Education, Faculty of
Education, School of (Okanagan)
DSpace
UBCO
Mercuri, Adaleena Johanna
Master of Arts - MA
University of British Columbia
The transition to university life can be challenging for students as they adapt to a new learning environment with potentially ineffective academic skills (De Clerq et al., 2018; Donche et al., 2010). Executive functioning skills (EFS), the higher order functions that are essential for complex behaviours and cognitive processes, play a critical role in students’ mental well-being and academic success within university life (Pendry et al., 2021). This quantitative study aims to explore university students’ self-reported EFS, investigating potential differences or correlations with variables such as age, sex assigned at birth, academic year of study, and academic performance. Using the Behavior Rating Inventory of Executive Function—Adult Version (BRIEF-A; Roth et al., 2005) questionnaire, this study assessed individual aspects of EFS in relation to demographic variables. Key findings included high deficits in the Task Monitor scale and Working Memory scale, and significant differences in the Emotional Control scale scores between assigned at birth males and females. Last, there were negative correlations found between deficits for Metacognitive Index (MI) items, including the Working Memory scale, Plan/Organize scale, and the Organization of Materials scale, and students’ final course grade percentage. Age, the Inhibit scale, the Shift scale, the Emotional Control scale, the Self-Monitor scale, the Initiate scale, the Task-Monitor scale, the Behavior Regulation Index (BRI) and the overall Global Executive Composite scale (GEC) did not correlate with any academic performance measures. No scales measured by the BRIEF-A questionnaire resulted in a correlation with overall GPA measures, including the 2021/2022 academic year GPA and the 2022 fall GPA. Future research in measuring EFS in university students could include focusing on metacognitive aspects in relation to academic performance. Should future research corroborate the present studies findings, employing and evaluating interventions or support systems targeting working memory enhancement and other crucial components like plan/organization and organization of materials may improve academic outcomes for university students (Minihan et al., 2021; Otero et al., 2014). Further investigation of the intersectionality of EFS and emotional control, particularly regarding sex assigned at birth differences, could inform tailored interventions to enhance well-being among university students (Esmaeilinasab et al., 2016).
INSIDE THE STUDENT MIND: AN EXPLORATION OF SELF-REPORTED EXECUTIVE FUNCTIONING SKILLS IN UNVERSITY STUDENTS by Adaleena Johanna Mercuri B.H.K., The University of British Columbia, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE COLLEGE OF GRADUATE STUDIES (Education) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2024 © Adaleena Johanna Mercuri, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: Inside the student mind: An exploration of self-reported executive functioning skills in university students submitted by Adaleena Johanna Mercuri in partial fulfillment of the requirements of the degree of Master of Arts. Dr. Peter Arthur, Okanagan School of Education Supervisor Dr. Robert Campbell, Okanagan School of Education Co-Supervisor Dr. Scott Douglas, Okanagan School of Education Supervisory Committee Member Dr. Zoë Soon, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Sally Stewart, School of Health and Exercise Sciences External Examiner iii Abstract The transition to university life can be challenging for students as they adapt to a new learning environment with potentially ineffective academic skills (De Clerq et al., 2018; Donche et al., 2010). Executive functioning skills (EFS), the higher order functions that are essential for complex behaviours and cognitive processes, play a critical role in students’ mental well-being and academic success within university life (Pendry et al., 2021). This quantitative study aims to explore university students’ self-reported EFS, investigating potential differences or correlations with variables such as age, sex assigned at birth, academic year of study, and academic performance. Using the Behavior Rating Inventory of Executive Function—Adult Version (BRIEF-A; Roth et al., 2005) questionnaire, this study assessed individual aspects of EFS in relation to demographic variables. Key findings included high deficits (above 25% of the sample population) in the Task Monitor scale and Working Memory scale, and significant differences in the Emotional Control scale scores between assigned at birth males and females. Last, there were negative correlations found between deficits for Metacognitive Index (MI) items, including the Working Memory scale, Plan/Organize scale, and the Organization of Materials scale, and students’ final course grade percentage. Age, the Inhibit scale, the Shift scale, the Emotional Control scale, the Self-Monitor scale, the Initiate scale, the Task-Monitor scale, the Behavior Regulation Index (BRI) and the overall Global Executive Composite scale (GEC) did not correlate with any academic performance measures. No scales measured by the iv BRIEF-A questionnaire resulted in a correlation with overall GPA measures, including the 2021/2022 academic year GPA and the 2022 fall GPA. Future research in measuring EFS in university students could include focusing on metacognitive aspects in relation to academic performance. Should future research corroborate the present studies findings, employing and evaluating interventions or support systems targeting working memory enhancement and other crucial components like plan/organization and organization of materials may improve academic outcomes for university students (Minihan et al., 2021; Otero et al., 2014). In addition, further investigation of the intersectionality of EFS and emotional control, particularly regarding sex assigned at birth differences, could inform tailored interventions to enhance well-being among university students (Esmaeilinasab et al., 2016). v Lay Summary This quantitative study explored potential differences and correlations between university students’ self-reported executive functioning skills (EFS), through the utilization of the Behavior Rating Inventory of Executive Function—Adult version (BRIEF-A) questionnaire, and factors such as age, sex assigned at birth, academic year of study and academic performance. This study seeks to capture the multifaceted aspects of EFS, with the goal to contribute to the discussion around variables influencing student success at the university level. vi Preface This thesis is an original and independent work of the author, Adaleena Mercuri. The research was conducted through the protocols of The University of British Columbia’s Okanagan Campus Behavioural Research Ethics Board (BREB) under the project title: “Exploring the Relationship Between Self-Reported Executive Functioning Skills and Academic Performance in First-year University Students,” and BREB identity: H22-00270. As per UBC’s BREB guidelines, the data collection was conducted by Adaleena Mercuri under the guidance of the Principal Investigator and the thesis committee. The committee for this project included: • Dr. Peter J. Arthur Graduate Supervisor and Principal Investigator Professor of Teaching Okanagan School of Education, UBC • Dr. Robert Campbell Associate Professor Okanagan School of Education, UBC • Dr. Scott Douglas Associate Professor Okanagan School of Education, UBC • Dr. Zoë Soon Associate Professor of Teaching Irving K. Barber Faculty of Science, UBC vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ............................................................................................................................................ vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................. x List of Figures ................................................................................................................................ xi Acknowledgements ...................................................................................................................... xii Dedications .................................................................................................................................. xiii Chapter 1: Introduction ................................................................................................................ 1 1.1 Introduction and Background ........................................................................................ 1 1.2 Purpose of Study ........................................................................................................... 4 1.3 Research Questions ....................................................................................................... 4 1.4 Summary of Relevant Literature ....................................................................................5 1.5 Overview of Research Methods .................................................................................... 7 1.6 Significance of Study .................................................................................................... 8 1.7 Organization of Thesis .................................................................................................. 9 Chapter 2: Literature Review .................................................................................................... 11 2.1 Chapter Overview ....................................................................................................... 11 2.2 Theoretical and Historical Perspectives of EFS ........................................................... 11 2.2.1 The BRI ......................................................................................................... 13 2.2.2 The MI .......................................................................................................... 14 2.3 Brain Development and EFS ....................................................................................... 14 2.3.1 Age and the Brain ......................................................................................... 14 2.3.2 Sex Assigned at Birth and the Brain............................................................. 17 2.4 How EFS Impacts University Students ........................................................................ 20 Chapter 3: Methodology .............................................................................................................. 23 3.1 Chapter Overview ....................................................................................................... 23 3.2 Research Design ........................................................................................................... 23 3.3 Participants ................................................................................................................... 25 viii 3.3.1 The Negativity Scale .................................................................................... 26 3.3.2 The Infrequency Scale .................................................................................. 26 3.3.3 The Inconsistency Scale ................................................................................ 27 3.4 Procedures ....................................................................................................................31 3.4.1 Ethical Considerations and Consent ............................................................. 32 3.5 Data Collection ............................................................................................................ 33 3.5.1 Demographic Information Collected ............................................................ 33 3.5.2 Instrument: The BRIEF-A Questionnaire .................................................... 33 3.6 Data Analysis .............................................................................................................. 35 3.6.1 Percentage of Survey Responses Flagged for Executive Deficits ................ 35 3.6.2 Mann-Whitney U Test .................................................................................. 36 3.6.3 Spearman’s Rank-Order Correlation ............................................................ 37 3.7 Limitations and Delimitations ...................................................................................... 38 3.8 Summary ..................................................................................................................... 41 Chapter 4: Results ......................................................................................................................... 42 4.1 Results Overview ........................................................................................................ 42 4.2 Descriptive Statistics .................................................................................................... 42 4.3 Percentage of Student’s Flagged for Executive Deficits ............................................. 44 4.3.1 BRIEF-A Questionnaire Scales .................................................................... 45 4.3.1.1 The Inhibit Scale ........................................................................... 45 4.3.1.2 The Shift Scale .............................................................................. 46 4.3.1.3 The Emotional Control Scale ........................................................ 47 4.3.1.4 The Self-Monitor Scale ................................................................. 48 4.3.1.5 The Initiate Scale ........................................................................... 49 4.3.1.6 The Working Memory Scale .......................................................... 50 4.3.1.7 The Plan/Organize Scale ............................................................... 51 4.3.1.8 The Task Monitor Scale ................................................................ 52 4.3.1.9 The Organization of Materials Scale ............................................. 53 4.3.2 BRIEF-A Indexes and GEC ..........................................................................54 4.3.2.1 The BRI .......................................................................................... 55 4.3.2.2 The MI ........................................................................................... 55 4.3.2.3 GEC Scores ................................................................................... 56 4.4 Differences Between EFS and Academic Performance .............................................. 57 4.4.1 Sex Assigned at Birth .................................................................................... 57 4.4.2 Academic Year of Study .............................................................................. 59 4.5 Relationships Between Age, EFS, and Academic Performance ................................. 61 4.6 Chapter Summary ........................................................................................................ 63 Chapter 5: Discussion ................................................................................................................. 65 5.1 Chapter Overview ....................................................................................................... 65 ix 5.2 MI Scales and Academic Performance ........................................................................ 65 5.2.1 Research Exploring Self-Reported EFS In Students .................................... 67 5.2.2 Potential Implications ................................................................................... 70 5.3 Sex Assigned at Birth and Emotional Control ............................................................ 72 5.3.1 Physiological Factors ................................................................................... 73 5.3.2 Psychosocial Factors .................................................................................... 76 5.3.3 Potential Implications ................................................................................... 79 5.4 Future Research and Practical Implementations.......................................................... 82 5.5 Limitations .................................................................................................................. 84 5.6 Conclusion ................................................................................................................... 86 References .................................................................................................................................... 89 x List of Tables Table 1 Participant Demographic Information from Each First-year Course .............................. 29 Table 2 Participant Demographic Information ..............................................................................44 Table 3 Mann-Whitney U Tests for Differences in EFS and Academic Performance Measures between Assigned Male and Assigned Female Groups ................................................................ 58 Table 4 Mean Rank Scores for EFS and Academic Performance Measures between Assigned Male and Assigned Female Groups .............................................................................................. 59 Table 5 Mann-Whitney U Tests for Differences in EFS and Academic Performance Measures Between First Year and Upper Year (Second, Third, Fourth and Fifth Year) Students ............... 60 Table 6 Spearman’s Rank-Order Correlation Tests Between Age, EFS Scores, and Academic Performance Measures .................................................................................................................. 62 xi List of Figures Figure 1 Participant’s Percentile Sum Scores for the Inhibit Clinical Scale Survey Items .......... 46 Figure 2 Participant’s Percentile Sum Scores for the Shift Clinical Scale Survey Items ............. 47 Figure 3 Participant’s Percentile Sum Scores for the Emotional Control Clinical Scale Survey Items ...............................................................................................................................................48 Figure 4 Participant’s Percentile Sum Scores for the Self-Monitor Clinical Scale Survey Items . ....................................................................................................................................................... 49 Figure 5 Participant’s Percentile Sum Scores for the Initiate Clinical Scale Survey Items ..........50 Figure 6 Participant’s Percentile Sum Scores for the Working Memory Clinical Scale Survey Items ...............................................................................................................................................51 Figure 7 Participant’s Percentile Sum Scores for the Plan/Organize Clinical Scale Survey Items . ....................................................................................................................................................... 52 Figure 8 Participant’s Percentile Sum Scores for the Task-Monitor Clinical Scale Survey Items . ....................................................................................................................................................... 53 Figure 9 Participant’s Percentile Sum Scores for the Organization of Materials Clinical Scale Survey Item ................................................................................................................................... 54 Figure 10 Participant’s Percentile Sum Scores for the BRI Survey Items ....................................55 Figure 11 Participant’s Percentile Sum Scores for the MI Survey Items ..................................... 56 Figure 12 Participant’s Percentile Overall Sum Scores for the GEC ........................................... 