’ self-reported increases in knowledge, skills, and classroom practice. Collectively, these findings underscore the critical role of advanced education and professional training in enhancing educators' confidence and effectiveness in applying intelligence theories. In addition to the findings regarding educators' application of intelligence theories, there are a number of broader issues faced by schools today that could possibly discourage teachers' practice as effective agents of change. Schools are, for example, often encounter systemic challenges, including factors such as limited resources, high-stakes testing, and large class sizes (O’Day & Smith, 2016), all of which create barriers for teachers trying to integrate new educational frameworks. These barriers can have an impact on teachers’ ability to engage with and implement the teaching and learning theories like the theories within the Intelligence-Friendly Classroom Framework. 120 Moreover, the role of school leadership and management approaches is critical in either supporting or obstructing the adoption of new teaching practices (Bellibaş et al., 2022). Effective school leadership is necessary to create an environment conducive to professional development, foster a culture of innovation, and ensure that teachers have access to the resources and support needed to apply new frameworks in their classrooms (Sun & Gao, 2019). Unless there is strong leadership in favor of the mission of equitable and inclusive pedagogy—e.g., transformational leadership (Çimen, 2023) and servant leadership (Sağlam & Alpaydın, 2017)—the opportunities for effectively applying theories like Gardner's MI, Sternberg's SI, and Dweck's ITI to educational settings are probably reduced. In this case, school administration is central to facilitating or hindering teachers' capacity to effectively apply intelligence theories, and hence it is an essential variable when examining these findings. 5.4. Educational Implications and Future Directions The results of the present study offer practical implications for the intelligence-friendly classroom framework, particularly in relation to teacher training and classroom practices. This discussion aims to bridge the gap between theory and practice by highlighting how the framework can be integrated into educational systems to foster environments that support diverse intelligences. Finally, possible directions for future research are discussed. Teacher training. The empirical data obtained in this study found gaps in teacher education programs regarding the application of certain intelligence theories (SI and FCI), particularly addressing the cognitive and developmental aspects. Therefore, integrating these theories into the curriculum could help address these gaps, enabling teachers to better understand and apply a broader range of intelligence frameworks. However, exposure to intelligence 121 theories in current teacher programs in BC, like UBC's EPSE 308 course with limited coverage, appears insufficient. The study has shown that educators have a highly positive attitude toward building intelligence-friendly classrooms by demonstrating their belief in the multi-dimensional nature of intelligence, its development, and the importance of fostering environments that nurture diverse intelligences. This indicates that integrating intelligence theories in the framework into teacher programs through policies regarding teacher training and certification will likely be well-received. Moreover, given ongoing educational reforms in BC, such as curriculum re-design (British Columbia Ministry of Education, 2015), a key challenge is how to better prepare educators to meet the diverse needs of all students in inclusive classrooms (Florian & Camedda, 2020; Gacoin, 2020; Hill et al., 2020; McCrimmon, 2015). Equipping pre-service teachers with a deeper understanding of intelligence-friendly practices through various intelligence theories can serve as a means that could address this gap. This exposure can empower them to transform traditional classrooms into talent-nurturing environments (Lo et al., 2019), which aligns with BC's educational goals (Milford et al., 2022). Moreover, having exposure to intelligence theories may also contribute to teachers' self-knowledge and personal development processes (Liesveld et al., 2005; Saglam et al., 2023). Through coursework and professional development opportunities related to intelligence theories, teachers can gain a grounded understanding of their own strengths profile. This self-knowledge, then contribute to their personal funds of knowledge. Additionally, this exposure can also influence teachers' interactions with students. A teacher who recognizes the concept of multiple intelligences might focus on discovering and nurturing students' strengths, and moving away from solely focusing on recognizing amending students’ weaknesses. This shift in focus can 122 foster a more positive learning environment where students feel valued and supported (Guardino & Fullerton, 2010). As a result, students might develop important cognitive and emotional structures like self-confidence, motivation, self-awareness, and well-being, as showed in the literature (Galloway et al., 2020; Mason, 2019; Passarelli et al., 2010). Classroom practices. The current study revealed a gap between educators' perceived knowledge and positive attitudes towards the intelligence-friendly classroom framework, and their perceived ability to translate this knowledge into classroom practices. While educators in the study expressed enthusiasm for creating intelligence-friendly classrooms, their perceived ability to translate that enthusiasm into practice were limited. Here are some strategies to bridge this gap and foster the practical application of the