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Constructing knowledge with digital technology : how high school science students can learn about unobservable… Trey, Svetlana 2006

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CONSTRUCTING K N O W L E D G E WITH DIGITAL T E C H N O L O G Y : HOW HIGH SCHOOL SCIENCE STUDENTS C A N L E A R N A B O U T U N O B S E R V A B L E P H E N O M E N A USING D Y N A M I C SIMULATED ANALOGIES by S V E T L A N A T R E Y A THESIS SUBMITTED IN PARTIAL F U L F I L L M E N T OF THE REQUIREMENTS FOR THE DEGREE OF M A S T E R OF ARTS in THE F A C U L T Y OF G R A D U A T E STUDIES ( Curriculum Studies ) THE UNIVERSITY OF BRITISH C O L U M B I A August 2006 © Svetlana Trey, 2006 Abstract Computer applications in the classroom are increasingly becoming effective tools for model-based teaching in science. A novel instructional computer simulation that incorporates a dynamic analogy to represent Le Chatelier's Principle was designed for this study. The simulation provides an analogy to a chemical reaction, a scale, and links the analogy's functionality to a chemical reaction. This connection where the analogy view is mapped to the components of a chemical reaction is hypothesized to be beneficial for visualization of students' conceptual models of molecular interactions. Two study groups of 12 th grade Chemistry students interacted with instructional computer simulation during the study. Both groups did the same simulation activities guided by a common set of guidance sheets. The difference between the two treatment conditions was that while one of the groups observed the analogical example in the analogy view window of the analogous simulation, the other group had to recall the analogical example. The analysis of the data suggested that analogies that are dynamic, interactive, and integrated in instructional computer simulation have a stronger effect on learning outcomes than analogies which are presented in the form of text and static pictures. The implication of the study is that educators may wish to consider model-based teaching via instructional computer simulations integrating an analogical view as a method to improve student learning. iii T A B L E OF CONTENTS Abstract ii Table of Contents . i i i List of Tables v List of Figures y i 1.0 Introduction 1 2.0 Rationale 2 3.0 Research Objectives 3 4.0 Research Questions 3 5.0 Null Hypotheses 3 6.0 Literature Review and Theoretical Framework. 4 6.1 Literature Review 4 6.1a The role of analogies for improving achievement in science classroom 4 6.1b Model-based instructions 11 6.2 Theoretical Framework 15 6.2a Model building 15 6.2b Mental imagery construction in the learning process. Terms definition 16 6.2c Mental imagery construction in the learning process 18 6.2d The role of multimedia in meaningful learning 22 6.3 Summary ••• 23 7.0 Software Design and Development 25 7.1 The TEMBS project 25 7.2 TEMBS Computer Simulation 27 8.0 Research Methodology 29 8.1 Introduction 29 8.2 Research Design 30 8.3 Population/Sample 31 8.4 Instrumentation 31 8.5 Procedures 35 8.6 Data Collection 46 8.7 Data Analysis..... 46 iv 8.8 Ethical considerations 50 9.0 Results and Discussion 51 9.1 Introduction 51 9.2 The analysis of the relationship of the types of instructional computer simulations and levels of achievement (Post-test total scores) 51 9.3 The analysis of symbolic, graphical and model-drawing conceptions (Three types of post-test questions formats) 59 9.4 The analysis of student's perception of the different types of instructional approach in the chemistry classrooms (Surveys) ; 63 10.0 Conclusion 71 11.0 Implications 74 12.0 Recommendations for Future Research 75 References 77 Appendices 82 A. TEMBS POST TEST 82 B. TEMBS POST TEST Marking Scheme 86 C. SIMULATION ACTIVITY #1 91 D. SIMULATION ACTIVITY #2 96 E. A N A L O G Y IN T E X T A N D DIAGRAMS INSTRUCTIONS 101 F. S U R V E Y 102 G. S U R V E Y RESULTS (INDEPENDENT S A M P L E TEST) 104 H. S U R V E Y RESULTS (GROUP STATISTICS) 107 I. S U R V E Y RESULTS (FREQUENCY TABLES) 109 J. TEMBS POST-TEST RESULTS (Grader 1) 116 K. TEMBS POST-TEST RESULTS (Grader 2) 117 L. QUESTION-ANSWER SIMULATION M O D U L E 118 V L I S T O F T A B L E S : Table 1. Symbolic formula understanding questions 33 Table 2. Graphs interpretation abilities question 33 Table 3. Structure of the study 36 Table 4. Five-Step Strategy 38 Table 5. Instructional analogies employed in the study 40 Table 6. Simulation Activities instructional sequence 44 Table 7. Correlations of the test scores between two graders 46 Table 8. Distribution of Learning Outcomes in Two Factor A N O V A Analysis 49 Table 9. Distribution of Learning Outcomes According to Three Question Types 50 Table 10. Post-test Results (Group Descriptive Statistics) 52 Table 11. Post-test Results (ANOVA: Test of Between-Subjects effects) 53 Table 12. Post-test Results (Distribution of the Means of the Total Scores in Two Groups according to the Prior Level of Achievement) 53 Table 13. Three Types of Post-test Questions 59 Table 14. Means of Post-test Scores According to Three Question Types 60 Table 15. Post-test Scores According to Three Question Types (Group Descriptive Statistics) 60 Table 16a. Survey Results Part 1 (Questions #1 - #9) 64 Table 16b. Survey Results Part 2 (Questions # 10 - # 18) 64 Table 17. Question #19 Results (statistical summary) 70 vi LIST O F FIGURES: Figure 1. Analogical Reasoning 19 Figure 2. Conceptual Change in Cognitive Process 20 Figure 3. Dynamic Mental Imagery Construction in Model-Based Reasoning 21 Figure 4. Simulation overview 28 Figure 5. Analogy in simulation 29 Figure 6a. The analogy view window 37 Figure 6b. The analogy view window disabled 37 Figure 7. The molecular view window 38 Figure 8. Multiple choice displays in the Graph view and in the Prediction mechanism 41 Figure 9. Example of a student's post-test answer for question 4b 47 ' Figure 10. Estimated Marginal Means of the Total Score in Two Groups 54 Figure 11. Survey Results (Frequency Bar Charts) 65 Figure 12. Question # 19 Results (graphs) 71 1 1.0 Introduction It is essential to examine the teaching strategies that encourage students' conceptual understanding of the way the world works (Gobert & Buckley, 2002). Researchers have found