57 xii Acknowledgements I would like to express my deepest gratitude to my supervisor, Dr. Peter Arthur, whose unwavering support, guidance, and insights have been instrumental in my research journey from the timid beginnings of a prospective graduate student to this thesis defence. His expertise, patience, and dedication has been pivotal in shaping my educational pursuits and academic growth. I am very proud to have been one of his graduate students. I am also immensely thankful to my thesis committee members, Dr. Robert Campbell, Dr. Scott Douglas, and Dr. Zoë Soon, as well as the external examiner, Dr. Sally Stewart, for their invaluable time, constructive feedback, encouragement, and expertise that has truly enriched the quality of this work. Their contributions and commitment to excellence and scholarly mentorship has been significant in my learning and is something I do not take for granted. To the loved ones in my life, thank you for being patient and attentive as I went through this journey. Know that every conversation, every moment shared, has helped move the needle along to the final piece of writing you see today. xiii Dedications To my father, Fiori, whose light-hearted comments and unwavering pride in even my smallest achievements has been a constant source of comfort and support throughout this process. Our relationship means the world to me, thank you for always being my buddy. To my mother, Janice, who has been an educator for over half of her life, who has constantly filled conversations in our home about the importance of the whole student, the holistic well-being of the student—this educational journey of mine was inspired by you. Your tireless efforts and commitment to your work has not gone unnoticed, and is why many of your previous students say, “Mrs. Mercuri was tough, but she was my favourite teacher.” It is to you, I dedicate this thesis to. 1 Chapter 1 Introduction 1.1 Introduction and Background The transition into university life can be challenging for students, often accompanied by feelings of displacement, anxiety, and distress as they adjust to the demands of higher academia (De Clerq et al., 2018). Some students may feel ill-equipped with the necessary skills and habits to achieve academic success in this new environment. Oftentimes, students bring with them learning and studying patterns that worked well in their previous educational roles, only to discover that these strategies are less effective in university courses (Donche et al., 2010). As a result, students may experience perceived setbacks in higher education. Additionally, according to the Canadian University Survey Consortium 2022 Survey of First-year Students, the average age of first-year students is age 19.4, with majority being 18 and under (CUSC, 2022). This age range coincides with significant personal development, as students may be navigating newfound freedom, independence, and autonomy over their own decisions. As a result, students may experience substantial and ongoing adjustments, both physically and psychosocially, as they learn and maneuver university life. It has been well-documented that brain development, particularly the connections and core-areas of the prefrontal cortex (PFC), continues well into the mid-twenties (Arain et al., 2013; Johnson et al., 2009; Kolk & Rakic, 2022). The PFC is in the front part of the brain and is primarily responsible for higher cognitive functions. It acts as the control center for complex behaviours and helps students make reasoned and adaptive choices in different situations. These functions are known as executive functions or executive functioning skills (EFS). EFS encompasses a range of abilities related to behavioural regulation and metacognition, which have shown to be important for mental well-being and academic success (Pendry et al., 2021). These 2 skills are multidimensional and can include important abilities such as memory utilization, emotional control, and task monitoring (Funahashi & Andreau, 2013; Letzer, 2018). The PFC is the brain region most associated with EFS, but it does not execute these functions alone. The PFC itself is divided into many different divisions and areas, with each area being associated with different executive functions. For example, the dorsolateral prefrontal cortex is responsible for sorting and maintaining working memory representation, and control of inhibition is associated with the right inferior frontal gyrus (Cristofori et al., 2019). Additionally, the PFC collaborates with other cortical and subcortical areas within the grain, included but not limited to the hippocampus, basal nuclei, and the cingulate cortex multiple regions of the brain and does not operate in isolation to execute these skills (Gilbert & Burgess, 2008). Holistically, for the development and execution of theses cognitive processes, it is important to acknowledge that there is a complex interplay between different brain regions. Executive functions have shown to be crucial factors when assessing the effectiveness of strategies used by successful university students, including cognitive task analysis, time management, and integration of prior knowledge (Hattie, 2017). Given that many university students are in their twenties, their capacity to refine these EFS may still be developing and not fully utilized. Additionally, there is evidence indicating variations in the onset and pace of development in the PFC between assigned at birth males and assigned at birth females (Koolschijn et al., 2013; Shaw et al., 2020). For example, some early longitudinal studies report that assigned males are appromixately one year delayed in their development of the frontal lobe compared to assigned females, with assigned females typically peaking in gray matter volume, the neural tissue in the brain responsible for processing and integrating information, in the frontal lobe areas around age 11 and assigned males peaking on average around age 12 (Lenroot et al., 3 2007; Peper et al., 2020). Assigned males and females may also experience different psychosocial pressures throughout their lives, such as socialization and gender bias in teaching, affecting their behaviour in educational settings (Pirmohamed et al., 2017). Depending on cultural background, gender stereotypes and parental expectations may also impact how assigned males and assigned females pursue and approach their education (Kollmayer et al, 2018). Finally, educational experience may be a factor to consider when measuring EFS in students, as those with more experience in post-secondary institutions may be able to employ executive functions more readily than those who are just first entering university. As students progress through university, they develop habits and skills that shape their approach to learning, contributing to their own sense of identity and self-efficacy. While neuropsychological tests offer objective measures of EFS, they can only assess one subset of executive skills at a time and do not capture the subjective experiences of participants (Goldstein & Naglieri, 2014). Given the explorative nature of this area, a standardized questionnaire that assesses the broad range of cognitive processes encompassing EFS could be the first step in understanding what students believe they need support in. Future research could then focus on specific areas of EFS once multiple studies converge findings on specific areas of need, and can include incorporating neuropsychological tests and qualitative assessments, such as interviews, to triangulate data and findings surrounding subsets of executive function. As self-reported EFS measures provide a student’s perspective for such productive behaviours required for academic achievement, the exploration of how students view their own skills can inform university student services about other facets of student well-being and success. Understanding how students perceive their own skills can inform university student services about additional aspects of 4 student well-being and success. Targeting these identified deficits could then provide additional support to students throughout their post-secondary education journey. 1.2 Purpose of Study The purpose of this quantitative investigation is to understand how university students perceive and report their EFS. Specifically, the study seeks to understand if there are distinctions or correlations between self-reported EFS and other pertinent student variables, including age, sex assigned at birth, academic year of study, and academic performance. To assess this, the present study utilizes a standardized survey, the BRIEF-A, to capture multifaceted aspects of EFS. Then, there can be separate analyses of each individual EFS. The goal of this study is to evaluate the potential interplay and disparities among age, sex assigned at birth, academic year of study, EFS, and academic performance within the university student population. 1.3 Research Questions This study aims to address the following questions: 1. What percentage of students are considered flagged as having executive deficits in specific clinical scales, indexes, and overall GEC scores, when measured by the BRIEF-A questionnaire? 2. Are there significant differences between assigned males’ and assigned females’ self- reported executive function scales, indexes, overall GEC scores, and/or academic performance measures? 5 3. Are there significant differences between first-year students and upper-level students (including second-, third-, fourth- and fifth-year students) self-reported executive function scales, indexes, overall GEC scores and/or academic performance measures? 4. Are there any relationships between student’s self-reported executive function scores, age, and/or academic performance measures? 1.4 Summary of Relevant Literature The literature review section begins by introducing theoretical and historical perspectives of EFS, tracing its origins from seminal case studies like the 1848 case of Phineas Gage, to the modern conceptualizations pioneered by researchers such as Lev Vygotsky (1962), Karl Pribram (1973), and Alexander Luria (1980). It discusses the consensus among psychologists regarding the role of the PFC in orchestrating EFS. The review also touches upon Alexander Luria's neurodevelopmental model, emphasizing the interplay between biological maturation and socio- cultural influences in shaping cognitive abilities of students. Contemporary understandings of EFS as the cognitive processes involved in controlling and managing thoughts, actions, attention, and emotions is also discussed. The literature review outlines the two main components of EFS: the Behavioral Regulation Index (BRI) and the Metacognition Index (MI), as outlined by the BRIEF-A questionnaire. It describes the subskills under each component, providing details on inhibition, shifting, emotional control, self-monitoring, initiation of tasks, working memory, planning/organization, task monitoring, and organization of materials. These components serve as a framework for understanding the multifaceted nature of EFS and its relevance to various aspects of daily functioning and learning. 6 The review continues to explore the intersection of brain development and EFS, beginning with a focus on the impact of age on cognitive functions. It discusses the structural and functional changes in the brain during the transition from late adolescence to early adulthood, emphasizing the role of the PFC in supporting these complex cognitive tasks. Additionally, it discusses the influence of genetic predispositions, hormonal fluctuations, and environmental factors on brain development and EFS, highlighting the concept of neural plasticity and the potential for targeted interventions to enhance cognitive abilities. Next, the literature review examines the role of sex assigned at birth in shaping brain anatomy and cognitive functions, acknowledging the complexities and controversies surrounding this area of research. It reviews the influence of gonadal steroid hormones on brain development, highlighting the potential differences in brain structure and function between assigned at birth males and females. It explores the findings from human studies regarding sex differences in EFS, defining the nuanced patterns observed in decision-making and risk-taking behaviours between assigned sexes. The literature review underscores the significance of EFS in university students' academic success and well-being. There is a discussion surrounding the relationship between EFS and self-regulatory strategies, showcasing implications for academic procrastination, stress, and mental health outcomes. Overall, the literature provides an understanding of the theoretical underpinnings, developmental trajectories, and practical implications of EFS in the context of university students' academic experiences, laying the groundwork for the present study's investigation into self-reported EFS and its findings. 7 1.5 Overview of Research Methods The research methodology section will outline the methods utilized in this study, detailing the specific demographic variables, EFS, and academic performance measures collected to understand the potential relationships and differences within the student population. This section will explain how the study used a quantitative approach, aimed at assessing and exploring the state of executive skillsets in university students enrolled in first-year courses. This design was chosen as an appropriate approach for the research objectives posed, as it allowed for the collection of numerical data that could be statistically analyzed to uncover patterns, relationships, and potential trends within the dataset. The research design used was a cross- sectional survey research design which enabled a snapshot of the student population's current self-perceived behaviours regarding their EFS to be captured. The methodology section describes how this research design offers valuable insights into the contemporary landscape of executive functioning among university student (Mills & Gay, 2016). The methodology sections will continue to describe the data collected with the primary instrument, the BRIEF-A questionnaire. This questionnaire is a validated survey specifically designed to assess self-reported EFS in adults aged 18 to 90 years. With 75 items covering various aspects of EFS, included in the BRI and the MI, the BRIEF-A provided a robust framework for evaluating participants' perceptions of their own EFS (Roth et al., 2005). Next, participant recruitment will be discussed, including explanations about in-person recruitment during scheduled class times and online recruitment via the university's web-based learning management system Canvas. Following data collection, procedures were implemented to address missing data and to ensure the validity of the dataset. Participants with incomplete 8 survey responses were carefully reviewed, and missing data were either imputed or excluded based on predefined criteria. This process is described at length in the methodology section. Additionally, validity scales defined in the BRIEF-A manual will be thoroughly detailed, specifically explaining the process in identifying and excluding participants with inconsistent or potentially unreliable responses. Data analysis will be outlined, detailing the use of the IBM SPSS Statistics software to explore relationships within and between variables included in the dataset. Descriptive statistics were used to summarize the demographic characteristics of the sample, while inferential statistics such as Mann-Whitney U tests and Spearman's Rank-Order Correlation test were employed to assess group differences and relationships between variables, respectively. The methodology section concludes by acknowledging and addressing several limitations inherent in the research design and methodology. These limitations included the reliance on self- reported data, the limited scope of demographic variables assessed, and the potential for biases in survey responses. By openly acknowledging these limitations, the study aimed to enhance the transparency and credibility of its findings while also providing avenues for future research to address these shortcomings. 1.6 Significance of Study Through the exploration and identification of potential associations between EFS and academic performance, educators and administrators can gain insights into factors that may influence students' ability to excel academically. Understanding these relationships can inform the development of targeted interventions and support services tailored to students' specific needs, ultimately fostering a more conducive learning environment for students to succeed. 9 This research opens avenues for future exploration in several directions. Subsequent studies could dive deeper into the underlying mechanisms linking EFS with academic performance. Interventions could then be designed and implemented into academic settings to help bolster specific EFS skillsets. Then, investigations into the effectiveness of these interventions aimed at enhancing EFS could identify what strategies are worth pursuing for promoting student success and well-being. Future research could also explore potential differences in EFS and their impact on academic outcomes across various demographic groups. Overall, this study can contribute to advancing a more nuanced understanding and further the discussion around factors influencing student success at the university level. 1.7 Organization of Thesis This thesis consists of five chapters. In Chapter 1, the research purpose and goal is introduced, emphasizing the significance of the study. It offers a concise overview of prior research, presenting the guiding questions, outlining the research methods, discussing potential limitations, and the overall thesis structure. Chapter 2 covers the theoretical and historical foundations of EFS. It explores how the brain develops overtime, and its impact on cognitive abilities. It discusses the intersection of sex assigned at birth and EFS, addressing debates on brain development differences. Finally, it examines how EFS affects university students' academic success and coping strategies. Chapter 3 outlines the study's approach, including research design, participant recruitment, data collection using the BRIEF-A questionnaire, analysis methods, and ethical considerations. It aims to understand the relationship between age, sex assigned at birth, 10 academic year of study, EFS, and academic performance in university students using a quantitative cross-sectional design. Chapter 4 summarizes quantitative data analyses addressing the study's research questions. It reports demographic characteristics, EFS self-reported deficits, significant differences between groups based on sex assigned at birth and academic year of study, and examines correlations between age, EFS, and academic performance. Through descriptive statistics and statistical tests, it suggests how these factors relate to executive functioning and academic success among university students. Last, Chapter 5 analyzes past research on self-reported EFS in students and potential implications, with a focus on working memory, task monitor, plan/organize and organization of materials. This section also highlights physiological and psychosocial factors influencing emotional control and suggests avenues for future research and practical implementations. This chapter acknowledges the limitations in the current study, including the use of self-reported data, restrictions in participant demographics, and the omission of external variables that could impact executive functioning. 11 Chapter 2: Literature Review 2.1 Chapter Overview This chapter provides an in-depth analysis of EFS among university students. There is a discussion of the historical timeline of EFS research, exploring its roots in case studies, theoretical frameworks, and modern perspectives. Neuroscientific underpinnings of EFS and its relevance in cognitive psychology is explained. An evaluation about the influence of age and sex assigned at birth on EFS development, drawing insights from interdisciplinary research spanning neuroscience, psychology, and education is discussed. By examining how these factors intersect with cognitive processes, there is an aim to provide a comprehensive understanding of the diverse pathways that shape students' cognitive abilities. Then, this chapter investigates the practical implications of EFS in the academic domain, particularly its impact on students' study habits, academic performance, and overall well-being. Last, there is an exploration of the socio- cultural dimensions that influence students' engagement with learning and academic success. 