intelligence-friendly classroom framework. Firstly, teachers can address the four aspects of intelligence, via FCI, MI, SI, and ITI theories, through providing maximal, diverse opportunities for student engagement, allowing students to express themselves, and using flexible and varied contexts (Hodapp et al., 2011). For example, through MI theory of Gardner (2004), teachers can provide multiple opportunities for students' engagement, and allow them to express themselves by using at least nine intelligence areas (Bruer, 1994; Krechevsky & Seidel, 1998; Rumfot et al., 2024). Another strategy that can be used to address multiple intelligence affordances is to use the framework of Cattel’s FCI theory (1963). For a student fascinated with architecture, a teacher can provide this student opportunities and tasks that initially served as indicators of fluid intelligence which then begin to crystallise as he/she spends time practising visual-spatial problems (Rose & Fischer, 2011). This multidimensional intelligence perspective in the framework may provide a valuable lens for differentiated (i.e., individualized) instruction (Gardner et al., 2018; Kaufman et al., 2013; Reis et al., 2011). By understanding students' diverse intelligences, teachers can design lessons that 123 cater to multiple strengths, interests, talents, and learning preferences. This might involve offering a variety of learning activities (e.g., kinesthetic activities, visual representations, open-ended projects, etc.) to allow students to demonstrate their understanding in ways that align with their dominant intelligences. Moreover, project-based learning allows students to integrate various intelligences while tackling real-world problems (Kokotsaki et al., 2016; Sternberg, 2011). For example, a science project might involve research (linguistic intelligence, analytic intelligence, and fluid intelligence), data analysis (logical-mathematical intelligence), and building a model (spatial intelligence, kinesthetic intelligence, and crystallized intelligence). Another example is that a teacher might design a project-based assignment that requires students to analyze a community issue (analytical), propose innovative solutions (creative), and develop a feasible implementation plan (practical), fostering a holistic approach to cognitive intelligence development (Sternberg, 1985). This approach encourages students to apply their knowledge and skills in a meaningful way, fostering deeper learning and engagement (Sternberg et al., 2021). Furthermore, assessing how students understand concepts taught in classrooms allows teachers to identify areas where students excel and areas that require additional support. This information can be used to adjust instruction and provide targeted interventions that build upon students' existing strengths, as identified through various intelligence theories. Teachers can also promote self-knowledge among students by offering opportunities for them to become aware of their own developing and potentially improvable abilities, capacities, skills, and strengths (Fogarty, 2008; Lo, 2014). Using life models (Betts et al., 2016) and promoting detailed case studies of autobiographies and biographies of eminent people with similar interest areas (Lo et 124 al., 2019) help students to understand and use metacognitive tools that can aid in monitoring their own development during their actualizing process. Future directions. Following further refinement and validation, the data collection survey employed in the study could be applied to different groups working in different environments such as universities and colleges, because this research primarily focused on K-12 educators. Moreover, our data were obtained from a single application process. Therefore, it is possible to discover new findings by using a wide range of research methodologies in subsequent studies, including qualitative methods to gain in-depth understandings. Researchers can also explore the applicability of this survey instrument in different cultural contexts by tailoring it to specific populations and settings, while also potentially incorporating other relevant theories beyond the four theories discussed herein. In addition, the Intelligence-Friendly Classroom (IFC) framework presented in this study is intended as an initial conceptual step for an intelligence-informed competency framework, rather than a fully developed or empirically validated model. While each theory within the framework has its own empirical foundation, their application under the IFC framework to guide educational practice is novel and requires further empirical investigation. Rather than being a fully tested intervention model, the IFC framework should be seen as a guiding structure for educators in recognizing, valuing, and cultivating students' potential. The interactions among its components (i.e., aspects) and how well they address students' needs and strengths still require careful investigation. This study represents an initial effort with the intention of informing future empirical research and refinement regarding the IFC framework. Furthermore, while this dissertation focuses specifically on self-efficacy, agency —defined as the broader capacity to make independent choices within sociocultural and 125 institutional contexts (Campbell, 2012)— remains a valuable conceptual lens. Therefore, teacher agency may serve as a longer-term research focus, although it is not empirically demonstrated in this study. Future research might explore how educators navigate institutional constraints, policy mandates, and school cultures to enact intelligence-friendly pedagogies. Such inquiry could benefit from qualitative or mixed-method approaches capable of capturing the relational and structural dimensions of agency in context. 