that using computer simulations may afford opportunities to promote student understanding of unobservable phenomena in science (Khan, 2002; de Jong, Martin, Zamarro, Esquembre, Swaak, & van Joolingen, 1999; Stratford, 1997,). Le Chatelier's Principle is considered one of the most difficult topics for students in high school chemistry (Huddle & Pillay, 1996). Le Chatelier's Principle states that i f a closed system at equilibrium is subjected to a change, processes will occur that tend to counteract that change (Hebden, 1998). One of the reasons for students' difficulties in understanding Le Chatelier's Principle is that while they are able to make observations of chemical processes at the macroscopic level, such as observing a colour change, they may not be able to state why these changes occur at a molecular level because they are unobservable (Harrison, & De Jong, 2005; Wu, Krajcik & Soloway, 2001; Gabel, 1998). Recently, the effectiveness of pedagogical strategies based on model-based teaching has been demonstrated in research studies (Khan, 2002; Kozma, 2000; Hansen, Narayanan & Hegarty, 2002). A model-based pedagogical approach in science promotes learning by fostering student construction and evaluation of mental models that are internal cognitive representations (Khan, 2002). Furthermore, researchers have found that using computer simulations may afford opportunities to promote students' understanding of unobservable phenomena in science (Khan, 2002; de Jong et al., 1999; Stratford, 1997). Computer simulations, in particular, have the capability to visually and dynamically run models at multiple levels and under different conditions (Khan, in press). The use of computer simulations provides opportunities to make abstract and unobservable concepts visualizable by facilitating the representation of students' 2 knowledge and scaffolding their learning processes (Gobert, Snyder & Houghton, 2002). According to Stagers and Norcio (1993), mental model development is based on analogical or metaphorical reasoning. However, an analogical view integrated in a computer simulation, one capable of fostering student understanding of the connection between molecular and macromolecular phenomena, has yet to be studied. 2.0 Rationale Else, Clement and Ramirez (2003) suggest that analogies can allow the learner to build on relationships within prior knowledge. Moreover, according to Clement and Steinberg (2002), some of these relationships may be represented in concrete images or simulations to enhance visual perceptions of the phenomena. Mental model development is based on reasoning, where learners generalize existing mental models to new phenomena through a process of mapping the old structural relations onto the new (Gentner & Gentner, 1983) using analogies. The proposed analogical view component of the simulation presents chemical equilibrium simulation in terms of a weighing scale analogy, where familiar objects represent reactants on one side of the scale and products on the other. The analogy demonstrates how the reaction rates are balanced and how both reactants and products are still present at equilibrium. Scale mechanics have also been observed by students in chemistry labs and daily life, and this previous knowledge can act as an initial model for students. The interaction between the scale and the scale's components are synchronized with the chemical reaction engine in the simulation to ensure that the mapping between the analogy and the chemical concentrations is more explicitly coupled. The Graph view illustrates concentrations for each chemical over time. Synchronization of the analogy and the chemical concentrations in a graph view fosters students' understanding of the unobservable underlying chemical process by creating a visual representation of the chemical equilibrium process by means of analogy. It has been observed that analogies are frequently used by students and teachers when describing difficult topics (Khan, 2004; Nashon, 2003; Else, Clement, & Ramirez, 2003). Therefore, it is hypothesized that an analogical view in a simulation encourages students to visualize concepts. 3.0 Research Objectives The objective of the present study is to investigate how classroom activities and interactions with a computer simulation contribute to students' understanding of unobservable processes in science. Specifically, the problem of the study is to determine the extent to which the analogical view in a novel computer simulation unit designed for modeling Le Chatelier's Principle has an impact on learning outcomes among high school students. 4.0 Research Questions 1. Are the learning outcomes for 12th -grade chemistry students affected by instructions involving the use of analogical or non-analogical computer simulations? 2. Do instructions involving the use of analogical or non-analogical computer simulations benefit both high and low achieving secondary students in chemistry equally? 5.0 Null Hypotheses 1. Ho: There is no significant difference in the learning outcomes for 12 th -grade chemistry students who are instructed using either analogical or non-analogical computer simulations modeling Le Chatelier's Principle. 2. Ho : There is no significant difference in the learning outcomes for 12th -grade chemistry students who possess high and low prior achievement in the Chemistry 12 course and who are instructed using either analogical or non-analogical computer simulations modeling Le Chatelier's Principle. 4 6.0 Literature Review and Theoretical Framework 6.1 Literature Review In the section that follows, I review and discuss research relevant to my proposed study which is largely about model-building instructional approaches that utilize analogies in the modern science classroom. In this review, attention will be given to studies that reported student achievement as an outcome. 