2.2 Theoretical and Historical Perspectives of EFS The field of EFS, like many neuropsychological topics, originates back to the famous case of Phineas Gage, a railroad foreman who was struck through the left frontal lobe by an iron rod. Physically, Phineas suffered from loss of sight in his left eye and partial paralysis of his face, yet remarkably retained his neurological functions (Guidotti, 2012). Despite being a medical marvel for surviving this profound injury, Phineas underwent significant alterations in his once gentle and reliable personality. Following the incident, he became characterized as rude, impulsive, and disorganized, changes that persisted for approximately four years after the indecent before gradually reverting back. Phineas was then able to secure a working position as a 12 long-haul driver, a role that required both planning and focus (Teles, 2020). This dramatic shift in demeanor post-accident sparked considerable curiosity and research into the role of the frontal lobes, which were previously believed to be unrelated to human behaviour. The “American Crowbar Case,” as it’s often referred to, propelled the field of psychology forward, with many researchers seeking answers to uncover the underlying mechanisms by which our control and execution of behaviours occur. Distinctions were eventually made between automatic and controlled processes within the nervous system, and the proposition of selective attention and working memory being held in “executive branches” of the mind became a consensus among psychologists (Baddeley, 1992; Broadbent, 1953; Shrifrin & Schneider, 1977). Alexander Luria, predominantly recognized as a founding father of modern neuropsychology, composed the first formal description of the frontal lobe functions, and contributed to the concept of higher order thinking (Derouesné, 2018; Goldstein & Naglieri, 2014). Heavily influenced by Lev Vygotsky’s cultural-historical theory surrounding brain development and higher mental functions, Luria expanded Vygotsky’s work by proposing his neurodevelopmental model, which posits that the interplay between biological development and socio-cultural stimuli shapes the abilities of the human cortices (Bodrova et al., 2011; Goldstein & Naglieri, 2014; Horton, 1987). In other words, how the brain develops is a product of both nature and nurture and is the guiding framework for the discussion of the results from the current study. In 1973, the term “executive functions” was coined by Karl Pribram, who determined that these overarching mental processes in the brain are controlled and mediated by the PFC (Pribram, 1973). Today, EFS is understood as the skillset that encompasses the broad range of higher- order cognitive processes coordinated by the PFC that involve the control and management of 13 thoughts, actions, attention, and emotions (Goldstein & Naglieri, 2014). EFS is crucial for everyday life and learning, as it regulates one’s ability to reason and make sound, appropriate judgements (Gilbert & Burgess, 2008). To better understand the specific skills that contribute to these cognitive and behavioural actions, EFS has been divided into two sections, as defined by the BRIEF-A posited by Roth and colleagues (2005). 2.2.1 The BRI BRI refers to the portion of EFS contributing to cognitive and emotional regulatory skills that allow one to think rationally and critically about a situation, and then proceed to act appropriately to achieve a goal (Duncan et al., 2017). The four subscales of behavioural regulation include inhibition, shifting, emotional control, and self-monitoring (Roth et al., 2005). • Inhibition describes the ability for one to stop their behaviours or responses at an appropriate time, such as halting actions and thoughts (Goldstein & Naglieri, 2014; Spaniol & Danielsson, 2022). • Shifting describes the ability for an individual to move from one situation freely and flexibly to the next and respond accordingly to the ongoing scenario and the “rules” within a given situation (Goldstein & Naglieri, 2014; Spaniol & Danielsson, 2022). • Emotional control describes the ability to mediate and override emotional reactions by consciously rationalizing about the situation (Goldstein & Naglieri, 2014). • Self-monitoring describes the ability to regulate and evaluate one’s own progress and/or performance against the end-goal or standard (Goldstein & Naglieri, 2014). 14 2.2.2 The MI The MI refers to an individual’s ability to think about their own thinking, learning, and understanding about their knowledge, abilities, and strategies to complete a task (Flavell, 1979). The five subscales of metacognition include the initiation, working memory, planning/organization, task monitoring and organization of materials (Roth et al., 2005). • Initiation describes the ability to start a task and independently construct original ideas or solutions (Goldstein & Naglieri, 2014) • Working memory describes the capacity to grasp and hold information in the mind for the purpose of working through and completing a task, including maintaining selective attention (Goldstein & Naglieri, 2014; Shatil et al., 2014). • Planning/organization describes the ability to coordinate present and future demands of a goal or task (Goldstein & Naglieri, 2014). • Task-monitoring describes one’s ability to monitor and track the progress and effectiveness of completing or engaging with a task (Goldstein & Naglieri, 2014). • Organization of materials describes the ability to establish order within multiple spaces of life (Goldstein & Naglieri, 2014). 2.3 Brain Development and EFS 2.3.1 Age and the Brain The most significant changes in brain development occur before birth. Rapid development then continues after birth, with changes in grey matter and white matter. Grey matter is responsible for processing incoming information, whereas white matter is responsible for the communication between neurons (Crone, 2017). During the development of the cortex, 15 the outermost layer of the brain, there is a large increase in grey matter volume between ages 0 to 6, with a peak between the ages of 4 to 8. This is then following by a rapid decrease in grey matter volume in middle childhood (approximately ages 6 to 12), which a process called synaptic pruning. Excess grey matter can lead to inefficiency due to unnecessary neural connections, therefore this synaptic pruning process is beneficial as these unnecessary neural connections are removed to optimize brain function. White matter volume expands and grows rapidly in the brain in the first two years of life and continues to undergo signification development until around the age of 11 (Crone, 2017). When white matter volume increases, it is associated with the process of myelination, which is when myelin sheath cells grow and envelop neuronal axons with phospholipid-rich plasma membranes around neuronal axons to provide insulation and increase the speed of communication between neurons. Peaks of white matter volume occur between the ages of 4 and 11 (Herting & Sowell, 2017). Within this age range, there is an abundance of connectivity between neurons, which creates optimal conditions for learning languages, mathematics, motor skills, social cues, and behaviours. During puberty and adolescence, the age range of 8 to 16, synaptic pruning and myelination continues to refine neural circuits in the brain, with cognitive functioning, memory formation and emotional regulation continuing to develop. The age range of 18 and 25, often referred to as the period of “emerging adulthood,” still continues to develop and refine grey and white matter, but at a much more subdued rate and level compared to earlier stages (Veroude et al., 2013). Due to the series of important events that take place during this phase of life, such as leaving home, acquiring newfound responsibilities, and gaining independence overall, the brain is continues to adapt structurally throughout these life 16 events. Therefore, there can be an observed period of changed in the brain from late adolescence (18–19 age group) to early adulthood (23–25 age group). While the brain is fully developed structurally by the time students enter university, the PFC continues to myelinate slowly into early adulthood (Kolk & Rakic, 2022). This myelination process is important for the connectivity that drives information transfer between brain regions in a synchronized manner to coordinate and execute EFS (Nickel & Gu, 2018). These interconnective changes may allow the early adulthood brain to support more difficult, complex, and structured actions, such as critical thinking and emotional regulation (Guyer et al., 2018). This is further illustrated in Veroude et al.’s (2013) study examining cognitive control differences between late adolescence and early adulthood when performing skills that require EFS. Brain imaging scans from this study revealed that the early adulthood group exhibited stronger employment between brain regions compared to the late adolescent group when completing the same tasks related to EFS, along with greater prefrontal cortex activation increasing linearly with age. Possible implications could include those students within the 18-19 age group exhibiting weaker task performance when presented with cognitive and emotional mental interferences. For example, those in the late adolescent group may have more difficulties persevering in challenging mental tasks like problem solving or feel less equipped to deal with anxiety leading up to a test, resulting in an inability to recall retained information (Grützmann et al., 2019; Pierce et al., 2014). The late adolescent brain has also shown to be more responsive to certain stimuli compared to early adulthood brains. In response to reward cues, emotional salience towards a situation, and the influence of peers has shown to produce heightened activity in the reward- related areas of the brain in late adolescent cohorts compared to older cohorts (Guyer et al., 17 2018). Implications of this elevated activity for the late adolescent group include decreased attentional control when distractions are present and a higher likelihood to engage in risk-taking behaviours compared to their early adulthood counterparts (Chein et al., 2011). As mentioned previously, the brain does develop based on genetic predispositions, but also based on the life events, specifically during the developmental stages of the brain. This can lay the neuronal foundation for effective utilization of EFS throughout the years. Therefore, students can utilize neural plasticity to their benefit by practicing these skills, in conjunction with positive lifestyle factors, such as appropriate nutrition, adequate sleep, and regular exercise, early in their academic career to “prime” their EFS neuronal pathways for future learning behaviours and psychosocial stressors experienced from the external environment (Gibb & Kolb, 2015). In psychological contexts, how experiences shape the brain relies heavily on the idea of neural plasticity. Neural plasticity refers to the consistent regulation of plastic changes that occur in the brain as a response to experiences and therefore will denote future plasticity within the brain (Kolb et al., 2012). Interactions that students have with the world around them organizes and re- organizes the structure and strength of neural pathways, leading to changes in future behaviour and psychological functioning. Lifestyles factors such as frequent exposure to stressful situations, relationships with peers, and exposure to psychoactive drugs are all examples of events that can significantly impact the development of the PFC (Sotiropoulos & Trejo, 2021). 2.3.2 Sex Assigned at Birth and the Brain This section must be prefaced by stating that neuroscientific research surrounding sex and gender differences found in the development and structure of the brain is a contradictory and convoluted discussion. Sex assigned at birth refers to the classification of individuals as female, 18 male or intersex based on the physical, anatomical characteristics present at birth, typically determined by reproductive organs. Gender identify reflects the social, cultural, and psychological traits linked to the sense of being male, female, both, neither or anywhere along the gender spectrum. Gender identity is an individual’s internal understanding and experience of their own gender in a social context (Bates et al., 2022). While neuroscientific research oftentimes only consider the biological components found between assigned at birth sexes, many contemporary scholars within the field caution the propagation of conversations justifying biological behavioural and mental differences between sexes alone, and to include gender identity in these conversations. Due to the inconsistent and controversial nature of research surrounding differences between sex assigned at birth and gender, only the most recent and extensive studies will be referenced, with tentative conclusions drawn. Early research in this field would first ground its work in genetics, exploring hormonal influences that impact the development of brain regions. There are an abundance of gonadal steroid receptors in the brain, allowing pubertal hormones to have a substantial effect on brain development (Konrad et al., 2013). While assigned males and females in general produce the same types of sex steroid hormones, their concentration levels differ, producing distinct effects on body organs. From conception, typical assigned at birth males have a sex-determining region Y protein (i.e., the Y chromosome) in their genetic code that will induce the formation of testes, which in turn produce the hormone testosterone (Bakker, 2022). Conversely, typical assigned female do not have the Y protein and development occurs without any hormonal input during the fetal stages, resulting in the creation of ovaries and eventually the production of the hormone, estradiol. 19 The revelation of brain imaging tools have allowed researchers to investigate differences in brain anatomy between assigned sexes. For example, a well-supported finding gained from brain imaging tools is the discovery that assigned male’s brain volume is on average 10%-11% larger than females (Bakker, 2022; Eliot et al., 2021; Liu et al., 2020; Raznahan & Disteche, 2021). Further exploration into regional brain anatomy has resulted in mixed findings that are hotly debated in the neuroscience community. For instance, early studies in this field found differences in grey matter volume, the portion of the brain that processes incoming information, between males and females (Bakker, 2022). Specifically, studies have suggested larger grey matter volume found in the amygdala region for males, and the hippocampal area for females (Bakker, 2022; Golstein et al., 2001; Raznahan & Disteche, 2021; Sisk & Zehr, 2005). The hippocampus is integral to learning and memory acquisition, and the amygdala is a core component in the limbic system, responsible for the processing of emotions, interests, and motivations. Past studies have suggested that testosterone has been documented as a hormone that increases the activation of the amygdala in assigned males, and estradiol has been found to have a regulating role in the neuronal spine synapse development within the hippocampus for assigned females (Hara et al., 2015; Radke et al., 2015). However, a recent narrative review by Kheloui, et al. (2023) states that these findings have been highly contested and lack a comprehensive view. Larger and more recent studies have failed to consistently replicate sex/gender differences in grey matter volume in the amygdala and hippocampus, especially once brain size is accounted for. Additionally, a review synthesizing results from three decades of brain imaging and post-mortem studies conducted by Eliot and colleagues (2021) also stated little to no evidence in support of sexual differences in brain anatomy between assigned sexes. They also agree that negligible differences are found in grey 20 matter volume in the amygdala and hippocampus across studies and urges researchers to take into consideration cultural and societal aspects that impact how the brain develops. Overall, authors and researchers within the neuroscientific field suggest that there is a need for a more comprehensive transdisciplinary approach that includes both neurobiological and psychosocial factors when investigating sex/gender differences in brain anatomy and function. 2.4 How EFS Impacts University Students There has shown to be a consistent correlation between EFS and academic performance among university students. Previous self-reported EFS studies have found significant negative correlations between challenges in specific EFS scales, such as working memory and organization abilities, and academic performance (Manuhuwa et al., 2023; Ramos-Galarza et al., 2020). Metacognitive skills specifically have been reported as significant predictors of academic success. Examples of these abilities, such as planning and monitoring task progress, have shown to be impactful for students adjusting and succeeding in university life and have shown to increase student’s abilities to be self-regulated learners (Sheehan & Iarocci, 2019; Thibodeaux et al., 2017). Furthermore, research has shown that students who report high levels of EFS on self- reported questionnaires employ self-regulatory strategies (such as managing study session time and maintaining focus on assignments) more often than those who report low EFS abilities (Baars et al., 2015; Petersen et al., 2006). Specifically, self-regulatory strategies require students to employ the working memory, inhibition, task-shifting, planning/organization, self- and task- monitoring subscales of EFS (Hofmann et al., 2012; Kitsantas et al., 2008). If unable to, students 21 may engage in other less effective strategies, like academic procrastination, a phenomenon where students delay starting or continuing progress on an academic activity (Schouwenburg, 2004). Procrastination is a common issue with university students and is often associated with poor academic success and well-being in post-secondary education, leading to heightened levels of stress, anxiety, depression, and feelings of burnout (Tekkurşun Demir et al., 2017). The exact opposite of self-regulation, academic procrastination has been shown to correlate with low levels of working memory, inhibition, self-monitoring, and task-monitoring on self-reported measures of EFS (Rabin et al., 2011). Avoidance strategies, such as procrastination, can be the result of ineffective coping strategies. Both males and females have reported high use of procrastination in post-secondary, but for different reasons. If female students engage in academic procrastination, it has shown to be attributed to fear of failing and negative emotions, resulting in utilizing avoidance coping mechanisms, such as seeking emotional support from friends or family to regulate the stress and unpleasant feelings that come with challenging tasks (Özer et al., 2009, Martínez et al., 2019). Male students were shown to employ more meaning-focused coping strategies, leading to higher engagement with studies in the pursuit of better understanding the problems or gaps in their knowledge (Martínez et al., 2019). If males do engage in procrastination, it is likely due to risk-taking and rebellion as opposed to feelings of incompetence (Özer et al., 2009). Additionally, high-scoring EFS students have been shown to exhibit better coping strategies than low-scoring EFS students. According to research, students who can visualize their academic aspirations and translate them into actionable steps perceive less stress in their lives than those who cannot (Petersen et al., 2006). These skills have been shown to predict academic success, especially for first-year university students as they transition from high school to higher 22 education and learn to make crucial learning decisions. Assigned females have also reported making fewer repeat mistakes, use more university resources, diversify their learning strategies and, on average, achieve higher grades than assigned males, according to research studies on sex assigned at birth differences in higher education (Almoslamani, 2022; Harrop et al., 2007; Kaldo & Õun, 2020; Khan & Rasheed, 2019; Marrs & Sigler, 2012). While assigned females appear to exhibit better learning habits, assigned males have been found to exhibit more confidence in the classroom than females. For example, assigned males ask more questions, share more ideas, participate in more discussions, and ultimately perceive academia as less stressful than assigned females (Harrop et al., 2007). 