5.5. Strengths and Limitations In this study, we conducted an investigation into educators' self-perceived knowledge, skills, attitudes, and efficacy regarding practicing intelligence-related theories in classrooms, with a focus on addressing the developmental, sociocultural, cognitive, and motivational aspects of intelligence development through the four theories. The strengths and limitations of the research are listed, respectively, as follows. Firstly, this research’s primary strength lies in its originality. By surveying teachers’ efficacy in applying FCI, MI, SI, and ITI theories, this study offers an initial empirical exploration of the concept of intelligence-friendly classrooms, conceived as a synthesis of these theoretical approaches. Second, its exploration of the intelligence-friendly classroom framework, through the integration of these four theories, provides an opportunity to bridge theoretical concepts with practical applications in strengths-based education. Its third strength refers to the methodological robustness, particularly the survey design procedure. This included expert reviews for content validity, a pilot study for refinement, and the use of appropriate sampling strategies and statistical analyses to ensure reliability of the findings. However, the non-random sampling method (i.e., convenience sampling) in this study limits the generalizability of the results to the broader population. 126 Fourthly, the study contributes to the discourse on inclusive education by exploring teachers’ perceptions of constructing intelligence-friendly classrooms that speaks strongly to strengths-based education. The developmental aspect of the framework helps identify and nurture students' evolving capacities, the sociocultural aspect promotes inclusivity by valuing diverse cultural and social backgrounds, the cognitive aspect fosters problem-solving and critical thinking, and the motivational aspect encourages a growth mindset to unlock students’ full potential. All these aspects work together to create intelligence-friendly classrooms that align with strengths-based education principles. However, it is important to note that the intelligence-friendly classroom framework proposed in this study is not confined to these four theories informing four aspects of intelligence, as it is an organic and evolving framework. For example, emotional intelligence theory could contribute to the affective aspect of intelligence development, while neuroscience theories can inform the biological aspect. Although there are several strengths to our study, the results should be considered in light of its limitations. The analysis of subgroups among demographic variables for our sample may have introduced potential biases, possibly leading to false positives or negatives because of the limited number of research participants. Additionally, while the study investigates a broad spectrum of educators' knowledge, skills, attitudes, and self-efficacy, in relation to the four theories, the sample size may have limited the depth of analysis for each variable and the ability to explore all potential relationships between them. Furthermore, one limitation of this study is the use of mean values in non-normal data distributions. Given that some variables in the dataset exhibited significant skewness (for example, in the knowledge of mindset theory), the mean might not always accurately represent the central tendency of the data. To address this issue, we also reported the median alongside the mean, as the median is less affected by skewness and 127 provides a more accurate measure of central tendency in skewed distributions (Norman, 2010). However, despite this dual presentation, the reliance on the mean in non-normal contexts should be interpreted with caution, as it may not fully capture the underlying distributional characteristics. Future research can consider the use of additional robust statistical measures to further mitigate the impact of non-normality. In addition, this sample size (or participation rate) might not fully capture the diversity of educators and contexts within British Columbia, Ontario, and Alberta. Most participants were from British Columbia, and to the best of our knowledge, no rural sample participated. Therefore, study findings can be considered as primarily urban-based as rural conditions may be different, which could affect the generalizability of the results. Further, it's important to acknowledge that the inclusion of participants from multiple provinces may limit the depth of analysis specific to BC. Future research focused exclusively on BC, ON, or AB could provide a more in-depth understanding of the unique challenges and opportunities within its educational context. Moreover, one limitation of the study is that it used self-reported data. This means participants' recent interactions with similar terms and concepts might influence their responses, leading to potential inaccuracies in their reported knowledge. Additionally, the limited duration and the survey's coverage of multiple theories may have restricted the exploration of nuanced differences in educators' knowledge of the four theories and potentially overlooked specific strengths and weaknesses. Further, using a theory-based approach may have caused confusion among participants, particularly in the assessment of the skills section. Many educators may already be applying existing intelligence theories as tacit knowledge in their classrooms to develop their students' potential. However, explicitly naming the intelligence theories in the 128 survey questions may have led to lower measured skill levels. Since the province of British Columbia has already integrated a strengths-based approach into its education