6. la The role of analogies for improving achievement in the science classroom Numerous studies including Gabel and Sherwood (1980), Huddle and White (2000), and Else, Clement and Ramirez (2003) have determined that analogies can assist students in developing a deeper understanding of abstract concepts (i.e. unobservable phenomena in science) that may be inaccessible through student reasoning (i.e. making logical inferences from observed events), prior knowledge, or direct experience. Gabel et al. (1980) conducted a study to determine whether the use of analogies over a wide range of topics in high-school chemistry classes would improve student achievement. The students were enrolled in nine chemistry classes taught by three different teachers in three different schools in central Indiana, USA, during 1977-1978. Three assessments (assessment of the level of logical thinking, assessment of learning, assessment of familiarity with unit analogies) were conducted, and three effects (effect of instruction, effect on cognitive level, chemistry achievement according to analogy comprehension) were analyzed. The analysis of data for the instructional effect of analogies, where the additional practice problems were compared to the use of analogies, showed that the additional practice problems caused greater beneficial outcomes. The researchers did not find a significant difference for the effect of analogies at a cognitive level. However, the chemistry achievement data brought 5 surprising results - 48% of the students did not understand 90% of the analogies. Such a discovery revealed the limitations of the above study. In order to determine or evaluate the effect of using analogies, it would be necessary to consider data for students who understood the analogies. The analysis of data could reveal different results i f it was separated into the groups whether students understood analogies or not. In addition, it would be important to understand the connection between the type of analogy (simple or complex), the type of instructions (such as written assignments and textbook readings) and student understanding. Nevertheless, the analysis of data for the instructional effect of analogies on students who understood the analogies was not done in the Gabel et al. (1980) study. However, another interesting trend was discussed by the researchers, that is, there were some indications that there may be differential effects for students at different developmental levels. Students who scored lower on the test for logical thinking benefited more from analogies than did students at higher stages of developmental levels. One of the reasons proposed by the authors for such an effect was that for students who were already able to think at the formal level (i.e. abstract thinkers), it was unnecessary to make the concept concrete by providing analogies. The results of the study confirmed that analogies help make chemistry concepts more accessible to students at the concrete operational level (i.e. concrete thinkers). However, the question of whether concrete operational students are capable of taking a familiar situation and applying it to a new one was not answered in this study. Huddle et al. (2000) conducted a study from 1993 to 1996 in Johannesburg, South Africa, involving four different study participants groups: Grade 12 students, student teachers, experienced teachers, and college lecturers. The Equilibrium Games were used as an instructional tool for teaching Le Chatelier's Principle in chemistry and were in the form of paper-based simulation games which mimic the microscopic events of chemical reactions. The 6 Equilibrium Games were employed as an analogy in the study. Huddle et al. (2000) found that higher achieving students who had some understanding of chemical equilibrium before they played The Equilibrium Games benefited greatly from the analogy, whereas students with a very poor understanding of equilibrium prior to the intervention study did not show any noticeable improvement. Also, researchers shared an insight regarding adaptation of The Equilibrium Games for computers. The researchers anticipated that the advantage of computer-based instructions lay in helping to achieve a greater level of flexibility in data manipulation where the students would be able to choose the number of molecules reacting and the graphs would be drawn for them by computer. That premise was left to be tested by future studies. Else et al. (2003) shared the results of a case study entitled The Energy in the Human Body that was conducted in a seventh grade biology classroom in Massachusetts, USA. The study attempted to infer connections between analogy features and the types of difficulties students encounter in learning in the model-based teaching approach. Data sources for this study included classroom observations, formative assessments, analysis of students' classroom work (including student drawings of the human body), discussions with teachers and students, and an assessment that was designed to obtain students' ideas about analogies. Else et al. (2003) believed that it is important to understand the context in which an analogy is presented if one is to understand how the process of analogy understanding works for students. "Our analogies have not served simply as beginning models that are then revised; their role has been more complex. Most are embedded in a complex matrix of other types of learning, prior conceptions, and the reasoning that occurs during the processing of the analogy" (p. 15). The researchers mentioned that they see some limitations to their study because it was difficult to assess the learning outcomes of the use of analogies since evidence of changes in 7 students' model drawings of the human body (that were employed as artefacts for analysis) can occur partly through analogical transfer and partly through other means, such as instructional learning. The researches suggested that analogies may not be effective if used without thoughtful preparation, particularly when an analogy uses a base that is familiar to students before instruction begins or is complex in terms of the number of mappable and unmappable elements and relations. The unmappable elements, such as those that cannot be compared between the analogy (familiar concept) and the studied phenomenon (new concept), add to students' cognitive load and may increase the amount of work that needs to be done by students for processing analogies. Therefore, Else et al. (2003) recommended integration of complex instructional analogies and extensive direction from teachers. In the above studies, different aspects of analogical instructions were investigated. While Gabel et