23 Chapter 3: Methodology 3.1 Chapter Overview This chapter outlines the methods used to obtain and analyze data for the current study. This includes a concise overview of the research design, and the participant recruitment protocols, including a comprehensive explanation of the inclusion and exclusion criteria. Additionally, the procedures and data collection methods are listed, providing an in-depth description of the primary instrument, the BRIEF-A questionnaire. Support for the use of the BRIEF-A questionnaire in this study is also provided. A detailed section about the data analysis with justifications for the statistical protocols used are described. Last, a list of limitations and delimitations present in this study are discussed and concludes with a brief summary of this section. 3.2 Research Design A quantitative approach was utilized to evaluate and explore the current state of executive skillsets in student’s enrolled in first-year university courses using a validated survey to report how students self-assess their own EFS. Since data were collected only at one time, this study follows a cross-sectional survey research design. According to Mills and Gay (2016), a cross-sectional research design effectively captures a single snapshot of a population’s current beliefs, attitudes, and self-perceived behaviours. Additionally, this study specifically focuses on examining differences and relationships between groups, making use of comparative and correlational methods to understand the interplay between variables. This design aligns with the overall objective of the present study, which is to explore modern student behaviours in relation to their EFS and academic 24 performance. Furthermore, the study aims to investigate how different demographic variables may influence these factors and how they may be interconnected. This methodology has been used in prior studies evaluating specific aspects of EFS among university students. Bernstein (2020) utilized a cross-sectional correlational approach utilizing multiple self-report surveys (including the BRIEF-A questionnaire) to assess how parental influence impacted college students’ procrastination habits. Similarly, Sun et al. (2022) assessed the relationship between students perceived self-directed learning abilities (a combination of executive functions) and their engagement in online learning throughout post- secondary education. Both studies emphasize that investigating EFS in the university student population is a new and developing area. By requesting students to self-assess their own cognitive skillsets, educational and psychological researchers can gain valuable insights into university students' perceptions of their own strengths and weaknesses concerning EFS. This approach contributes to a better understanding of how executive functioning skills may impact students' academic experiences and performance. In summary, this study adopts a quantitative cross-sectional research design to explore the interweaving relationships between age, sex assigned at birth, academic year of study, EFS, and academic performance in university students. Participants had the option to permit the researchers access to their academic data, including their 2021/2022 academic year GPA, 2022 fall semester GPA, and final course grade percentage from the course they were enrolled in when recruited. Following consent, participants completed demographic questions and the 75-item BRIEF-A questionnaire. 25 3.3 Participants Initially, 107 participants from five different first-year courses were recruited for this study. One survey response was removed due to one student completing the survey twice. Twenty-six participants in total had missing data from incomplete survey responses. To address this issue, a missing data process described by Hair (2006) and Tabachnick and Fidell (2013) was applied. First, each incomplete survey response was analyzed to determine the number of missing responses in each individual dataset, ensuring that the missing responses per variable fell within the acceptable range of 0.4% to 10% (Hair, 2006). Out of the 26 participants with missing data, 20 participants exceeded the maximum percentage and were subsequently excluded from the study. The remaining six survey responses were further analyzed to identify the pattern of missing data. To confirm that the pattern of missing data was random, Little’s Missing Completely at Random (MCAR) test was conducted (Tabachnick & Fidell, 2013). The results indicated that the missing data responses were missing completely at random, as the estimated means did not show statistical significance across the missing data. As the remaining six respondents met the criteria for imputing missing data, the Expectation Maximization strategy was employed (Tabachnick & Fidell, 2013). This strategy is specifically used for MCAR data and involves creating a general correlation by assuming the distribution curve based on other complete datasets to fill in the missing responses from participants. After assessing, removing, and/or imputing the missing datasets, the remaining 86 participant survey responses were analyzed for validity. The primary instrument used to evaluate self-perceived EFS in undergraduate students was the BRIEF-A questionnaire. To assess the 26 validity of the survey responses, the BRIEF-A manual was used, which provided detailed instructions on the scoring and interpretation of the questionnaire results. The BRIEF-A manual includes three scales that offer information and assessment of the validity of each individual survey response based on the normative and clinical data samples used to standardize the BRIEF-A questionnaire (Roth et al., 2005). These three scales are the Negativity Scale, the Infrequency Scale, and the Inconsistency Scale. These scales serve as the inclusion and exclusion criteria for the participants of this study. 3.3.1 The Negativity Scale The Negativity Scale evaluates infrequent patterns of high ratings (responses of Often) to particularly negative questions. This scale consists of ten specific questions. If respondents answered Often to six or more of these questions, they were considered to have a significantly negative outlook. In such cases, it should be noted that the participant may possesses an overly negative perception and/or experience severe executive dysfunction. Consequently, their survey response should be carefully interpreted. Examples of questions in the Negativity Scale include items related to high emotional reactivity to minor situations or making inappropriate comments in social settings. In the current study, none of the participants scored six or higher on the Negativity Scale, and thus, none were excluded based on this criterion. 3.3.2 The Infrequency Scale The Infrequency Scale evaluates the unusual nature of responses that are highly unlikely in a normative population. For instance, questions referring to the inability to perform basic counting skills or forgetting one's own name, answered as Often, would be highly unusual, 27 especially in a post-secondary academic setting. This scale consists of five specific questions. If a respondent answered Often to three of more of these questions, they were considered to have significantly infrequent responses. Scoring significantly infrequent responses often indicates that respondents may have completed the survey in a haphazard or unintentional manner, leading to skewed results. In this study, twelve respondents received significantly infrequent scores, and as a result, these participants were removed from the study. 3.3.3 The Inconsistency Scale The Inconsistency Scale evaluates conflicting survey responses belonging the same EFS scale. If a participant responds to a question about a particular EFS scale in one direction, it would be unlikely for them to answer a question about the same skill within the same EFS scale in the complete opposite direction. For example, it is unlikely that a participant would answer Often to a prompt asking if they are disrupted by change happening in their life and, at the same time, answer Never to being disturbed by abrupt changes in their daily routine. Both of these items pertain to task-related skills belonging within the Shift scale and would be typically answered in the same manner. The Inconsistency Scale utilizes ten pairs of questions, each pair belonging to the same EFS scale to determine the differences found between similar questions. To assess this, answers were converted to numerical values: Never = 1, Sometimes = 2, and Often = 3. Then, for each pair of questions, the absolute value of the differences between the item scores was calculated. For instance, if the first item score in a pair of questions from the same EFS scale is 1 (Never) and the second item score is 3 (Often), the absolute difference value would be 2. Participants 28 with a score of eight or higher were considered significantly inconsistent. Similar to the Infrequency Scale, significantly inconsistent scores often indicate that the respondents completed the survey in a careless or inadvertent way, leading to potential skewing of survey results. In the present study, four participants received significantly inconsistent scores and were subsequently removed from the study. Therefore, a total of 70 participants were included in the data analysis, with 41 identifying as assigned-females, one as assigned-intersex, 27 as assigned-males, and one participant choosing not to disclose their sex assigned at birth. The participant’s mean age was 20.84 years (Mdn = 20, SD = 3.642, range: 18-44). Among the participants, 28 were in their first year of post-secondary academic study, 19 in their second year, 12 in their third year, nine in their fourth year, and two in their fifth year. Of the 70 participants, 53 granted consent to access their 2021/2022 academic year GPA, 2022 fall semester GPA, and final course grade percentage of their respective course they were recruited from. Within this group, 32 participants were assigned females, and 21 were assigned males. Additionally, all 53 of these students provided final course percentages. Out of the 53 participants who consented to share their academic data, 34 had 2021/2022 academic year GPAs, and 52 had 2022 fall semester GPAs. Within the 34 participants with 2021/2022 academic year GPAs, 24 were assigned females and 10 were assigned males. Of the 52 students who provided 2022 fall semester GPAs, 32 were assigned females, and 20 were assigned males. In the present study, a total of four instructors participated, each teaching one course, except for one instructor who taught two. Two courses were from the summer 2022 semester, and three were from the fall 2022 semester. The courses recruited from were as follows: 1. PHYS 122 – Introductory Physics for the Life Sciences II 29 2. PSYO 121 – Introduction to Psychology: Personal Functioning 3. GEOG 129 – Human Geography: Resources, Development, and Society 4. GEOG 128 – Human Geography: Space, Place, and Community 5. HES 130 – Social Determinants of Health, respectively. The participant demographics from each course is listed in Table 1 below. Table 1 Participant Demographic Information from Each First-year Course Course n PHYS 122 2022 Summer Age 18 1 19 6 20 3 21 3 22 1 23 1 Total From Course 15 Sex Assigned at Birth Male 3 Female 12 Total From Course 15 Year of Post-Secondary Academic Study First year 6 Second year 3 Third year 3 Fourth year 3 Total From Course 15 Course n PSYO 121 2022 Summer Age 19 6 20 4 21 1 23 2 24 3 Total From Course 16 Sex Assigned at Birth Male 7 Female 9 Total From Course 16 Year of Post-Secondary Academic Study First year 5 Second year 5 Third year 3 Fourth year 3 Total from Course 16 30 Course n GEOG 129 2022 Fall Age 18 2 19 4 20 5 21 1 22 1 25 1 44 1 Total From Course 15 Sex Assigned at Birth Male 4 Female 9 Intersex 1 Prefer Not to Disclose 1 Total From Course 15 Year of Post-Secondary Academic Study First year 7 Second year 6 Third year 2 Total From Course 15 Course n GEOG 128 2022 FALL Age 18 3 19 2 20 5 21 6 22 1 23 1 26 1 27 1 Total From Course 20 Sex Assigned at Birth Male 11 Female 9 Total From Course 20 Year of Post-Secondary Academic Study First year 8 Second year 3 Third year 4 Fourth year 3 Fifth year 2 Total from Course 20 31 Course n HES 130 2022 Fall Age 18 1 19 1 20 1 31 1 Total From Course 4 Sex Assigned at Birth Male 2 Female 2 Total From Course 4 Year of Post-Secondary Academic Year First year 2 Second year 2 Total From Course 4 Total Number of Participants in Study 70 3.4 Procedures Participant recruitment occurred at the beginning of one regular scheduled class time for each approved University of British Columbia, Okanagan (UBCO) course. Recruitment was also conducted through the campus web-based learning management system, Canvas. Through online recruitment, students had the opportunity to view a posting of the study description on their announcements page and could choose to voluntarily participate in the study. For in-class recruitment, twenty-minutes were allotted at the beginning of class time for students to voluntarily choose to partake in the research study. No incentives were offered for participating in the study. The recruitment materials for both in-class and online modalities included a concise speech (stated either verbally or written) that informed students about the study’s purpose potential benefits, risks of participation, confidentiality measures, their rights as participants and contact information of both the researcher and the supervisor. A downloadable consent form in a Word document format was made available to students on their online Canvas course shell through the announcements page. Participating students were directed to follow the online Qualtrics survey 32 link provided on their respective Canvas announcements page. The survey contained the consent form, demographic information questions and the BRIEF-A questionnaire. 3.4.1 Ethical Considerations and Consent The present research study received approval from the UBCO Behavioural Research Ethics Board (BREB). Additionally, a licensing agreement with the Psychological Assessment Resources (PAR, Inc.) was established to use the BRIEF-A Self Report Form for student research purposes. In the summer 2022 semesters, courses with over fifty students enrolled were considered for recruitment. In the fall 2022 semester, instructors with over 200 students enrolled in their courses were considered for recruitment. This criterion was established to target courses with high student enrollment for their respective terms to maximize the potential for student recruitment. To safeguard the confidentiality of survey responses and identifiers, each survey response was given a unique study identifier. Subsequently, all names and student number identifiers were promptly removed after collecting of GPA and final course grade percentages. The data concerning incoming GPA and final course grade percentage were then securely stored in a password protected Excel sheet on the researcher’s personal laptop, which was also password protected.33 3.5 Data Collection 3.4.2 Demographic Information Collected Students were requested to provide the following information: their full name, student identification number, age, sex assigned at birth, program major and current academic year of study. Additionally, students were given the option to provide consent to access their incoming GPA for both the 2021/2022 academic year and the 2022 fall semester, as well as the final grade percentage of the course they were recruited. After, a list of participants’ names and student number was compiled in a password-protected Excel sheet and forward via email to the registrar’s office to obtain specific academic metrics. The registrar’s office then provided calculated incoming GPA for the 2021/2022 academic year, GPA for 2022 fall semester, and final course grade percentage for consenting students in another password-protected Excel sheet. 3.4.3 Instrument: The BRIEF-A Questionnaire The BRIEF-A questionnaire is a self-reported assessment that gauges adults’ (aged 18 to 90 years) perceptions of their executive functions. It measures two indexes: the BRI and the MI. The BRI assesses an adult’s ability to maintain and monitor appropriate regulatory control over their behaviour and emotions. The BRI is comprised of four clinical scales: the Inhibit scale, the Shift scale, the Emotional Control scale, and the Self-Monitor scale. The MI evaluates an adult’s capacity to solve problems systematically and consistently across different contexts by cognitively managing their attention in appropriate ways. The MI is comprised of five clinical scales: the Initiate scale, the Working Memory scale, the Plan/Organization scale, the Task-Monitor scale, and the Organization of Materials scale (Roth et al., 2005). 34 The scores from these indexes are combined to calculate the Global Executive Score (GEC), which represents the overall score of the entire questionnaire. The BRIEF-A questionnaire consists of 75 items and uses a 3-point scale Likert scale for responses: 1. Never 2. Sometimes 3. Often The BRIEF-A has demonstrated its reliability and validity through extensive research. It has been translated into over thirty languages and has consistently shown a moderate to high alpha coefficient (Cronbach’s alpha, 1951) of 0.73 to 0.90 for their clinical scales and 0.93 to 0.96 for their clinical sample in the BRI, MI, and GEC regarding internal consistency. Test-retest reliability correlations within a normative population have also exhibited moderate to high alpha coefficients in the range of 0.82 to 0.93 (Roth et al., 2005). The questionnaire’s validity has been examined through various studies and tests. For instance, a study involving 40 adult participants from mixed healthy/clinical populations found strong correlations between the BRI (r = 0.84), the MI (r = 0.73) and GEC (r = 0.84) scores of the BRIEF-A and the overall scores of the Dysexecutive Self-report Questionnaire (DEX), another psychometrically valid survey assessing EFS. Moreover, other widely accepted surveys of executive function, such as the Frontal Systems Behavior Scale and the Cognitive Failures Questionnaire, have also shown strong content convergence with the measures of EFS in the BRIEF-A questionnaire. The BRIEF-A has been validated in the pre-diagnosis of clinical groups, including patients with attention-deficit/hyperactivity disorder (ADHD), mild cognitive impairment (MCI), and epilepsy. These results indicate that the BRIEF-A is sensitive to executive complexities aligned with cognitive difficulties related to specific clinical populations. 35 However, it’s essential to note and emphasize that the BRIEF-A questionnaire is not intended to diagnose specific disorders. Instead, it serves to indicate potential risks for specific patterns that might suggest executive dysfunctionalities in adults, otherwise known as being “flagged” for executive deficits. The BRIEF-A questionnaire should be considered as only one component within a comprehensive evaluation that assesses various aspects of the individual participant. 3.5 Data Analysis Statistical analyses were performed using IBM SPSS Statistics (Version 29) software for MacIntosh. 3.5.1 Percentage of Survey Responses Flagged for Executive Deficits To determine the percentage of scales, indexes and overall GEC scores indicating executive deficits, the BRIEF-A manual was consulted. First, the raw score for each survey item of the BRIEF-A questionnaire was obtained. This process was carried out using SPSS, where each Likert scale response was transformed and recoded into a numerical value, aligning with the BRIEF-A scoring protocol (Never = 1, Sometimes = 2, and Often = 3). Next, a new variable was computed to sum up the numerical item responses on the survey belonging to a specific scale, resulting in a raw score for each of the nine scales of EFS. The same procedure was followed to calculate raw scores for the BRI, MI, and the overall GEC score. To convert the raw scores into percentiles for each scale, index and overall GEC, the BRIEF-A manual appendix tables were utilized (Roth et al, 2005). These tables provided information about how a respondent’s score compares to those of the normative sample 36 population used to standardize the BRIEF-A questionnaire. If a respondent’s raw score translated to a percentile at or exceeding 95%, they would be considered clinically significant, and therefore flagged for potential executive deficits in that particular scale, index or overall GEC score. These percentiles were then manually translated and inputted into SPSS for analysis. To present the results, a simple bar graph was created to depict the number and percentage of participants who exceeded the 95% limit for each scale, index, and overall GEC score. This allowed for a clear representation of the proportion of participants showing potential executive deficits in different areas. 