system (British Columbia Ministry of Education, 2015; Gacoin, 2020), teachers may be unknowingly implementing the strategies of the relevant theories. Another limitation concerns the use of a novel survey instrument specifically designed for this study. While employing a new survey offers the potential to uncover fresh insights into the topic, its overall validity requires further exploration. Future studies can conduct factor analysis to test the construct validity and examine the relationships between self-reported responses and classroom observations or other objective measures to establish criterion-related validity. In addition, even if the survey is anonymous, there is still a risk that the respondents consciously or unconsciously give the answers that seem more socially acceptable. In summary, our research can be seen as a substantial first step in understanding teachers perceived efficacy and self-efficacy in constructing intelligence-friendly classroom. As a result, our research opens new avenues for further inquiries and studies on addressing various aspects of intelligence development through the integration of diverse intelligence theories in the classroom, in order to promote school environments conducive for inducing gifts and intelligences among all students. 5.6. Conclusion The purpose of the present dissertation was to understand the possibility of applying the Intelligence-Friendly Classroom (IFC) framework, which can lead to fortify the strengths-based approach focused in our current educational environment. Employed a quantitative research methodology, this study investigated educators' self-perceived knowledge, skills, attitudes, and efficacy towards constructing intelligence-friendly classrooms by focusing on the cognitive, 129 sociocultural, developmental, and motivational aspects of intelligence through the integration of the four theories: Sternberg’s Successful Intelligence (SI) theory, Gardner’s Multiple Intelligences (MI) theory, Cattell’s Fluid and Crystallized Intelligence (FCI) theory, and Dweck’s Implicit Theories of Intelligence (ITI). The findings reveal that educators are more familiar with the motivational aspect of intelligence through Dweck's mindset theory and the sociocultural aspect through Gardner's MI theory. In contrast, there is a significant gap in educators' knowledge and application of the developmental and cognitive aspects of intelligence, as outlined by Cattell's FCI theory and Sternberg's SI theory, respectively. Despite these gaps, the overwhelmingly positive attitudes of participants towards constructing intelligence-friendly classrooms reflect a strong belief in the importance of creating learning environments that nurture students' diverse strengths and talent. This study also highlights the importance of integrating FCI, MI, SI, and ITI theories into educational policies and practices. Such integration may promote a more inclusive and effective learning environment, supporting the diverse intelligences and strengths of every student. By offering the Intelligence-Friendly Classrooms (IFC) framework to in-service and pre-service teachers, this study contributes to possible directions for creating equitable, fair, effective, and enriched education for all students. 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This survey was prepared to determine “Educators’ Knowledge, Skills, Attitudes, and Self-Efficacy Towards Applying Multiple Dimensional Intelligence Theories in Classrooms.” It contains 4 sections, which are knowledge (12 items), skills (12 items), attitudes (11 items), & self-efficacy (7 items) of educators on multiple dimensional intelligence theories. We need your expert judgement on the degree of relevant of each item to the measured domains. Therefore, you are requested to evaluate each item on the survey by selecting (1) “essential”, (2) “useful, but not essential” and (3) “not necessary” in order to ascertain their alignment with the survey's objectives. Your valuable insights, including comments for potential corrections or suggestions, would be greatly appreciated. Thank you in advance for your valuable contributions. Best regards, Mehmet Hilmi Saglam Mehmet Hilmi Saglam, PhD Candidate (He, Him, His) PhD Candidate | Graduate Research Assistant | GATE Lab Manager High Ability Studies Faculty of Education | Department of Educational and Counseling Psychology, and Special Education (ECPS) The University of British Columbia | Vancouver Campus | 2125 Main Mall | Vancouver BC | V6T 1Z4 Canada Phone: 604 309 6025 EMAIL@ubc.ca 175 Dimension Items 1 2 3 Reviews Knowledge: I am familiar that… 1. There are 2 types of intelligences (i.e., fluid & crystallized) in Cattell’s theory. 2. Crystallized intelligence increases through experiences. 3. Fluid intelligence is used during learning something new. 4. There are 9 different intelligences in Gardner's theory of multiple intelligences. 5. Everyone has their unique combination of the 9 intelligences. 6. There are multiple ways for students to learn according to their strengths. 7. There are 3 types of intelligences (i.e., analytical, creative, & practical) in Stenberg’s theory of successful intelligence. 8. Successful intelligence is the ability of adapting to, shaping, and selecting environments. 9. Sternberg proposed that success is achieved through a balance of analytical, creative, and practical abilities. 10. There are 2 mindsets about intelligence (e.g., growth mindset & fixed mindset) in Dweck’s implicit theories of intelligence. 11. Mindset of students towards their own intelligence shapes their own learning engagement. 12. Mindset of educators (e.g., teachers) towards students’ intelligence shapes students’ learning engagement. Dimension Items 1 2 3 Reviews Skills: I am able to… 1. I am able to explain Cattell's theory of fluid & crystallized intelligence to students. 