al. (1980) investigated the application of verbal analogies in the chemistry classroom and found that analogies are most useful for concrete thinkers, Huddle et al. (2000) examined the use of analogies integrated into paper-based simulation games finding no support for Gabel et al.'s (1980) claim that analogies are more beneficial for concrete thinkers. Instead, the former study determined that abstract thinkers benefited from analogies more than concrete thinkers. Huddle et al. (2000) explained their results by claiming that simulation games - while concrete -still require abstract reasoning to extrapolate to the target and, therefore, abstract thinkers benefit more from analogies and games. Else et al. (2003) suggested that analogies may only be effective if used with thoughtful preparation in familiar to student content. Nonetheless, Gabel et al. (1980) Huddle et al. (2000), and Else et al (2003) validated the benefits of analogical instructions. Moreover, Else et al. (2003) recommended that complex instructional analogies be presented with extensive direction from teachers. 8 Such instructional strategies were developed by Nashon (2003) and discussed in his qualitative case study that involved classroom observation in three Grade 10 Kenyan physics classes. Nashon shared his insights into the nature of analogies used by Kenyan teachers. Because most analogies were environmental (i.e., drawn from students' everyday cultural environment) the teachers did not attempt to organize or review the analogue structural • information before applying it to the target as suggested in Nashon (2000). An analogue is defined as a familiar concept and a target as an unfamiliar concept (Duit, 1991; Glynn, 1991). Nashon (2003) argued that effective analogy use is based on clear identification of matching and non-matching features of the analogue-target structure. It is important to identify the similarities and the differences between analog and target in order to avoid the confusion and mismatching of the learned concepts' characteristics in student comprehension of analogies in regard to new phenomena. Nashon (2003) proposed the following six-step model, W W A (Working with Analogies): 1. Assess students' knowledge of the analogue 2. Assess students' prior knowledge of the target 3. Identify analogues and target attributes 4. Map similar attributes 5. Point out unmapped attributes 6. Draw conclusions about target Also, Nashon suggested that there is a need for further research that might contribute to positive change in physics instruction in multicultural societies. "The students must be given the opportunity to draw on previous knowledge and be allowed to be major contributors in the generation of such analogies. This is because the analogues these students know best and are 9 comfortable with can make the target information meaningful and usable" (p. 343). Therefore, one can see a close correspondence between Nashon's (2003) suggestions and Gabel et al.'s (1980) findings that there is no benefit to using analogies in instruction i f students do not understand the meaning of the analogies. In the above studies, the use of analogies in different contexts was discussed - verbal analogies (Gabel et al., 1980), analogies in simulation games (Huddle et al., 2000), analogies in model drawings (Else et al., 2003), and analogies in the instructional context (Nashon, 2003). Moreover, the effective use of analogies in science instructions was confirmed. Accordingly, it was interesting to see how analogies not only assist high school students in learning scientific concepts, but also help preservice school teachers to comprehend the teaching material, since teachers' beliefs about teaching can be influenced by their own experiences while learning. Paris and Glynn (2004) conducted a study entitled Elaborate analogies in science text: Tools for enhancing preservice teachers' knowledge and attitudes in a public land-grant university in Athens, USA. The 140 participants were preparing for teaching careers in public and private schools in grades four to eight. The preservice teachers read and studied science texts relating to three important science concepts. The concepts examined were ones that middle school teachers routinely teach: the animal cell, the human eye, and the electrical circuit. They read versions with no analogy, versions with a simple analogy, and versions with an elaborate analogy. In the Paris and Glynn (2004) study, an elaborate analogy was defined as one that consists of text and pictorial components in which similarities between the analog and the target concept are made explicit and verbal and imagery processes are combined to promote a mapping of conceptual features. The researchers shared the results of their study and proposed that elaborate analogies may enhance learners retention of new ideas by creating instructional 10 scaffolds between new ideas and ones with which the learners are already familiar. It was emphasized that the elaborate analogies could help the learners to create verbal and visual links between analog concepts and target concepts. By contrast, a simple analogy (one that has weak operational definitions and that fails to explicitly map analog features systematically onto the target features of analogies) was found to be inadequate for the learning process. The researchers theorized that analogies should help learners to make correct conceptual inferences without causing them to make incorrect ones. And, elaborate analogies, in contrast to simple analogies, can remind learners that analogies are not perfect and provide examples analogies cannot provide examples of where the analogy breaks down, thereby reducing the likelihood of forming misconceptions. As a result, elaborate analogies (ones that consist of text and pictorial components in which similarities between the analog and the target concept are made explicit) could provide learners with personally relevant points of reference for more accurate assessment of their comprehension of target concepts. It was suggested in the Paris and Glynn (2004) study that current practices whereby preservice teachers learn science should be adapted to take advantage Of analogies in a more systematic way and should incorporate carefully crafted analogies that verbally and visually compare unfamiliar science concepts with more familiar ones. When learners think about a scientific concept in analogical terms, they learn by means of elaboration, connecting what is new to what is familiar. Thus, in a teacher learning process as well as in a student learning process, elaborate analogies can serve as powerful models and instructional scaffolds. In the section that follows, I discuss research relevant to my proposed study which partly investigates the role of computer simulations in assisting and enhancing reflective inquiry as part of a model-building instructional approach involving analogical instructions. The term 11 "reflective inquiry" for the purpose of this study refers to a process that occurs when the reflective and collaborative aspects of thinking and learning are integrated into an instructional sequence and students are engaged in the construction of meaning. 