3.5.2 Mann-Whitney U Test To examine if there were statistically significant differences in EFS scores and academic performance measures between assigned males and assigned females, as well as between first- year students and upper-level students, the Mann-Whitney U test was performed using SPSS. The Mann-Whitney U test is a nonparametric test designed for two independent samples and is employed when the data of the dependent variables in a sample do not follow a normal distribution (Wall Emerson, 2023). Unlike standard t-tests, the Mann-Whitney U test does not assume any specific distribution of data and evaluates the median value, instead of the mean value, of each group. It is particularly useful when dealing with data that may not meet the assumptions of normality and provides a robust way to compare differences between groups in such cases. The distribution of data in the present study’s dataset was assessed by creating histograms for all dependent variables. All variables did not meet the requirements of a normal 37 distribution to apply parametric tests. Therefore, the Mann-Whitney U test was used to assess differences between: 1. The assigned male and assigned female group’s raw scores from the nine EFS scales, two indexes, overall GEC scores, 2021/2022 academic year GPA, 2022 fall GPA, and final course grade percentage. 2. The first-year and upper-year student group’s raw scores from the nine EFS scales, two indexes, overall GEC scores, 2021/2022 academic year GPA, 2022 fall GPA, and final course grade percentage. It is important to note that for the comparison between assigned males and females, two participants were excluded. These were the participants who selected the “Prefer not to disclose” option, and the “Intersex” option when asked to provide their assigned at birth sex. Due to only one participant present in each group, no analyses were performed using these groups. To interpret the results, the asymptomatic two-tailed p-value was used to assess significant differences between the analyzed groups. Significance was considered if the p-value was less than 0.05. The choice of a two-tailed test was made because the research questions and hypotheses did not specify which group would perform better. Therefore, the assessment of possible differences in either direction of change was warranted. 3.5.3 Spearman’s Rank-Order Correlation To explore potential relationships between age, EFS scale scores, and academic performance, multiple Spearman’s Rank-Order Correlation tests were performed. As previously mentioned, the non-normal distributions of the dependent variable datasets required the use of nonparametric statistical procedures. 38 Spearmen’s Rank-Order Correlation, which is the nonparametric equivalent to the Peterson Product-Moment Correlation, was applied to assess any associations between age, the raw scores of the nine BRIEF-A EFS scales, the two EFS indexes (BRI and MI), and the overall GEC score, as well as the 2021/2022 academic year GPA, 2022 fall GPA, and final course grade percentage (Corder et al., 2009). The correlation coefficient, rs, measures the relationship between two ordinal scale variables. A positive relationship is indicated by a coefficient value ranging from 0 to 1.0, with values closer to 1.0 representing a stronger relationship. In this case, as one variable increases in one direction, the other variable also increases in the same direction. Conversely, a negative relationship is indicated by a coefficient value ranging from 0 to -1.0, with a variable closer to - 1.0 representing a stronger relationship. Here, as one variable increases, the other variable decreases (Corder et al., 2009). 3.6 Limitations and Delimitations Age and sex assigned at birth data were collected as biological and sociological markers and were assessed to explore if these measures were connected to EFS abilities. Asking participants their academic year of study was intended to identify students with varying levels of learning experience in a post-secondary setting, and to assess if this difference impacted EFS scores and academic performance. However, due to a low sample size of students in their third, fourth and fifth years of study, first-year students and upper-year students (including second-, third-, fourth- and fifth-year students) were separated into two distinct groups for analysis. Participants’ program major information was gathered to investigate if students pursuing specific disciplines had similar strengths or deficits in EFS scores. Unfortunately, due to a low 39 sample size of students in several different program majors, it was not appropriate to analyze and draw implications from this specific data. Seeking permission to access each participant’s 2021/2022 academic year GPA and 2022 fall GPA was done to utilize this data as a measure of academic performance. Additionally, obtaining final course grade percentage was requested to evaluate any differences found between students in the same course and across different courses. The original intent of this study was to collect data from one specific course, but due to unfortunate circumstances this was no longer an option, and final course grade percentages in this study now reflect the inclusion of various first-year courses. Targeting recruitment towards large, first-year courses in the summer 2022 and fall 2022 semesters was a convenience measure adopted to maximize the number of participants recruited in one solicitation session. As participants were recruited from two separate semesters, each semester may consist of a cohort with different motivations and characteristics. For instance, the summer semester may include students retaking courses or taking extra courses to graduate early, which may not be as common in students enrolled in the fall semester. Additionally, no winter semester courses were recruited in this study, which further limits generalizability of data. Moreover, the inclusion of second-, third-, fourth-, and fifth-year students enrolled in these first-year courses may also contribute to some uniqueness of the sample. These upper-level students, who are taking a first year courses later in their academic journeys, may not fully represent the typical characteristics and experiences of their respective academic year cohorts. Taking first-year courses in later academic years is not overly common among undergraduate students. As a result, the present study’s findings may not be fully generalizable to students in all 40 semesters, or academic years, and the results will be interpretated within the context of the study’s limitations and potential generalizability constraints. The BRIEF-A questionnaire was selected as the assessment tool for self-reported EFS due to its well-established reputation and extensive use as a valid and reliable measure of executive functioning skills. Regardless, it is acknowledged that obtaining self-reported data using the BRIEF-A questionnaire is not a direct physiological measure of EFS and may be subject to certain limitations. For example, participants’ responses could be influenced by social desirability bias, leading them to exaggerate or downplay certain aspects of their executive functions to present themselves in a more favorable light. Additionally, individuals may have inaccurate memories or a limited frame of reference when assessing their own EFS, which could affect the accuracy of their responses. Therefore, it is recognized that there are potential sources of bias and inaccuracies when interpreting these results. The decision to omit the BRIEF-A questionnaire from the current study is due to the commitment to ethical standards and compliance with copyright regulations, as agreed upon with the distributor, Psychological Assessments Resources. Although the BRIEF-A serves as the primary tool for assessing EFS, its proprietary nature dictates adherence to restrictions on distribution and publication. Consequently, while the questionnaire holds substantial importance in this study, it cannot be directly included. Instead, there is acknowledgement of its utilization within the appropriate methodological framework, ensuring transparency and adherence to ethical standards, thereby preserving the integrity of the research process. It is also acknowledged that this study does not measure any other internal or external variables that could potentially influence the development of the brain, and subsequently EFS capabilities. Factors such as nutrition, prenatal care, diseases, medication history, virus exposure, 41 genetic causes, usage of drugs, toxins and more can play crucial roles in brain development and behaviours. However, due to the complexities of measuring all possible intrinsic and extrinsic elements, the present study focused on only certain specific measures that were easily accessible and within the scope of the present study. As a result, this study does not claim any direct relationship between age, sex assigned at birth, EFS and academic performance, as it did not examine every factor that could impact brain development. Instead, its goal is to contribute to the exploration of potential links between the included measures and variables. The findings from this study can serve as valuable insight for future research in this area, helping to inform more comprehensive investigations in the complex interplay of factors influencing neurodevelopment and education. 3.7 Summary This quantitative research study aimed to investigate potential differences and relationships within the university student population by assessing age, sex assigned at birth, academic year of study, EFS scores and academic performance. To gather data on students’ EFS scores, the study utilized the BRIEF-A questionnaire, along with relevant demographic and academic information. Given the non-normal distribution of datasets, nonparametric data analysis was utilized to ensure accurate and reliable findings. A report of the limitations of the study was provided to describe justifications for the decisions and protocols chosen in the research designed. This survey-based study seeks to shed light on the interplay of various factors relating to executive functioning skills and academic performance in the university student population to provide valuable insights in this field, while acknowledging its limitations and areas of growth. 42 Chapter 4: Results 4.1 Results Overview This chapter provides a comprehensive summary of the quantitative data results obtained from the statistical analyses conducted to address the research questions of this study. It begins by presenting demographic characteristics of the study sample and providing an overview of the participants’ background. Next, a detailed and descriptive section outlines the percentage of students flagged for specific EFS. This analysis sheds light on the prevalence of potential challenges in different EFS areas among the participants. The chapter proceeds to present the findings from nonparametric statistical tests, including results from the Mann-Whitney U tests and Spearman’s Rank-Order Correlation tests. These results highlight significant differences and relationships observed between the variables studied, offering valuable information about the interplay of various factors. Finally, the chapter concludes with a concise summary, synthesizing the main findings derived from the dataset. This summary provides a clear and cohesive overview of the key results found in this study. 4.2 Descriptive Statistics Table 2 provides an overview of the descriptive statistics for participant demographics and academic information. A total of 70 students participated in the study, and their age distribution was as follows: 27.1% were 19 years old (n = 19), 24.3% were 20 years old (n = 17), 15.7% were 21 years old (n = 11), 11.4% were 18 years old (n = 8), 5.7% were 23 years old (n = 4), 4.3% were 22 years old (n = 3), 4.3% were 24 years old (n = 3), and the remaining 7% fell within the age range of 25 to 44 years old (n = 5). The mean age of participants was 20.84 years old, with a standard deviation of 3.642, and the median age was 20 years old. 43 For sex assigned at birth information, the majority of the sample identified as assigned females, accounting for 58.6% of the participants (n = 41). Assigned males made up 38.6% of the participants (n = 27), with the remaining 2.8% consisting of two participants who identified as “intersex” or “prefer not to disclose.” Regarding academic year of study, the distribution was as follows: 40% of participating students were in their first year of academic study (n = 28), 27.1% were in their second year (n = 19), 17.1% were in their third year (n = 12), 12.9% were in their fourth year (n = 9), and 2.9% were in their fifth year (n = 2). 44 Table 2 Participant Demographic Information Measure n % Age 18 8 11.4 19 19 27.1 20 17 24.3 21 11 15.7 22 3 4.3 23 4 5.7 24 3 4.3 25 1 1.4 26 1 1.4 27 1 1.4 31 1 1.4 44 1 1.4 Total 70 100 Mean Age; 20.84 ± 3.642 Median Age; 20.00 Sex Assigned at Birth Male 27 38.6 Female 41 58.6 Intersex 1 1.4 Prefer Not to Disclose 1 1.4 Total 70 100 Academic Year of Study First year 28 40 Second year 19 27.1 Third year 12 17.1 Fourth year 9 12.9 Fifth year 2 2.9 Total 70 100 4.3 Percentage of Student’s Flagged for Executive Deficits This section presents the results addressing the first research question: “What percentage of students are considered flagged for executive deficits in specific clinical scales, indexes, and overall Global Executive Composite (GEC) scores, as measured by BRIEF-A questionnaire?” To visualize the findings, simple bar graphs generated by the SPSS platform were utilized. Each bar graph represents a scale, index, or the overall GEC score and displays the 45 number of participants falling within specific percentile scores. The bars are color-coded, with blue bars representing participants within the normal range of BRIEF-A sum scores, and red bars indicating participants within the abnormal range of BRIEF-A sum scores. The abnormal scores indicate participants who scored at or above the 95th percentile of combined sum scores, based on the normative sample used to standardize the BRIEF-A questionnaire. Such participants are considered flagged for executive deficits. 4.3.1 BRIEF-A Questionnaire Scales The BRIEF-A questionnaire assesses executive functioning skills through nine clinical scales. These scales include the Inhibit scale, Shift scale, Emotional Control scale, Self-Monitor scale, Initiate scale, Working Memory scale, Plan/Organize scale, Task-Monitor scale, and Organization of Materials scale. In this section of the results chapter, the findings for each clinical scale will be presented. 4.3.1.1 The Inhibit Scale Out of the 70 participants, seven scored at or above the 95th percentile on the combined sum of the Inhibit scale survey items, accounting for 10.0% of the total participants. The distribution of percentile scores can be found in Figure 1. The Inhibit scale measures an individual's ability to resist or stop a behaviour or response at an appropriate time. Those with poor inhibition skills may exhibit impulsivity or hyperactivity. Inhibition is often considered a central deficit in conditions like attention deficit hyperactivity disorder (ADHD) and obsessive- compulsive disorder (OCD) (Roth et al., 2005). 46 Figure 1 Participant’s Percentile Sum Scores for the Inhibit Clinical Scale Survey Items 12 10 8 6 4 2 0 9 19 30 41 45 48 54 63 74 84 92 95 97 98 99 Percentile Score (%) 4.3.1.2 The Shift Scale Out of the 70 participants, 11 scored at or above 95th percentile on the combined sum of the Shift Scale survey items, accounting for 15.7% of the total participants. The distribution of percentile scores can be found in Figure 2. The Shift scale measures an individual's ability to transfer from one situation or activity to another, demonstrating their cognitive and behavioural flexibility in appropriate and timely manners. This includes the capacity for flexible problem- solving and switching attention when required by the task or activity. Poor shifting skills may manifest as inflexibility, rigidity, or resistance to change or understanding of other people's perspectives. These poor shifting skills can be commonly observed in developmental disorders, such as Asperger's Disorder (Roth et al., 2005). Number of Partipants 47 Figure 2 Participant’s Percentile Sum Scores for the Shift Clinical Scale Survey Items 16 14 12 10 8 6 4 2 0 20 38 39 51 64 77 82 92 98 99 Percentile Score (%) 4.3.1.3 The Emotional Control Scale Out of the 70 participants, three scored at or above the 95th percentile on the combined sum of the Emotional Control scale survey items, accounting for 4.3% of the total participants. The distribution of percentile scores can be found in Figure 3. The Emotional Control scale assesses an individual's ability to regulate emotional responses. Poor emotional control skills may be characterized by overreacting to small problems, experiencing frequent mood changes, or displaying high emotional reactions with minimal provocation (Roth et al., 2005). Number of Participants 48 Figure 3 Participant’s Percentile Sum Scores for the Emotional Control Clinical Scale Survey Items 9 8 7 6 5 4 3 2 1 0 15 20 25 31 37 45 49 54 60 67 76 86 88 91 97 98 99 Percentile Score (%) 4.3.1.4 The Self-Monitor Scale Out of the 70 participants, four scored at or above the 95th percentile on the combined sum of the Self-Monitor Scale survey items, accounting for 5.7% of the total participants. The distribution of percentile scores can be found in Figure 4. The Self-Monitor scale evaluates an individual's capacity to monitor their own behaviour and comprehend the impact it may have on others around them (Roth et al., 2005). Poor self-monitoring skills may lead to difficulty conforming to social groups and could be associated with antisocial behaviours (Goldstein & Naglieri, 2013). Number of Participants 49 Figure 4 Participant’s Percentile Sum Scores for the Self-Monitor Clinical Scale Survey Items 20 18 16 14 12 10 8 6 4 2 0 16 20 31 41 51 56 70 83 92 96 99 Percentile Score (%) 4.3.1.5 The Initiate Scale Out of the 70 participants, 13 scored at or above the 95th percentile on the combined sum of the Initiate Scale survey items, accounting for 18.6% of the total participants. The distribution of percentile scores can be found in Figure 5. The Initiate scale assesses an individual's ability to independently begin tasks or activities and generate self-driven ideas and problem-solving strategies. Poor initiation skills may manifest as difficulty starting chores or assignments, and individuals may rely heavily on reminders and prompts to initiate activities. People with poor initiation skills may describe themselves as lazy, procrastinators, or lacking motivation to self- initiate tasks (Roth et al., 2005). Number of Participants 50 Figure 5 Participant’s Percentile Sum Scores for the Initiate Clinical Scale Survey Items 12 10 8 6 4 2 0 7 20 31 44 53 55 66 72 80 88 93 96 98 99 Percentile Scores (%) 4.3.1.6 The Working Memory Scale Out of the 70 participants, 19 scored at or above the 95th percentile on the combined sum of the Working Memory Scale survey items, accounting for 27.1% of the total participants. The distribution of percentile scores can be found in Figure 6. The Working Memory scale assesses an individual's ability to retain relevant information in short-term memory to complete a task or provide an appropriate response. Working memory is also crucial for sustained attention and academic performance in educational settings (Goldstein & Naglieri, 2013). Those with poor working memory skills may struggle to follow complex instructions or directions, forget rules for specific assignments easily, or lose track of the steps required