2. Based on Cattell's theory of intelligence, I am able to design activities to enhance students' intelligences in the following areas: X X X a. Fluid intelligence b. Crystallized intelligence 3. I am able to design assessment that cater towards various types of intelligences (i.e., fluid & crystallized intelligence). Fluid & Crystallized Intelligence Theory Gardner Multiple Intelligence Theory Sternberg Successful Intelligence Theory Fluid & Crystallized Intelligence Theory Dweck implicit theories of intelligence 176 4. I am able to explain Gardner's 9 types of multiple intelligences theory to students. 5. Based on Gardner's theory of intelligence, I am able to design activities to enhance students' intelligences in the following areas: X X X a) Linguistic b) Logical-Mathematical c) Visual-Spatial d) Bodily-Kinesthetic e) Musical f) Interpersonal g) Intrapersonal h) Naturalist i) Existential 6. I am able to design assessment that cater towards multiple intelligence. 7. I am able to explain Stenberg’s theory of successful intelligence theory to students. 8. Based on Stenberg’s theory of successful intelligence, I am able to design activities to enhance students' intelligences in the following areas: X X X a) Analytical Intelligence b) Creative Intelligence c) Practical Intelligence 9. I am able to design assessment that cater towards successful intelligence. 10. I am able to explain growth & fixed mindsets in Dweck’s implicit theories of intelligence to students. 11. I able to design activities that help students reflect their own growth & fixed mindsets. 12. I am able to design assessment that promotes growth mindset. Dimension Items 1 2 3 Reviews Attitudes: I believe that… 1. Intelligence is multiple dimensional construct rather than single dimensional construct. 2. Intelligence can be enhanced by new experiences through the life. Gardner Multiple Intelligence Theory Sternberg Successful Intelligence Theory Dweck implicit theories of intelligence 177 3. Every student possesses, more or less, each type of intelligence indicated in the survey. 4. Intelligences can be enhanced through enriched environment. 5. It is important to both maximise students’ strengths and compensate their weaknesses for their intelligence development. 6. A topic can be taught in a variety of appropriate ways rather than one way. 7. A topic can be assessed in a variety of appropriate ways rather than one way. 8. Explaining intelligence theories to students can help them understand their potentials. 9. Classroom should provide every student opportunities to recognize their intelligences. 10. Classroom should provide every student opportunities to cultivate their intelligences. 11. Classroom should provide every student opportunities to celebrate their intelligences. Dimension Items 1 2 3 Reviews Self-efficacy: 1. I actively seek professional development opportunities (e.g., workshop and consultation) to enhance my knowledge and skills in intelligence theories. 2. I actively seek advice from teachers who have experiences in enhancing various types of intelligences in the classroom. 3. I look for various instructional materials to help students develop their intelligences. 4. I effectively collaborate with administrative levels at school to support and develop students’ intelligences. 5. I work collaboratively with other teachers to develop students’ intelligences. 6. I work effectively with parents to help students develop their intelligences. 7. I work effectively with the communities to help students develop their intelligences. Other Comments (flaw, language & survey design): ________________________________ _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ 178 Appendix B: Pilot Study Recruitment Email Subject: "Invitation to Participate in Pilot Study on Educators' Perspectives" Hello everyone, I hope this email finds you well. As a doctoral researcher at UBC, I have been working on an exciting research study with Dr. Owen Lo. This is a standalone study, central to my thesis project, and I would like to invite you to participate in the pilot phase of the study. Our research focuses on examining educators' knowledge, skills, attitudes, and self-efficacy in applying multi-dimensional intelligence theories within the classroom. By understanding your perspectives, we aim to contribute to a more comprehensive understanding of each student's intelligence and potential. If you have any teaching experience in Canada, you are eligible to participate in the pilot study. Participation is simple and involves completing an anonymous survey with 42 questions related to your experiences and perspectives as a teacher. The survey should take approximately 10-15 minutes. Your participation is entirely voluntary, and your responses will remain confidential. To express our gratitude for your involvement, we're offering you $15 Amazon gift cards upon completing the survey. If you're interested in participating or have any questions, you can contact me at EMAIL@ubc.ca. Your time and insights are highly valued. Your contribution to this research will significantly advance our understanding of effective teaching practices and the application of intelligence theories in the classroom. Sincerely, Mehmet Saglam Mehmet Hilmi Saglam, PhD Candidate (He, Him, His) Graduate Research Assistant | GATE Lab Manager High Ability Studies Faculty of Education | Department of Educational and Counseling Psychology, and Special Education (ECPS) The University of British Columbia | Vancouver Campus | 2125 Main Mall | Vancouver BC | V6T 1Z4 Canada Phone: 604 309 6025 EMAIL@ubc.ca 179 Appendix C: Pilot Study Instruction Sheet Pilot Study Instruction Sheet Thanks for being interested in our study! We're excited to have you on board. Below, you'll find all the info you need to finish the survey. Please read it carefully and look out for anything underlined that needs your attention. Study Introduction: This project aims to investigate the examining educators' perspectives in applying multi-dimensional intelligence theories within the classroom environment. Through examining teacher perspectives, we anticipate that this research will contribute to strengths-based education and enhanced comprehension of each student's unique intelligences and potential. What will you do? 1. First, we'll share a link for you to take the survey, which includes five short sections. After each section, there's a text box where you can share your thoughts on items, grammar, language, and the section as a whole. 