6.1b Model-based instructions Researchers such as de Jong, Martin, Zamarro, Esquembre, Swaak. & van Joolingen (1999) and Khan (in press) provided practical insight into model-based instructional strategies that support students in discovery learning in the science classroom. Both studies examined the ways in which instructional strategies foster model construction and evolution. However, while de Jong et al. (1999) discussed a study on discovery learning with a computer simulation environment, Khan analyzed a guided discovery approach to developing inquiry skills in a traditional classroom both with and without any classroom computer assistance. Khan investigated the ways in which model-based instructional strategies foster the development of transferable process skills such as generating hypotheses and evaluating information. The objectives of this study were to determine the instructional strategies and interactions in the chemistry class that help students construct enriched models. The case study, wherein interactions between pairs of first-year university chemistry students and their teacher were monitored, surveyed and evaluated, took place in New England, USA. Khan discussed the teacher's main strategy for promoting conceptual understanding of molecules and the forces that exist between them, without directly telling students about bonds. Rather, a form of "reflective inquiry" appeared to be promoted, whereby the teacher guided his students to enrich their mental models by encouraging them to focus on important relationships that make up the model. The students built relationships among variables that were central to the concept being taught by examining simulated lab information and attempting to offer 12 explanations for this information. This instructional approach involved the teacher encouraging students to express their models, guiding them in enriching and modifying their models, and then asking them to evaluate their models. An interesting teaching strategy was observed by the researcher at a stage of initial molecular model building - making predictions based on "prior models. In this conceptual exercise students were asked to construct a new model based on prior knowledge about the phenomenon and make a prediction regarding the relationship between new variables. When the teacher asked the students to make a prediction and when they offered one, the teacher followed up with a request for an explanation. Asking students to provide an explanation for their predictions appeared to encourage students to make inferences at a molecular level. At the stage of generating enriched models, the teacher suggested that students draw what they were envisioning. The researcher noted that drawing models helped students postulate unique, unobservable, dimensions of their model that were not expressed before. At a concluding testing or evaluation stage, an examination of the models under different conditions appeared to reinforce pre-existing models which were formed on the basis of students' prior knowledge or encourage a modification to pre-existing models of unobservable phenomena, such as molecular structures. Students were able to offer explanations of the relationships between variables that they were not able to before. The results of the surveys in Khan's study suggested the effectiveness of a guided discovery approach and revealed how students were able to express, enrich, and evolve their models when prompted by questions and activities from their teacher. On the post-survey, students progressed conceptually and perform significantly better than on the initial survey. 13 De Jong et al. (1999) found that using computer simulations may provide opportunities to promote students' understanding of unobservable phenomena in science. In the study, students learned with a computer-based learning environment in which the central part was a simulation of collisions. In addition to the simulation, the learning environment contained two instructional support measures: model progression and written assignments. De Jong et al. discussed a study on discovery learning in a computer simulation environment in the physics domain of collisions; the subjects were fifteen first-year students from the Computer Science and Biology Departments at a Spanish university. To assess students' performance, two types of tests were used: a definitional knowledge test and an intuitive knowledge test. For the definitional knowledge test, the same test was used at both the pre- and post-test sessions, while parallel versions of the intuitive knowledge test were used. It is of interest that the researchers developed knowledge tests that not only examined students' understanding of the fact and concepts of the physical process (the definitional knowledge test), but also assessed the structure of knowledge (the intuitive knowledge test). The tests for definitional knowledge concerned knowledge of individual elements - the facts and concepts of the collision domain. To measure intuitive knowledge, a what-if'test was created. In this test, each test item contains three parts: conditions, actions, and predictions. The conditions and predictions were displayed as a simulation output and were accompanied by a minimum amount of text. The action, or the change of a variable within the system, was presented in text. The