to carry out a task efficiently (Roth et al., 2005). Number of Participants 51 Figure 6 Participant’s Percentile Sum Scores for the Working Memory Clinical Scale Survey Items 10 9 8 7 6 5 4 3 2 1 0 17 22 33 50 59 67 76 81 85 94 95 96 98 99 Percentile Scores (%) 4.3.1.7 The Plan/Organize Scale Out of the 70 participants, 11 scored at or above the 95th percentile on the combined sum of the Plan/Organize scale survey items, accounting for 15.7% of the total participants. The distribution of percentile scores can be found in Figure 7. The Plan/Organize scale assesses an individual's ability to manage present and future tasks by evaluating the skills needed to achieve goals. The Plan component involves anticipating possible future events, setting appropriate steps or milestones, and creating proactive timelines to reach objectives. The Organize component entails arranging relevant information or sequences of actions to achieve goals. Those with poor planning/organization skills may approach assignments or goals in a disorganized or chaotic manner and may feel anxious when faced with complex or large tasks (Roth et al., 2005). Number of Particiapnts 52 Figure 7 Participant’s Percentile Sum Scores for the Plan/Organize Clinical Scale Survey Items 10 9 8 7 6 5 4 3 2 1 0 12 27 32 37 49 58 65 69 75 80 86 94 95 98 99 Percentile Scores (%) 4.3.1.8 The Task Monitor Scale Out of the 70 participants, 26 scored at or above the 95th percentile on the combined sum of the Task Monitor scale survey items, accounting for 37.1% of the total participants. The distribution of percentile scores can be found in Figure 8. The Task Monitor scale evaluates an individual's ability to consciously be aware of their problem-solving skills and strategies and assess how these strategies contribute to task successes and failures. Those with poor task monitoring skills may struggle to evaluate or notice their own errors, make repetitive mistakes while completing tasks of a similar nature, and may encounter difficulties in completing tasks in general (Roth et al., 2005). Number of Participants 53 Figure 8 Participant’s Percentile Sum Scores for the Task-Monitor Clinical Scale Survey Items 16 14 12 10 8 6 4 2 0 12 29 45 56 59 70 84 95 98 99 Percentile Scores (%) 4.3.1.9 The Organization of Materials Scale Out of the 70 participants, four scored at or above the 95th percentile on the combined sum of the Organization of Materials scale survey items, accounting for 5.7% of the total participants. The distribution of percentile scores can be found in Figure 9. The Organization of Materials scale assesses an individual's ability to physically organize materials in their everyday environments, such as at school, home, and work. Those with poor organization of materials skills may struggle with picking up after themselves, frequently misplace or lose items, or live in a constant state of disarray. As a result, they may have difficulty working efficiently as pertinent materials needed to achieve their goals can rarely be found when needed (Goldstein & Naglieri, 2013; Roth et al., 2005). Number of Participants 54 Figure 9 Participant’s Percentile Sum Scores for the Organization of Materials Clinical Scale Survey Item 12 10 8 6 4 2 0 7 17 27 34 38 49 57 64 67 73 82 85 90 97 99 Percentile Scores (%) 4.3.2 BRIEF-A Indexes and Global Executive Composite Scores (GEC) This section will provide detailed information on the two clinical indexes outlined in the BRIEF-A manual, which encompass the nine individual scales described previously. The first index is the BRI, which includes the Inhibit scale, the Shift scale, the Emotional Control scale, and the Self-Monitor scale. The second index is the MI, which includes the Initiate scale, the Working Memory scale, the Plan/Organize scale, the Task Monitor scale, and the Organization of Materials scale. In addition, this section will present the results from the Global Executive Composite (GEC) scores, which represent the total sum of all BRIEF-A survey items and provides an overall score of EFS as a whole. The percentiles for both indexes and the GEC scores will be individually presented in this section of the results chapter. Number of Participants 55 4.3.2.1 The BRI Out of the 70 participants, four scored at or above the 95th percentile on the combined sum of the scale survey items that comprise the BRI, accounting for 5.7% of the total participants. The distribution of percentile scores can be found in Figure 10. The BRI assesses a respondent's overall capacity to maintain regulation and control over their behaviour and emotional responses. Effective behavioural regulation forms the basis for general self-regulation and is crucial for the successful use of metacognitive strategies (Roth et al., 2005). Figure 10 Participant’s Percentile Sum Scores for the BRI Survey Items 6 5 4 3 2 1 0 7 8 12 15 18 20 22 28 35 36 37 41 44 49 53 56 57 63 69 74 76 78 81 85 86 89 92 93 95 96 98 Percentile Scores (%) 4.3.2.2 The MI Out of the 70 participants, nine scored at or above the 95th percentile on the combined sum of scale survey items that comprise the MI, accounting for 12.9% of total participants. The Number of Participants 56 distribution of percentile scores can be found in Figure 11. The MI assesses a respondent's overall capacity to utilize working memory, regulate attention, and systematically problem-solve in various situations by employing skills such as physical and mental planning and organization (Roth et al., 2005). Figure 11 Participant’s Percentile Sum Scores for the Metacognitive Index (MI) Survey Items 6 5 4 3 2 1 0 3 6 10 13 19 24 31 32 35 39 46 56 58 59 63 66 72 74 78 81 83 85 86 88 89 92 93 94 96 97 98 99 Percentile Scores (%) 4.3.2.3 GEC Scores Out of the 70 participants, nine scored at or above the 95th percentile on the combined sum of all survey items that comprise the GEC score, accounting for 12.9% of the total participants. The distribution of percentile scores can be found in Figure 12. The GEC is a self- perceived reflection of a respondent's overall executive functioning skills and serves as a general summary measure for total executive function abilities. Number of Participants 57 Figure 12 Participant’s Percentile Overall Sum Scores for the Global Executive Composite (GEC) 7 6 5 4 3 2 1 0 4 6 7 11 20 22 26 30 33 38 43 44 49 51 54 56 62 65 67 72 74 76 79 82 85 86 88 89 91 93 95 96 Percentile Scores (%) 4.4 Differences Between EFS and Academic Performance 4.4.1 Sex Assigned at Birth This section presents the results addressing the second research question: “Are there significant differences between assigned males’ and assigned females’ self-reported executive function scales, indexes, overall Global Executive Composite (GEC) scores, and/or academic performance measures?” To assess these differences, the Mann-Whitney U test was performed. The analysis included 68 participants for the comparison of EFS scales, indexes, overall GEC scores, 53 participants for the comparison of final course grade percentage, 34 participants for the comparison of 2021/2022 academic year GPA, and 52 participants for the comparison of 2022 fall GPA. Number of Participants 58 From these analyses, the results revealed that only the self-reported scores for the Emotional Control scale showed a significant difference (p<0.050) between the assigned male and assigned female group, with a p-value of 0.028 (Table 3). As shown in Table 4, the Emotional Control Scale mean rank scores indicated higher self-reported scores in the assigned female group (Mean Rank: 38.76) compared to the assigned male group (Mean Rank: 28.04). Table 3 Mann-Whitney U Tests for Differences in EFS and Academic Performance Measures between Assigned Male and Assigned Female Groups EFS Measure Significant (p) n Inhibit .516 68 Shift .904 68 Emotional Control .028* 68 Self-Monitor .798 68 Initiate .930 68 Working Memory .450 68 Plan/Organize .734 68 Task-Monitor .366 68 Organization of Materials .134 68 BRI .233 68 MI .585 68 GEC .430 68 Academic Performance Measure Final Course Grade Percentage .500 53 2021/2022 Academic Year GPA .445 34 2022 Fall Semester GPA .057 52 *Note. Significance level is p<0.050 as measured by an asymptomatic two-sided nonparametric t- test. n represents the number of participants analyzed for each test. 59 Table 4 Mean Rank Scores for EFS and Academic Performance Measures between Assigned Male and Assigned Female Groups EFS Measure Sex Assigned at Birth Assigned Male Assigned Female Inhibit Mean Rank 36.41 33.24 n 27 41 Shift Mean Rank 34.15 34.73 n 27 41 Emotional Control Mean Rank 28.04* 38.76* n 27 41 Self-Monitor Mean Rank 33.76 34.99 n 27 41 Initiate Mean Rank 34.76 34.33 n 27 41 Working Memory Mean Rank 32.28 35.96 n 27 41 Plan/Organize Mean Rank 35.50 33.84 n 27 41 Task-Monitor Mean Rank 31.85 36.24 n 27 41 Organization of Materials Mean Rank 30.09 37.40 n 27 41 BRI Mean Rank 30.98 36.82 n 27 41 MI Mean Rank 32.89 35.56 n 27 41 GEC Mean Rank 32.17 36.04 n 27 41 Academic Performance Measure Final Course Grade Percentage Mean Rank 28.76 25.84 n 21 32 2021/2022 Academic Year GPA Mean Rank 15.40 18.38 n 10 24 2022 Fall Semester GPA Mean Rank 31.55 23.34 n 20 32 *Note. Mean Ranks with asterisks indicate significant differences found between groups. n represents the number of participants analyzed in each group. 4.4.2 Academic Year of Study This section presents the results addressing the third research question: “Are there significant differences between first-year students and upper-level students (including second-, 60 third-, fourth- and fifth-year students) self-reported executive function scales, indexes, overall Global Executive Composite (GEC) scores and/or academic performance measures?” The Mann-Whitney U test was conducted to determine if there were any significant differences between the first-year and upper-year student groups in their self-reported EFS scale scores, index scores, overall GEC scores, final course grade percentage, 2021/2022 academic year GPA, and 2022 fall GPA. A total of 70 participants were included in the analyses for EFS scales, indexes, and GEC scores, 55 participants for final course grade percentage, 36 participants for 2021/2022 academic year GPA, and 54 participants for 2022 fall GPA. The results from these analyses did not show any significant differences (p<0.050) between the first-year and upper-year student groups, as displayed in Table 5. Table 5 Mann-Whitney U Tests for Differences in EFS and Academic Performance Measures Between First Year and Upper Year (Second, Third, Fourth and Fifth Year) Students EFS Measure Significant (p) n Inhibit .638 70 Shift .072 70 Emotional Control .128 70 Self-Monitor .413 70 Initiate .700 70 Working Memory .075 70 Plan/Organize .234 70 Task-Monitor .185 70 Organization of Materials .132 70 BRI .087 70 MI .155 70 GEC .080 70 Academic Performance Measure Final Course Grade Percentage .296 55 2021/2022 Academic Year GPA .985 36 2022 Fall Semester GPA .403 54 *Note. Significance level is p<0.050 as measured by an asymptomatic two-sided nonparametric t- test. n represents the number of participants analyzed for each test. 61 4.5. Relationships between Age, EFS, and Academic Performance This section presents the results addressing the fourth research question: “Are there any relationships between student’s self-reported executive function scores, age, and/or academic performance measures?” Multiple Spearman’s Rank-Order Correlation tests were conducted to explore potential relationships between the variables mentioned previously. The results are presented in Table 6. Among these correlational tests, significant negative correlations, in which lower EFS scores reflect better EFS performance, at the 0.05 level (p<0.050) were found between final course grade percentage and the Working Memory Scale (rs = -.294, p = .030), the Plan/Organization EFS Scale (rs = -.304, p = .024), the Organization of Materials EFS scale (rs = -.317, p = .018), and the Metacognitive Index (MI) (rs = -.278, p = .040). 62 Table 6 Spearman’s Rank-Order Correlation Tests Between Age, EFS Scores, and Academic Performance Measures Measures Age Final Course Grade Percentage 2021/2022 Academic Year GPA 2022 Fall Semester GPA Age Correlation Coefficient 1.000 -.004 .003 .008 Sig. (2-tailed) . .978 .985 .953 n 70 55 36 54 Inhibit Correlation Coefficient -.036 -.071 .036 .025 Sig. (2-tailed) .765 .606 .835 .860 n 70 55 36 54 Shift Correlation Coefficient .062 .077 .166 .053 Sig. (2-tailed) .612 .575 .333 .703 n 70 55 36 54 Emotional Control Correlation Coefficient .035 -.182 -.002 -.014 Sig. (2-tailed) .775 .183 .992 .918 n 70 55 36 54 Self-Monitor Correlation Coefficient -.027 .025 .039 -.081 Sig. (2-tailed) .823 .855 .824 .562 n 70 55 36 54 Initiate Correlation Coefficient -.117 -.176 .131 -.116 Sig. (2-tailed) .337 .200 .445 .402 n 70 55 36 54 Working Memory Correlation Coefficient .021 -.294* -.053 -.186 Sig. (2-tailed) .864 .030 .757 .178 n 70 55 36 54 Plan/Organize Correlation Coefficient .001 -.304* -.065 -.150 Sig. (2-tailed) .994 .024 .709 .279 n 70 55 36 54 Task-Monitor Correlation Coefficient -.033 -.161 -.090 -.122 Sig. (2-tailed) .785 .240 .602 .380 n 70 55 36 54 Organization Of Materials Correlation Coefficient -.033 -.317* .162 -.173 Sig. (2-tailed) .787 .018 .344 .210 n 70 55 36 54 BRI Correlation Coefficient .039 -.065 .068 .001 Sig. (2-tailed) .747 .637 .696 .993 n 70 55 36 54 MI Correlation Coefficient -.039 -.278* .065 -.160 Sig. (2-tailed) .747 .040 .707 .248 n 70 55 36 54 GEC Correlation Coefficient .004 -.232 .056 -.112 Sig. (2-tailed) .974 .089 .748 .420 n 70 55 36 54 *Note. Correlation is significant at the 0.050 level (p<0.050). n represents the number of participants analyzed in each group. 63 4.6. Chapter Summary The results presented in this section contribute to the main goal of this study: exploring potential relationships within and between EFS measures and academic performance, taking into account factors like age, sex assigned at birth, and academic year of study. The key findings are summarized below. The Task Monitor scale and the Working Memory scale were identified as the two scales with the highest percentage of deficits among all BRIEF-A measures in the total sample population, with 37.1% and 27.1% of the participants, respectively, scoring at or above the 95th percentile range. The Mann-Whitney U tests comparing the EFS scores between the assigned males and females showed a significant difference in the Emotional Control scale item scores (p = 0.028). Mean rank scores revealed that assigned females self-reported higher scores (indicating poorer performance) on the Emotional Control scale items compared to the assigned male group. Age, as well as specific scales, including the Inhibit scale, the Shift scale, the Emotional Control scale, the Self-Monitor scale, the Initiate scale, the Task-Monitor scale, the BRI, and the overall GEC scale, did not show any correlations with academic performance measures. None of the scales assessed by the BRIEF-A questionnaire displayed significant correlations with overall GPA measures, encompassing both the 2021/2022 academic year GPA and the 2022 fall GPA. Lastly, multiple Spearman’s Rank-Order Correlation tests were performed to explore potential relationships between age, EFS scores, and academic performance measures. The results showed statistically significant negative correlations between the EFS scales Working Memory, Plan/Organize, Organization of Materials, as well as the Metacognitive Index (MI), and student’s final course grade percentage. In the next chapter, these results will be discussed in 64 relation to relevant literature, findings from previous studies, and their implications within a university context. 65 Chapter 5: Discussion 5.1 Chapter Overview This chapter provides a review of relevant literature and research papers related to the findings, exploring specific Metacognition Index (MI) scales and their connections to academic performance and success among university students. After, there is an examination of literature and research articles investigating the potential physiological and psychosocial factors contributing to statistically significant differences found in the Emotional Control scale between sex-assigned male and female university students. Drawing from the findings of the current research and synthesizing them with existing literature, each main result discusses potential explanations and implications. Next, there are propositions for future research, highlighting the ongoing need for investigations into EFS, their development in university students, and their impact on academic performance and success. There is an acknowledgement of the study’s limitations, including methodological and contextual constraints. The section concludes by synthesizing key insights, underlining the contributions of the research to the broader understanding of executive functioning skills, and summarizing the study’s significance within the existing academic landscape. 5.2 MI Scales and Academic Performance As detailed in Chapter 4.3, an examination of percentile scores distribution among participants highlighted two predominant deficits within the sample population’s EFS. The two scales identified were the Task Monitor scale, which accounted for 37.1% of the total sample reporting deficits within this scale, and the Working Memory Scale, which represented 27.1% of 66 the total sample reporting deficits in this scale. Both of these scales are components of the MI, reflecting a participants’ ability to manage their attention and problem-solving skills (Roth et al., 2005). The Task Monitor scale, as defined in the BRIEF-A manual, assesses an individual’s problem-solving and task-oriented monitoring function. It gauges the extent to which an individual keeps track of their own problem-solving skills. In an academic context, this skill involves students adeptly understanding the strategies employed in problem-solving, comprehending their effectiveness, and making informed judgments about task difficulty. Meticulous task completion, along with efficient task management, and the ability to discern the difficulty of assignments are other examples of how students may employ effective task monitoring abilities in a university context (Roth et al., 2005). The Working Memory Scale, also part of the MI, measures the respondent’s capacity to actively hold information in mind for task completion or generating a response. In an academic setting, this skill could be demonstrated by maintaining attention throughout task completion, recalling information form past assignments, and adding complexity to steps taken when attempting to complete an ongoing task (Roth et al., 2005). Chapter 4 also reveals a significant, negative correlation between the Working Memory scale and final course grade percentage. Thus, a higher deficit in self-reported working memory scores was shown to correspond to lower final course grade percentages. Chapter 4 continued to describe negative correlations found with final course grade percentages with the Plan/Organize scale and the Organization of Materials scale, both also components of the MI. Plan/Organize assesses the ability to manage current and future task demands, including anticipation of future events and creating steps to achieve goals, emphasizing 67 cognitive task-oriented organization elements. The Organization of Materials scale evaluates an adult’s ability to organize their everyday environment, measuring orderliness in work, living, and other personal spaces. This includes a student’s proficiency in managing their physical course materials, such as textbooks and notes, and organizing their physical surroundings and contents (Roth et al., 2005). It is important to address that among the academic performance variables examined, only final course grade percentage correlated with self-reported EFS scales. The other academic performance measures, the 2021/2022 academic year GPA and the 2022 fall GPA were not shown to have any significant correlations with self-reported EFS scales. A possible explanation for these correlations could include that certain academic courses demand specific metacognitive abilities, and the courses included in this study may have unique course competencies that require strong metacognitive skills for students to succeed. Additionally, a GPA calculated across an academic term or year amalgamates performances across a mixture of courses, each potentially requiring distinct executive functioning skills for success. Attempting to correlate GPA with individual EFS skills may be too challenging due to the mixing of academic requirements across courses. Final course grade percentage isolates specific courses, offering insights in the requisite skills for success within particular contexts, such as working memory, planning/organization and organization of materials. 