2. As you go through the survey, please carefully review each item and the overall section. Use the text box below each section to provide your feedback. 3. Before you begin the survey, take a look at the "what we are looking for in the survey questionnaire" section below. This will help you understand the kind of feedback we're seeking and what we aim to assess. 4. While filling out the survey questionnaire, please evaluate the survey questionnaire based on the questions in the “what we are looking for in the survey questionnaire” section. Please provide specific item numbers while you are writing your feedback if it’s specific to an item. 5. Once you finish the survey questionnaire, the final step includes a last question seeking your overall feedback on the survey. Please provide as much feedback as possible, including any recommendations you may have. What are we looking for in the survey questionnaire? • If there is some confusing wording • If some items feel repetitive • If the total number of questions is too many (if the survey is too large and if you lost interest while filling the questionnaire out) • If some items are not clearly stated • If some items have too many multiple options • If some multiple-choice options in an item are not clearly stated • If there is something you would like to add to the multiple-choice options of a question • If the organization of the questionnaire makes sense to you • If you feel like your privacy is protected and respected What will you need? 1. A laptop or computer with an Internet connection. 2. A space where you will not be disturbed for the duration of the survey (around 15 minutes). What will you get in return? A $15 Amazon gift card will be given as a token of our gratitude for your contribution to our study if you complete all the required steps for the study. 180 Important Notes 1. If you are interested and would like to contribute, please make sure that you can devote all your attention to the survey while working on it. (By reading the statements carefully, not skipping any questions, etc.) 2. There will be a deadline for you to finish and submit your feedback on the survey. You will be expected to finish the survey by the end of the day on Thursday, Nov 30th at 11:59 pm. We want to hear your feedback on improving our survey questionnaire, so all your feedback is very precious for this project. Best, Co-investigator of the study 181 Appendix D: Participant Recruitment Flyer 182 Appendix E: Consent Form and Survey Questionnaire Survey Questionnaire Principal Investigator: Dr. C. Owen Lo, Department of Educational & Counselling Psychology, and Special Education, University of British Columbia Ph: 604 309 6025 | Email: 604 309 6025 Co-Investigator: Mehmet Saglam, Department of Educational & Counselling Psychology, and Special Education, University of British Columbia Ph: 604 309 6025 | Email: 604 309 6025 Purpose: The purpose of this study is to investigate teachers’ and educators’ knowledge, skills, attitudes, and self-efficacy towards applying multi-dimensional intelligence theories in classrooms in Canada. Through examining teachers’ and educators’ perspectives, we anticipate that this research will contribute to strengths-based education and an enhanced comprehension of each student's unique intelligences and potential. It will also help us foster ways in which teachers and educators can construct intelligence-friendly learning environments and encourage student engagement in learning. The study is part of the thesis requirements for the Doctoral Dissertation in Special Education for the Co-Investigator, Mr. Saglam. Study Procedures: To participate in this study, you will be requested to complete an anonymous survey regarding intelligence theories. The survey questionnaire consists of 42 questions and takes about 10 – 15 minutes to complete. No further participation will be required upon completion of the survey. Project Outcomes: The findings of this study will be reported in a PhD dissertation and may also be published in journal articles and books after the completion of this study. Potential Benefits & Risks: Your participation will make a valuable contribution to the body of scientific knowledge in the field of educational psychology. After finishing the survey, you will have the opportunity to enter a draw to win one of five $100 gift cards by submitting your contact information through a separate survey, to which you'll be automatically redirected. All participants shared their email, even those who withdraw their survey, are eligible for the draw. There are no physical or psychological risks associated with participating in the survey. Confidentiality: Access to the information collected during this study will be restricted to the Principal Investigator and the Co-Investigator of the project. The data will be gathered using the secure UBC Survey Tool provided by Qualtrics, ensuring that the survey data is stored and backed up within Canada. The collected data will be stored, analyzed, and reported in an anonymized format. On the other hand, the contact information for the draw will be collected separately from the survey and will be password protected and encrypted. How can I withdraw from this study? 