timed what-if task required the student to decide as accurately and quickly as possible which of the predicted states follows from a given condition as a result of the action that is displayed. The focus of the de Jong et al. study is not on whether computer simulations are effective but how to embed them into classroom instructions for supporting the learning process. The main 14 objective was to determine whether integrating instructional measures, in the form of model progression and assignments, into a simulation environment would lead students to higher performance compared to a simulation environment that did not include these instructional measures. In the experiment researchers conducted, they evaluated the effects of adding two different strategies to guide students when interacting with computer simulations: model progression (which will be elaborated below), in which the model is presented in separate parts, and assignments - small written instructional exercises on the physics domain of collisions that the student can choose to do. The effect of providing assignments and a model progression was evaluated by comparing the learning behaviour and learning results r for three experimental conditions in which different versions of the simulation environment were presented: pure simulation, simulation plus assignments, and simulation plus a model progression and assignments. The findings have shown that integration of the assignments and the simulation improved students' performance on an intuitive knowledge test. Providing the students with a model progression did not have an effect. Researchers mentioned that there were some limitations to their study. The learning effects, though significant, were not very large. Researchers cited several reasons for this - the study time was not very long, the number of subjects was not sufficient and the quality of the assignments could have been improved. De Jong et al. (1999) argued that one of the ways to help learners in the planning of the model of collision process is to give them assignments. Assignments tell the learner what to do and in this way support the planning process. Assignments could be in the form of small exercises that point the learner to specific elements of the simulation model. For example, investigation assignments could prompt learners to find the relationship between two or more variables, while specification assignments ask learners to predict the value of a certain variable. 15 However, the question of examining, in a longitudinal way, over a few months, the effects when assignments gradually disappear from simulation learning environments used by students over a longer period in time was not answered in this study. Another way to support learning processes, as suggested by de Jong et al. (1999) is to restrict the simulation environment, so that learners do not have to cope with the simulation in its full complexity from the start. This way of organizing a simulation environment is called model progression. In model progression, the simulation model is offered in separate steps in which learners gain control over an increasing number of variables. Even though the findings of the study did not confirm the effect of model progression on learning outcomes, the researchers give possible explanations for such a result. First, the complexity of the collision domain was not sufficient for a model progression to have the most effect. Second, the initial knowledge of the students could have had an influence, since the students' prior knowledge measured at the pre-test was substantial. De Jong confirmed that computer simulations positively effect learning outcomes and are most effective when used in combination with instructional strategies, such as writing predictions about what the simulation will do. 6.2 Theoretical Framework In the section that follows, I build a theoretical framework for my study wherein I discuss research relevant to a model-building instructional approach; I also construct a theoretical basis for the pedagogical strategy in my study which employs an analogical view integrated into a instructional computer simulation. 6.2a Model building In the theoretical work of Barbour (1976) one can find underlying principles for the connection between theoretical models in science, analogies and the role of imagination in 16 building models. " A model is a symbolic representation of selected aspects of the behaviour of a complex system for particular purposes. It is an imaginative tool for ordering experience, rather than a description of the world." (p. 6). Barbour defines theoretical models in science as imaginative mental constructs which originate in a combination of analogy to the familiar and discovery or understanding of the new. Barber identifies three key features of theoretical models that clarify their main functionality (p.8): 1. Models are analogical. In the start of a novel theory the scientists may propose a model incorporating analogies drawn from several familiar situations, together with new observed phenomena. 2. Models contribute to the extension of theories. The use of a model may encourage the hypothesis of new rules of correspondence and the application of a theory to new kinds of phenomena. 3. A model is intelligible as a unit. A model is grasped as a whole. It provides a mental picture whose unity can be more readily understood as opposed to the abstracts. Therefore, models can be used for teaching purposes, since they may help students understand theory. 