5.2.1 Research Exploring Self-Reported EFS in Students Manuhuwa et al. (2023) investigated 315 first-year psychology students at the University of Applied Sciences in the Netherlands, utilizing the BRIEF-A questionnaire for executive functions and the Motivated Strategies for Learning Questionnaire (MSLQ) for self-regulated 68 learning assessment. The study aimed to explore the connections between executive functions, self-regulated learning, and their combined impact on study success, measured by earned credits after the first year. Findings revealed a weak but significant correlation between six executive functions and study success (ranging from r = −0.12 to r = −0.24). These skills were the Organization of Materials, Task Monitor, Plan/Organize, Working Memory, Initiate, and Inhibit scales, with five falling under the MI and one under the BRI. Overall, the study suggests that students facing more executive challenges tended to earn fewer credits in their initial university year compared to peers with more proficient executive functions. Another investigation by Ramos-Galarza et al. (2020) assessed the correlations between self-reported executive functions and academic performance (quantified by a student’s overall grade mean) through the utilization of the Executive Functioning (EFECO) self-reported scale for 175 students at an university in Ecuador. Through correlational analysis, a medium magnitude negative correlation emerged between academic performance and challenges in working memory (r = −0.30, p < 0.01) and conscious supervision of behaviour (r = −0.29, p < 0.01). Although the term “task-monitor” is not explicitly used in this study, conscious supervision of one’s behaviour implies a method and skillset integral to task monitoring. An ordinal logistic regression analysis was also conducted on these variables and indicated that difficulties in working memory and conscious supervision of behaviour collectively accounted for 31% of the variance observed in academic performance among the university students in their sample. While the present study did not find a correlation with the Task Monitor scale and final course grade percentage, this does align with the result of the Working Memory scale being a large deficit in the student population, as well as a chief contributor to academic success in university studies. 69 Sheehan and Iarocci (2019) conducted a study to examine specific cognitive elements like inattention, hyperactivity and EFS as potential predictors of academic adjustment in 135 undergraduate university students. Academic adjustment was measured using the Student Adaptation to College Questionnaire (SACQ), where students self-reported their overall adjustment to college in both academic and social aspects. The SACQ results have been shown in previous studies to be significantly correlated with student GPA (Credé & Niehorster, 2012). The researchers also employed the BRIEF-A questionnaire to measure self-reported EFS from students. Through hierarchical regression analysis to predict success in academic adjustment, the MI emerged as a crucial predictor (β = -0.22, p < 0.001), surpassing the impact of behavioural regulation or depressive symptoms, which is similar to the results in the present study. Sheehan and Iarocci suggest that an individual’s ability to initiate tasks, effectively use working memory, task-monitor, plan, and organize in various contexts significantly influences the academic adjustment of first-year students. Additionally, Thibodeaux et al. (2017), examined various measures to explore the relationship between self-regulated learning, time use, and academic performance among 589 university students. The researchers utilized surveys and questionnaires to gather data on these factors and the interplay within these variables. The study aimed to examine students reported planned time and actual time used per week on each university course. Results indicated that many first-year students faced challenges with self-regulated learning, often underestimating the time required for academics. These difficulties were found to be significant contributors to a decline in academic performance and GPA compared to their high school achievements. This finding aligns with the correlation identified in the present study, emphasizing the pivotal role 70 that planning and organization play in academic success and underscores the importance of accurate self-assessment and time allocation to university tasks. Baars et al. (2015) conducted a study involving 1320 first-year students who completed the Amsterdam Executive Functioning Inventory (AEFL) to assess perceptions of EFS and first- year success, as measured by credits earned at the end of the academic term. Regression analyses suggested that level of attention (β 0.13, p < 0.000), planning (β 0.15, p < 0.000), and self-control (β 0.06, p < 0.05) were predictive of first-year study success. Similarly, Petersen et al. (2006) examined results from 81 first-year psychology students who completed the Learning and Study Strategies Inventory (LASSI) and the Executive Functioning Rating Scale (EFRS) to explore the relationship between EFS, study strategies, and academic success as measured by high school GPA and final course grades. Findings showed that students who reported problems in executive functioning behaviours had positive correlations with anxiety (r = 0.34, p = 0.005) and negatively correlated with concentration (r = -0.44, p < 0.001), time management (r = -0.39, p < = 0.001), and test taking skills (r = -0.34, p = 0.004). Overall, they also found that students who reported difficulty in EFS had negative correlations with academic performance. 5.2.2 Potential Implications The findings from these studies collectively unravel crucial implications for understanding why university students’ self-report deficits in working memory, planning, and organization are strongly correlated with academic performance. Working memory emerges as a focal point of interest, with findings indicating not only its pivotal role in academic performance and success, but also in the deficits found in university students. Furthermore, other skills encompassed by the MI, including plan/organization and organization of 71 materials, have demonstrated their importance in influencing students’ academic success. These aspects merit careful consideration, given that students themselves acknowledge deficits in these areas through self-reported measures in this current study. Additional research corroborates these findings, revealing that students facing challenges in self-regulated learning, underestimating time requirements, and encountering difficulties in planning and organization tend to exhibit lower academic performance. All findings relate and highly emphasize metacognitive abilities as being crucial to academic success compared to behavioural regulation abilities. University places the responsibility on students to structure their daily academic lives, determining when and how they study new subjects. This contrasts significantly with high school, where students are often guided on how to prepare for each class through assigned homework, note packages, etc. For some students, this transition can be daunting, particularly if they’ve moved away from parents who helped with daily chores and aspects of living. Students may struggle due to a lack of organization and planning skills, learning to use this metacognitive skillset for potentially the first time. Moreover, navigating a new academic environment can be overwhelming, especially if metacognitive skills are not well-developed. Some students may find it challenging or overlook the importance of tracking their progress or reflecting on the strategies and tools used in a project, potentially contributing to self-reported deficits in the MI. The significance of planning and organization in university success becomes even more pronounced when considering the multifaceted nature of academic responsibilities. University courses often involve a higher level of complexity, requiring students to manage multiple assignments, deadlines, and projects simultaneously. Those who lack effective planning and organizational skills may find it difficult to juggle these demands, resulting in missed deadlines, increased stress, and ultimately, reflect a negative impact on academic performance. It 72 underscores the importance of accurate self-assessment and time allocation, important elements of self-regulation which includes many of these metacognitive abilities. Students with low working memory capabilities may also encounter challenges in processing and retaining information essential for academic tasks. This could hinder their ability to follow lectures, understand complex concepts, and efficiently complete assignments. Consequently, as these students might struggle to meet the demands of university coursework, it could lead to lower academic performance. Developing and honing these skills could be crucial for students to navigate the intricate academic landscape successfully. 5.3 Sex Assigned at Birth and Emotional Control An additional aim of this study was to uncover potential differences in self-reported EFS between sex assigned males and sex assigned females. In the analysis of the relevant data between assigned sexes, it was found that only the self-reported scores on the Emotional Control scale from the BRIEF-A questionnaire showed a significant difference between the assigned male and assigned female groups. The assigned female group exhibited higher self-reported scores on the Emotional Control scale than the assigned male group, with a statistically significant p-value of 0.028. As a higher score on the BRIEF-A questionnaire indicates greater executive deficits compared to lower scores on this questionnaire, this result indicates that assigned males self-report better emotional control skills compared to assigned females. While there was a significant difference between assigned males and assigned females emotional control scores, only 4.3% of the total participant sample were flagged for executive deficits in this scale. Additionally, the Emotional Control scales was not shown to be correlated with any academic performance measures. Therefore, this section will focus solely on why 73 differences may be present between assigned males and females in the reporting for the Emotional Control scale items, and how it may affect other areas of university life, such as well- being. The nature of sex assigned at birth having significant difference in self-reported emotional control scores may be understood from different perspectives, including physiological and psychosocial. 5.3.1 Physiological Factors The observed variation of emotional control between assigned at birth sexes has been previously studied and researched within the neuroscientific field. Please note that the referenced studies did not specify if the reported participants identified with their assigned sex or gender, referring them only as males/men or females/women. Consequently, in discussing these studies, they will be referred to in these terms, as it remains unclear whether the participants are those assigned their sex at birth or those identifying with a particular gender. In their 2023 study, Langer et al. investigated emotional regulation across genders through an experiment. Participants, both male and female, were subjected to the Socially Evaluated Cold-Pressor Test (SECPT), where half immersed their hands in near-freezing water, while the others kept their hands in warm water. All participants maintained this for three minutes. Post-SECPT, they were exposed to emotionally neutral and negative photos, with instructions to either observe or employ specific strategies: reappraisal (altering their interpretation of the images) or distraction (thinking about something else). Afterwards, participants rated their emotional responses. Results indicated that men exhibited enhanced emotional regulation under stress when using distraction. Men also demonstrated elevated cortisol levels compared to women, 74 facilitating increased energy and focus, particularly when exposed to negative images. There was shown to be an opposite effect when negative images were presented to naturally cycling women who were tested during the mid-luteal phase of their menstrual cycle. During this phase, when progesterone levels are high and estradiol levels are moderate, there may be reduced sensitivity and binding capacity of glucocorticoid receptors to cortisol due to elevated progesterone. As cortisol plays a large role in dealing with stress and moderating emotional reactions, there may be an increase in the brain's amygdala response to threatening stimuli due to progesterone levels. Additionally, Langer et al. states that women tend to be more emotionally reactive, with heightened activation of the locus coeruleus (LC) arousal system following stress, compared to men. The LC arousal system releases norepinephrine, inducing a "fight or flight" response, enhancing alertness. Women also exhibited greater cardiovascular reactivity (increased heart rate, blood pressure), contributing to heightened emotional arousal and potentially hindering effective use of emotional regulation strategies. In another recent study conducted by Tomasino et al. (2022), functional MRI scans were used to investigate potential differences in the neural circuits associated with emotional imagery reactions between males and females. Emotional imagery involves mentally visualizing or imagining emotionally charged situations or scenarios. This process of envisioning emotional states in response to hypothetical situations allows observation of participants’ abilities to control and regulate their emotions. The right putamen is a key component of the neural circuitry related to emotional experiences particularly in the domains of reward processing and emotion regulation. The findings of this experiment revealed that females exhibited higher activation in the right putamen compared to males. This heightened activation in the right putamen suggests that when the females engaged in the study, they experienced more intense emotional 75 involvement. This greater activation could indicate that females had more vivid emotional imagery and responses compared their male counterparts in this study. This showcased that potentially female’s emotional responses were more pronounced and involved during these emotional tasks, which could result in emotional control being more challenging for females due to an exacerbated response in the right putamen, making the regulation of emotions more demanding in comparison to males. Existing research on disparities in brain anatomy and physiology between sex assigned males and females has been notable in lacking compelling and accurate findings. A meta- synthesis conducted by Eliot et al. (2021), encompassing over 300 articles spanning the last three decades, states that there are minimal differences in the brain based on sex/gender and insufficient evidence to assert that there are distinct variations in the structure of the brain based on sex assigned at birth. However, Hirnstein and Hausamann (2021) present an alternative perspective in their commentary on Eliot et al.’s meta-analysis. They agree that differences related to sex/gender in the brain do not necessarily translate to significant behavioural distinctions. They use the example of males having indisputably larger brain than females, yet this size disparity does not appear to affect IQ between sexes. Conversely, however, they argue that while many studies cited in Eliot et al.’s work show small effect sizes in anatomical differences in brain structures between sexes, these could still have behavioural consequences. For example, there is a 1-5% size difference in the amygdala or hippocampus between sexes across studies. Although a single memory test will likely reveal negligible differences between assigned males and females, these brain structures, crucial to processing emotional memories, could lead to profound behavioural distinctions throughout a lifetime. Both researchers conclude that discussions regarding brain differences between assigned sexes should include not only the 76 biological function and structure of the brain, but also psychological and sociological evidence to paint a comprehensive picture. These additional factors will be further explored in the upcoming section. 5.3.2 Psychosocial factors The intricate interplay of psychosocial factors adds another layer to the differences found in how assigned males and assigned females perceive emotional control. This section provides a brief overview of gender-specific socialization processes, societal expectations, and cultural norms, examining their influences on patterns of perceived emotional control. Beyond individual cognitive processes, this exploration broadens to encompass wider psychosocial dynamics, taking into account familial influences, peer interactions, and societal stigmas. Prentice et al. (2022) discusses relevant literature and explores potential explanations in their review of gender differences in emotional processing with a focus on interoception. Interoception refers to an individual’s capacity to process internal bodily sensations, such as an elevated breathing rate or increased heart rate. Proficient interoception involves accurately perceiving bodily processes, consistently focusing on and paying attention to them, and labelling what these sensations mean. The discussed literature in this article highlights gender differences in interoceptive accuracy. Generally, women tend to exhibit lower interoceptive accuracy compared to men. However, women often demonstrate distinct advantages over men in the processing and recognition of emotions. Additionally, in a meta-analysis by Prentice and Murphy (2022), which reviewed thousands of studies assessing sex differences in interoception specific to cardiovascular changes, their results revealed that males exhibited greater interoceptive accuracy in tasks related to heartbeat detection and discrimination compared to females. 