183 Participation in this study is entirely voluntary. You can choose not to take part or withdraw at any time without providing a specific reason. However, please note that once you submit your responses in this anonymous survey, data withdrawal will not be possible. Contact for information about the study: If you have any further questions or would like to request more information about the study, please contact the Co-investigator, Mehmet Saglam (EMAIL@ubc.ca). Who can you contact if you have concerns or complaints about the study? If you have any concerns or complaints about your rights as a research participant and/or your experience while participating in this study, contact the Research Participant Complaint Line in the UBC Office of Research Ethics at 604-822-8598 or if long distance, email RSIL@ors.ubc.ca or call toll free 1-877-822-8598. Please reference our application identification number: H23-03462 Consent: By completing and submitting this survey, you are agreeing to participate in this study. Thank you very much for your help with this project. I am willing to participate in this research project. (Please click next) 184 Survey About Teachers’ and Educators’ Knowledge, Skills, Attitudes, and Self-Efficacy Towards Applying Multiple Dimensional Intelligence Theories in Classrooms Please rate the following statements from strongly disagree to strongly agree (1-Strongly Disagree, 2-Disagree, 3-Neither Agree or Disagree, 4-Agree, and 5-Strongly Agree). Knowledge: I am aware that… 1. There are two types of intelligences (fluid & crystallized) in Cattell’s theory. 2. Crystallized intelligence increases through experiences. 3. Fluid intelligence is used during learning something new. 4. There are nine different intelligences in Gardner's theory of multiple intelligences. 5. Everyone has their unique combination of the nine intelligences. 6. There are multiple ways for students to learn according to their strengths. 7. There are three types of intelligences (analytical, creative, & practical) in Stenberg’s theory of successful intelligence. 8. Successful intelligence is the ability of adapting to, shaping, and selecting environments. 9. Sternberg proposed that success is achieved through a balance of analytical, creative, and practical abilities. 10. There are two mindsets about intelligence (growth mindset & fixed mindset) in Dweck’s implicit theories of intelligence. 11. Mindset of students towards their own intelligence shapes their own learning engagement. 12. Mindset of teachers/educators towards students’ intelligence shapes students’ learning engagement. Skills: I am able to… 1- I am able to explain Cattell's theory of fluid & crystallized intelligence to students. 2- Based on Cattell's theory of intelligence, I am able to design activities to enhance students' intelligences in the following areas: a. Fluid intelligence b. Crystallized intelligence 3- I am able to design assessments that cater towards various types of intelligences (fluid & crystallized intelligence). 4- I am able to explain Gardner's nine types of multiple intelligences theory to students. 5- Based on Gardner's theory of intelligence, I am able to design activities to enhance students' intelligences in the following areas: a. Linguistic b. Logical-Mathematical c. Visual-Spatial d. Bodily-Kinesthetic e. Musical f. Interpersonal g. Intrapersonal h. Naturalist 185 i. Existential 6- I am able to design assessments that cater towards multiple intelligence. 7- I am able to explain Stenberg’s theory of successful intelligence theory to students. 8- Based on Stenberg’s theory of successful intelligence, I am able to design activities to enhance students' intelligences in the following areas: a. Analytical Intelligence b. Creative Intelligence c. Practical Intelligence 9- I am able to design assessments that cater towards successful intelligence. 10- I am able to explain growth & fixed mindsets in Dweck’s implicit theories of intelligence to students. 11- I able to design activities that help students reflect their own growth & fixed mindsets. 12- I am able to design assessment that promotes growth mindset. Attitudes: I believe that… 1- Intelligence is multi-dimensional construct rather than a single dimensional construct. 2- Intelligence can be enhanced by new experiences through the life. 3- Every student has, more or less, each type of intelligence indicated in the survey. 4- Intelligence can be enhanced through enriched environment. 5- It is important to both maximize students’ strengths and compensate their weaknesses for their intelligence development. 6- A topic can be taught in various appropriate ways rather than one way. 7- A topic can be assessed in various appropriate ways rather than one way. 8- Explaining intelligence theories to students can help them understand their potentials. 9- The classroom should provide every student with opportunities to recognize their intelligences. 10- The classroom should provide every student with opportunities to cultivate their intelligence. 11- The classroom should provide every student with opportunities to celebrate their intelligences. Self-Efficacy: 1- I actively seek professional development opportunities (e.g., workshop and consultation) to enhance my knowledge and skills in intelligence theories. 2- I actively seek advice from teachers/educators who have experiences in enhancing various types of intelligences in the classroom. 3- I look for various instructional materials to help students develop their intelligences. 