6.2b Mental imagery construction in the learning process. Terms definitions As an advanced organizer, I wish to define important terms employed in the discussion below. A mental model is an explanation of someone's thought process for how something works in the real world. The idea is believed to have been originated by Kenneth Craik in his 17 1943 book The Nature of Explanation. A mental model is an internal cognitive representation of objects, events, or the social and psychological actions of daily life (Khan, 2003). Imagery is any literary reference to the five senses (sight, touch, smell, hearing, and taste). It is a cognitive process which influences perception (Finke, 1989). While mental imagery is a reference to any or all the five senses, visual imagery usually refers to pictorial images. In the theory of cognitive development, a schema is a mental set or representation. In schema theory (Armbruster, 1996; Driscoll, 1994;) there are three different reactions that a learner can have to new information: accretation (learners take the new input and assimilate it into their existing schema without making any changes to the overall schema), tuning (learners modify their existing schema accordingly to new information), and restructuring (creating a new schema addressing the inconsistencies between the old schema and the newly acquired information). Analogy is a comparison between two structures or concepts, one of which is a source or analogue, while the other is new or a target. Analogue is defined as a familiar concept and target as an unfamiliar concept (Duit, 1991; Glynn, 1991). In cognitive science, analogy is used in structure mapping theory, where analogy depends on the mapping or alignment of the elements of the source and target (Gentner & Gentner, 1983). Analogical reasoning involves mapping the problem representation (target) onto a structurally similar schema (base), which has been learned through experience (Halford et al., 1992). Correlational reasoning involves identification and verification of the relationships between variables in solving problems (Stratford, 1998). 18 A general model of conceptual change is largely derived from the current philosophy of science (Posner, 1982). In the pedagogical process conceptual change is defined as a change (hopefully advancement) in student knowledge, which occurs during the process of learning. 6.2c Mental imagery construction in the learning process A constructivist approach to understanding the learning process offers a metaphor for knowledge construction wherein learning is considered as a process of active mental construction within the learner while teaching is viewed as a process of fostering and supporting the student's effort to construct his/her own knowledge (Mayer, 1997). A model-based pedagogical strategy in science promotes learning by fostering student building and evaluation of mental models of a "to-be-explained" system; and understanding of scientific phenomena involves the construction of theoretical models with explanatory power (Khan, 2002). Analogical reasoning allows learners to construct a model based on relationships already in prior knowledge rather than creating a new conceptual structure (Gentner & Gentner, 1983). Figure 1 shows the theory of the process of analogical reasoning according to Duit research. A domain which functions as a base (the familiar) and a domain which is learned (the new) are explicitly compared through mapping the similarities of the two structural relations. As a result, a mental model is generated by analogy in the process of transferring structures from familiar to new domains. (Forbus & Gentner, 1997; Duit, 1991). In the diagram, the environment represents the observed events, and the mapping of familiar and new into a model represents mental processes. 19 Environment Figure 1. Analogical Reasoning. Analogical reasoning is interrelated with visual imagery (a set of mental pictures and images) and correlational reasoning (the act of using reason to derive a conclusion, using correlated premises). Construction of visual imagery involves imagination, that is, the mental capacity for constructing and manipulating 'mental imagery' as an integral part of the cognitive process (the elaboration on cognitive process follows below). Figure 2 represents a hypothetical cognitive process involving conceptual change and memory development. Cognition involves three processes: long-term memory, short-term or working memory and sensory memory (Held, Vosgerau, Knauff & Strube, 2003). Perception provides an input through sensory memory, which is very short. It lasts less then a second. Long term memory serves as storage for prior knowledge. It can last for years. Working memory is used to perform cognitive processes on the items that are temporarily stored in it. It is what is involved in processes that require reasoning, such as reading, or writing, or performing computations. In constructing mental imagery, working memory serves as the arena for combining and processing visual information (Croft & Thagard, 2002). It has been proposed that the elements of perception from sensory memory enter working memory. Visual concepts from long-term memory are retrieved or recalled in a process of imagination. Imagination plays an active role in two main processes of mental imagery: in retrieving prior knowledge from long 20 term memory and in linking that retrieved structure with observed elements that come from perception. Environment Working memory Figure 2. Conceptual Change in Cognitive Process. Figure 3 is a proposed dynamic mental imagery illustration, the result of a combination of analogical reasoning (Figure 1) and cognitive process (Figure 2). Linking familiar and new structures is a complex procedure which requires mapping of the elements and relationships of one domain onto another by finding similarities and differences between them. This structure mapping process involves repetitions in working memory and confirmations from the empirical observations which engage sensory memory. A new structure that evolves as a result of the conceptual change in the cognitive process is stored in long term memory. In other words, a mental model, a new schema for the observed phenomena is generated by analogy, by transferring structures from source domains to target domains. The following two main points are illustrated in Figure 4: (1) Model-based reasoning is supported by the analogies that provide a base, a prior knowledge (see Familiar) for building anew concept (see New); and (2) Imagination (occurs in building a Model) plays a key role in the process of linking mental imagery of a familiar with a new structure students observe in scientific activities. .Accordingly, learning, in a constructivist learning approach, is an active construction process which is based 21 on previously acquired knowledge. Analogies, therefore, are a means to understand the unfamiliar by employing the familiar and constructing similarities between the new and already known (Duit, 