77 Prentice and Murphy reference Pennebaker and Roberts (1992) Theory of Gender Differences in Emotion as a basis for these findings. This theory proposes that men and women may rely on different cues in order to gauge their internal states. Men may use internal physiological cues, while women may rely on external situational cues to dictate how they perceive their emotions during an event. Pennebaker and Robert’s also propose that men are more physiologically reactive than females, potentially supporting males having stronger interoceptive signals and accuracy. They also suggest that girls’ experiences with menstruation and societal attitudes may lead to mistrust of their internal signals, and due to historical imbalances, women have adopted a skillset that relies on the perception of social and emotional cues as a survival mechanism. Differences in language socialisation may be an additional contributor to variations in interoceptions skills found between males and females (Prentice et al., 2022). Individuals subconsciously decide whether a physiological change is purely interoceptive or emotional, influencing their identification of the cause. The hypothesis of language socialisation suggests that men and women have distinct biases in interpretating internal states of change. Parental figures tend to use emotional interpretations for girls and physiological interpretations for boys when either display signs of distress. That is, when infant boys cry out, primary caregiver tend to associate it is because of pain or hunger, versus when infant girls cry out, primary caregivers tend to associate it with emotionality. Caregivers also tend to use more emotional language when speaking comforting their daughters compared to sons. Because of this, women tend to use more emotional language and display greater understanding of complex emotions compared to men. Based on these theories, sex assigned females may interpret majority of their changes of internal states as emotional versus sex assigned males, impacting how they report and interpret 78 their emotional control. As assigned males have been shown to have more defined understandings of physiological sensations, they may be more likely to attribute emotional experiences to physiological causes, portraying and reporting better emotional control. Understanding the differences in self-reported emotional control between male and female university students has practical implications for both students and the academic community. For students, awareness of these variations may help in self-reflection and personal growth. Recognizing potential differences in emotional regulation and expression can guide individuals in seeking appropriate support and resources, such as counseling or stress management programs, to enhance their executive functioning abilities. Last, Cherry and Gerstein (2021) conducted a review focused on gender rather than assigned at birth sex, emphasizing the socialization of emotions and masculinity within the United States. They discuss variations of parent-child interactions, exploring how gender norms shape emotional expression. The review suggests that parents’ responses to their children may be influenced by societal expectations, linking emotions like sadness to femininity and anger to masculinity. When children expressed emotions, mothers tended to discuss emotional states with their daughters, while with sons, they focused more on discussing the causes and consequences of their emotional reactions. Fathers, influenced by views on masculinity, interacted different with sons and daughters, paying more attention to daughters’ sadness and anxiety, but primarily focusing on son’s expressions of anger only. However, these patterns can vary based on cultural and contextual factors, emphasizing the need to consider individual beliefs about masculinity, gender roles, emotion, and parenting in understanding how children's emotions are socialized. Overall, this could lead to differences in an individual’s ability to perceive and express emotion, 79 another potential factor in differences found between sex assigned males and females self- reported scores on emotional control. 5.3.3 Potential Implications In conjunction with the present study’s results and the findings from the aforementioned articles, there are potential physiological and psychosocial explanations for the reported differences in emotional control abilities between assigned males and assigned females. From the physiological perspective, assigned males and females may rely on distinct neuroendocrine pathways for emotional regulation, particularly in stressful situations which could occur in higher education. This could result in varying impacts of stress on self-perceived emotional control abilities, potentially contributing to differences in reported emotional control abilities between these two groups. Assigned males may employ effective coping mechanisms, such as focusing on alternative thoughts or activities to manage their stress, whereas females might experience different emotional responses to academic stress specific to differences in hormonal influences. In the context of assigned males reporting better emotional control, it could be argued that the tendency to employ effective coping mechanisms, like distraction, contributes to their perceived sense of better emotional control. Assigned females alternatively, may feel a lack of emotional control, due to potential hormonal and neuroendocrine pathways that elicit a greater cardiovascular reactivity response following stress. Additionally, females were shown to have higher activation of the right putamen, a structure in the brain associated with emotional processing and integration, when presented with emotional images and situations compared to males. This could mean that when assigned males and females are presented with a similar stressor, such as an upcoming final exam, assigned 80 females more vividly visualize the upcoming event, and could trigger distinct emotional imagery responses compared to assigned males. Coupled with the findings in the literature surrounding coping strategies, assigned females may feel they have less emotional control than assigned males. When analyzing the psychosocial perspective, relevant literature has suggested that males were shown to have better interoception compared to females. The heightened interoceptive accuracy observed in males could suggest that they have a more finely tuned awareness and regulation of their physiological responses, potentially contributing to their perceived better emotional control skills. Pennebaker and Roberts' Theory of Gender Differences in Emotion (1992), proposing that men and women may rely on different cues to gauge their internal states, provides insights into potential explanations for why assigned males in a university setting might self-report better emotional control skills compared to assigned females on the BRIEF-A questionnaire. If, as suggested by the theory, men rely more on internal physiological cues and are more physiologically reactive, it could imply that males have a heightened ability to recognize and regulate their physiological responses. This may contribute to assigned males' perception of better emotional control skills, as they may perceive outbursts occur less frequently due to acute regulation of internal body changes. Moreover, the theory suggests that societal attitudes may have led women to adopt taking emotional cues from the environment they are in, as opposed to being in-tune with their internal physiology. This could result in differences in how assigned males and females navigate and express their emotions in a university context, and therefore on a self-reported survey. Regarding stressful times during university, if assigned males indeed possess stronger interoceptive signals and accuracy, it may contribute to their success in managing stress. The ability to effectively regulate emotions, especially during challenging 81 situations, could contribute positively to academic performance and overall well-being. This could potentially explain why assigned males might self-report better emotional control skills during stressful times in a university setting. In conjunction with this, Cherry and Gerstein’s (2021) review of language socialization by parents could also shed light on why assigned males and assigned females may report significantly different emotional control skills. As discussed previously, this review showed across studies that when daughters and sons expressed emotions, parents responded differently. Generally, for females, parental figures tended to focus on discussing the emotional states daughters went through, including having open conversations surrounding a variety of emotions, but specifically sadness and anxiety. For males, parental figures tended to focus on the causes leading up to the emotional outburst, as well as what the consequences could be for these emotions, specifically anger. There are ten total BRIEF-A questions evaluating emotional control. Eight of these questions focus on how intense or frequent emotional responses occur in general, and if they are out of proportion compared to others around them. Examples of these questions include prompts asking if the participants mood changes frequently or if they feel their emotional outbursts occur for little reason. Two questions specifically ask about the intensity and quickness to angry outbursts. There are no other questions pertaining to other emotions in the BRIEF-A questionnaire. When considering the BRIEF-A questionnaire's emphasis on emotional control, particularly regarding anger-related responses, these gendered language socialization patterns could contribute to variations in self-reported emotional control skills. Assigned males, who may have been socialized to understand and manage anger more explicitly, might perceive and report better emotional control in situations specifically related to anger. In contrast, assigned females, 82 who may have been encouraged to openly discuss a broader range of emotions, might not perceive the same level of control in situations specific to anger, leading to differences in self- reported emotional control skills on the BRIEF-A questionnaire. 5.4. Future Research and Practical Implementations The absence of statistical differences or correlations between academic year of study and age with EFS measures or academic performance in this study suggests that these demographic factors may not strongly influence the self-reported results of EFS within the specific population examined. The perspectives of Vygotsky (1962) and Luria (1980) emphasize the importance of considering sociocultural influences and the dynamic nature of brain development in shaping cognitive development. This finding could continue to underscore the complexity of individual differences in EFS, suggesting that factors beyond chronological age or academic progression may play a more significant role. Individual variations in EFS and academic performance may be influenced by a multitude of factors, such as cognitive abilities, environmental influences, study habits, or levels of motivation. Additionally, the data in this study did reveal an uneven distribution of ages and academic year of study groups in the sample. This may have also influenced the ability to detect significant differences or correlations accurately for age and academic year of study. Future research studies should aim for a more robust sample population with a balanced distribution across the measured age range and academic year of study groups to ensure the reliability and generalizability of findings. Furthermore, future research in measuring EFS within the university student population could include various dimensions, building on the current findings provided by existing studies. There is an emphasis on metacognitive aspects within EFS and their impact on academic success 83 warrants further exploration. Working memory stands out as a key focus, consistently proving its pivotal role in academic performance. Future research could investigate specific interventions or training programs targeting working memory enhancement to understand their efficacy in improving academic outcomes. Expanding beyond working memory, research could be directed towards a broader approach in specifically focusing on the skills encapsulated by the MI. For example, the scales of working memory, plan/organization, and organization of materials have been identified as crucial contributors to academic success and could be specific focal points for future research projects. Investigating interventions or support systems that address deficits in these areas could be valuable for universities aiming to enhance the overall executive functioning skills of their students. Investing in developing strategies to enhance these metacognitive skills could prove instrumental in fostering better academic outcomes. This could take the form of incorporating metacognitive scaffolding in curriculum itself, or investing in executive functioning experts to provide supports and resources on campus. Further exploration into the developmental aspects of these skills during the transition from high school to university may also shed light on why deficits emerge and how they can be proactively addressed. Considering the cultural and contextual factors influencing students' metacognitive abilities can contribute to tailored interventions for diverse student populations, enriching our understanding of executive functioning skills in the university setting and informing targeted interventions to enhance academic success. Additionally, there is an opportunity to explore the intersectionality of EFS and emotional control. If replication studies continue to report differences in emotional control abilities between assigned males and females, future research could investigate how executive functioning interventions or support programs might influence emotional regulation and the overall well-being of university students. Understanding the interplay between executive functioning and 84 emotional control could provide comprehensive insights for university educators and mental health professionals. While this study illuminates’ sex-assigned differences in self-reported Emotional Control, future research could be centred around understanding the underlying mechanisms driving these disparities. This could involve more extensive and diverse samples, including various age groups, ethnic backgrounds, and cultural contexts within a university population. Qualitative research methods, such as interviews and focus groups, can provide deeper insights into how societal norms and expectations influence self-reported executive functioning scores. Additionally, exploring the impact of interventions, like emotional intelligence training, on self- reported executive functioning abilities could offer practical insights into improving these skills among university students. Longitudinal studies could assess whether changes occur over time and whether interventions are effective in closing gender-related gaps. 5.5. Limitations The current study is subject to several limitations that warrant consideration. One notable limitation involves the use of self-reported data to assess EFS through the BRIEF-A questionnaire. This method, while commonly and extensively used, is not a direct physiological measure of EFS. Therefore, there could be potential inaccuracies and bias in the reporting of these skills. An example could include social desirability bias, where participants may exaggerate or modify their responses to align with perceived societal expectations, which could pose a risk to the validity of the obtained data. Furthermore, the study's participant pool consists of students from specific university course disciplines, restricting the generalizability of findings to a broader student population. The 85 inherent diversity across academic disciplines may influence the manifestation of executive functioning skills, and caution should be taken when interpreting these results. A contextual limitation arises from the composition of the study's participants, who are exclusively enrolled in summer and fall semester courses. The distinct characteristics of students in summer semesters, including those who may have previously failed a course or are fast-tracking their education, introduce a potential source of bias. Generalizing the study's outcomes solely to students in the summer semesters may not accurately represent the broader student body enrolled during the fall and winter terms. The current study did not include information about a participants' gender identity as a variable. Initially, the study design prioritized a biological and physiological perspective on EFS development and PFC maturation, thus focusing solely on sex assigned at birth as a biological marker. However, as the project evolved, it became apparent that this approach was limited. Recognizing the importance of sociological and cultural perspectives in understanding EFS development, subsequent discussions of the findings integrated these perspectives. It's crucial to note that this decision was not intended to disregard the significance of gender identity narratives or overlook the diverse experiences related to gender. Moving forward, research endeavors exploring EFS and student experiences should incorporate both sex assigned at birth and gender identity inquiries to provide a comprehensive understanding of participants' backgrounds. Nevertheless, it's important to acknowledge the limitations regarding gender discussion in this study. Without data on the distribution of participants' gender identities, the study lacks direct insights into gender effects. However, discussions on psychosocial aspects remain relevant to the study's framework and are included in this context. 86 This study also does not conduct an examination of various external variables that could impact the PFC development and, consequently, executive functioning. Factors such as nutrition, prenatal care, pre-existing medical conditions, medication history, and cultural expectations were not considered. Omitting these variables limits the study's ability to establish a comprehensive understanding of the relationship between age, sex assigned at birth, and EFS. Lastly, the limited availability of prior research studies on the topic of testing undergraduate students' EFS may impact the credibility of this research. The absence of a robust body of literature limits to ability to relate findings to previous studies and to draw important implications and interventions. Researchers should acknowledge this gap and recognize the need for further exploration and validation in subsequent studies to strengthen the foundations of knowledge in this area. 5.6 Conclusion The exploration into the intricate dynamics of EFS and their interplay with academic performance has provided a nuanced understanding into the university student experience. Through the use of the BRIEF-A, this study was able to suggest potential deficits in key metacognitive aspects, notably the Working Memory scale. In addition to the Working Memory scale, the Plan/Organization scale and Organization of Materials scale emerged as contributing skills to success for specific courses. The study's emphasis on metacognitive elements, encapsulated by the MI, has broad implications for understanding how university students navigate the challenges of higher education. Working memory, plan/organization, and organization of materials could be pivotal contributors to success in specific courses, and upon further research, the relevance of these 87 metacognitive aspects could extend beyond academic success. As suggested by previous studies, these variables could have pivotal influences on academic adjustment, self-regulated learning, and time management. Additionally, this study highlighted significant differences in self-reported emotional control skills between sex assigned males and females. Physiological factors, including neuroendocrine pathways and brain activation patterns, provided insights into potential mechanisms shaping these disparities. Psychosocial factors, such as language socialization and parent-child interactions, further contributed to variations in how individuals perceive and express emotions, particularly during stressful academic periods. Recognizing the practical implications of these findings, suggested interventions could include targeting deficits in working memory and metacognitive skills. More importantly, there should be a call to future research investigating metacognition specifically to validate these results. However, it is essential to acknowledge the study's limitations for a nuanced interpretation of its results. The reliance on self-reported data and the limited generalizability to specific disciplines and summer semesters necessitate caution. Furthermore, the absence of an examination of external variables impacting prefrontal cortex development emphasizes the need for comprehensive exploration. The limited prior research on undergraduate students' EFS also suggests the importance of further investigations. As mentioned previously, Lev Vygotsky (1962) and Alexander Luria (1980) contributed ideas and theories that highlight the interplay between biological maturation and socio-cultural stimuli in shaping human cortical abilities. This framework is the guiding principle for this research, showcasing the multifaceted nature of EFS in the university student population. This further emphasizes a holistic approach to supporting students in their academic endeavors. The 88 implications extend beyond the academic realm, encompassing other important factors like personal development and well-being, calling for tailored interventions and continued exploration in this field. 89 References Almoslamani, Y. (2022). 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Thesis/Dissertation
eng
Education
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Arthur, Peter J.
Campbell, Robert
Inside the student mind : an exploration of self-reported executive functioning skills in university students
Text