4- I effectively collaborate with administrative levels at school to support and develop students’ intelligences. 5- I work collaboratively with other teachers/educators to develop students’ intelligences. 6- I work effectively with parents to help students develop their intelligences. 7- I work effectively with the communities to help students develop their intelligences. 186 Demographics 1) What gender do you identify as? o Male o Female o Non-binary person o Prefer not to answer 2) What year were you born in? ________________ Prefer not to answer 3) What is your first language? ________________ Prefer not to answer 4) In what province or territory do you currently live? o Alberta o British Columbia o Manitoba o New Brunswick o Newfoundland and Labrador o Northwest Territories o Nova Scotia o Nunavut o Ontario o Prince Edward Island o Quebec o Saskatchewan o Yukon o Not in Canada 5) Although we all live in Canada, people sometimes identify themselves by the ethnic, or cultural group to which their parents, grandparents or ancestors belong. How do you identify your ethnic background? o Indigenous origins (for example, First Nations, Inuit, Métis); o African o Asian (China, Mongolia, North Korea, South Korea, Japan, Hong Kong, Taiwan, etc.) o South Asian (Afghanistan, Bangladesh, India, Nepal, Pakistan, Sri Lanka, etc.) o South East Asian (Brunei, Burma (Myanmar), Indonesia, Malaysia, Singapore, Thailand, Vietnam, etc.) o European o Hispanic or Latina/Latino (Latin, Central and South American) o Middle Eastern (Arabic, Iranian, Kuwaiti, Persian, Israeli, etc.) o Other (tell us) ________________ o I don’t know o I prefer not to answer 6) What is your highest level of education? o PhD/EdD 187 o Masters o Bachelors o Associates Degree (diploma) o Other __________________________________ 7) Please specify your graduate student status (i.e., MA/MEd PhD/EdD). o I am a Graduate Student with Canadian teaching experience o I am a Graduate Student with International teaching experience o I am a Graduate Student without teaching experience o I am not a Graduate Student 8) Which of the following best describes your current status? o Teacher / In-service Teacher o Teacher Candidate / preservice Teacher o Not applicable o Other (please specify): _______________ 9) How many years of teaching experience do you have? o 0-2 years o 3-5 years o 6-10 years o +10 years 10) What was your major during your undergraduate studies? ______ Prefer not to answer 11) How many courses during your bachelor's degree program did you expose with multiple dimensional intelligence theories? o Never o 1 course o 2 courses o 3 courses o 4 and more courses o Not applicable 12) How many professional development sessions related to multiple dimensional intelligence theories have you experienced throughout your teaching career? o Never o 1 session o 2 sessions o 3 sessions o 4 and more sessions o Not applicable 188 Appendix F: Initial Contact Email Subject: "Invitation to Participate in a Survey Study on the Implementation of Intelligence Theories in Classrooms" Dear Teachers and Educators, We are writing to let you know about an opportunity to participate in a research study on the examination of teachers' and educators’ knowledge, skills, attitudes, and self-efficacy in applying multi-dimensional intelligence theories in the classroom. This study is being conducted by graduate student researcher Mehmet Saglam at the Faculty of Education of the University of British Columbia under the guidance of Dr. C. Owen Lo, the Principal Investigator of the project. The focus of this research revolves around examining teachers' and educators’ perspectives in applying multiple dimensional intelligence theories within the classroom environment. Through examining teachers’ and educators’ perspectives, we anticipate that this research will contribute to strengths-based education and enhanced comprehension of each student's unique intelligences and potential. Your valuable insights as an experienced educator can help the success of this study. Participation in this research involves completing an anonymous survey that consists of 42 questions related to your experiences and perspectives as a teacher or an educator. The survey is designed to be straightforward and should take approximately 10-15 minutes to complete. Your participation is entirely voluntary, and your responses will be kept confidential. As a token of our appreciation for your participation, you will have the opportunity to enter a draw for the chance to win one of five $100 gift cards. If you are interested in taking part in this study, you have two options for accessing the survey: you can click on the provided link [https://ubc.ca1.qualtrics.com/jfe/form/SV_bDEpml4keKrGPL8], or you can refer to the attached recruitment advertisement with a QR code. If you have any questions or require further information about the research, please do not hesitate to contact Mehmet Saglam at EMAIL@ubc.ca. We appreciate your time and help. Your contribution to this research will significantly advance our understanding of effective teaching practices and the application of intelligence theories in the classroom. Sincerely, Mehmet Hilmi Saglam, PhD Candidate (He, Him, His) Graduate Research Assistant | GATE Lab Manager High Ability Studies Faculty of Education | Department of Educational and Counseling Psychology, and Special Education (ECPS) The University of British Columbia | Vancouver Campus | 2125 Main Mall | Vancouver BC | V6T 1Z4 Canada EMAIL@ubc.ca
Thesis/Dissertation
eng
Special Education
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Lo, C. Owen
Towards an intelligence-informed competency framework : examining educators’ knowledge, skills, attitudes, and self-efficacy in inclusive classrooms
Text