1991). E n v i r o n m e n t Working memory Figure 3. Dynamic Mental Imagery Construction in Model-Based Reasoning. However, researchers have found that i f students lack visual imagery, or lack analogical reasoning or correlation reasoning, then the effective use of analogies in model-based reasoning is believed to be limited (O'Brien, 2002). Another reason for student's difficulties in developing mental models is that while students can observe a scientific activity (such as the new through sensory memory in Figure 4) and can access images of concepts from their memory (such as the familiar from long-term memory in. Figure 4), they are not always able to map the familiar analogical structural relations onto new empirical phenomena using mental imagery (such as building a model in working memory in Figure 4) due to a deficiency in imaginative skills, such as the ability to visualize the process of mapping the elements between two structures in their imagination. 22 6.2d The role of multimedia in meaningful learning How can teachers help students to "construct their own knowledge"? What tools are available for teachers to scaffold (the process of supporting student investigation and learning) mental model development? One of the answers to these questions might lie in employing computer simulations in science classrooms. Computer simulation can be considered as an abstract tool that enhances cognitive processes and assists a teacher in scaffolding reflective inquiry. Computer simulation is a software program that runs on any size computer and that attempts to simulate some phenomenon based on a scientist's conceptual and mathematical understanding of the phenomenon. Meaningful learning occurs when a cognitive representation, such as a mental model, is constructed in short-term memory and is saved in long-term memory. Several teaching strategies can be employed for helping students enter information into long-term memory. One of them is a rehearsal technique. Two types of knowledge acquisition could be achieved through the rehearsal of information - retention, such as remembering by repetition and the transfer of knowledge in problem-solving reasoning (Mayer, 1997). In a multimedia instructional environment, computer animation enhances the process of visualization of the phenomena. Computer simulations tools are capable of visually and dynamically representing models at multiple levels and under different conditions. Runnable or animated models provide an opportunity to explicitly display familiar and new phenomena in single multimedia environments. It is hypothesized that dynamic correlation of the functionality of two structural relations over time (displays of familiar and new phenomena) induces the activation of imagination and mental imagery, and, consequently, helps to foster mental model development in learners. Furthermore, with regard to supporting reflective inquiry in model-23 based learning, it has been suggested that the learner's interaction with the dynamic multimedia environment should be structured. In order to avoid overloading students' cognitive capacity to perceive information (Sweller, 1998), an instructional scaffolding strategy may be used to guide the learner in focusing on specific variables of underlying models and and generating and evaluating a hypothesis (Bodemer, Ploetzner, Bruchmuller & Hacker, 2005; Khan 2002). In particular, researchers have found that when simulations are represented in the form of dynamic and interactive visualisations and are embedded in multimedia learning environments, dynamic visualisations may overburden the learners' cognitive capabilities due to the presence of large amounts of continuously changing information (Bodemer et al., 2005; Hegarty, 2004). However, there is a difference in student comprehension and cognitive load demand for dynamic interactive and non-interactive displays. Dynamic interactive displays not only reduce cognitive load, but also prompt learners to be more active in the learning process (Hegarty, 2004). In order to guide learning with dynamic and interactive visualisations, various instructional support measures to facilitate specific processes of discovery learning have been suggested. Accordingly, it has been proposed that learners receive structured guidance. And, particularly, the integration of different sources of information into coherent mental representations, such as exploration of static representations, is recommended before processing dynamic visualisations of information (Bodemer et al., 2005). 6.3. Summary Barbour states in his theory that a theoretical model is an imagined structure of an observable system and is used to construct a theory to correlate a set of observations. Based on Barbour's theory, one can infer that computer simulations, which are capable of displaying two structures (such as the familiar and observable, see above) and visually correlating their 24 functionality over time, can be a useful tool for generating scientific models. Different pedagogical strategies that could be integrated with a simulation to scaffold reflective inquiry should be considered in order to make computer simulations an effective tool in model-based instruction. Researchers such as Gabel et al. (1980), Huddle et al. (2000), and Else et al. (2003) have demonstrated that analogies do allow learners to build on relationships that are part of their prior knowledge base. The results of the Gabel et al. (1980) study are particularly interesting because they suggest that analogies can help to make formal concepts more accessible to concrete thinkers. Even though the study by Huddle et al. (2000) did not confirm this premise, I am interested in testing Gabel et al.'s (1980) that there is a difference in comprehension of analogies between concrete and abstract thinkers. Researchers such as De Jong et al.(1999), Khan (in press) and Else et al. (2003) suggested that computer simulations can enhance the instructional environment and contribute to student learning outcomes and are most valuable when used in combination with other instructional strategies. In the studies by Khan (in press) and Else et al. (2003) there is a suggestion to incorporate prediction activities into model building. Else et al. (2003) advised making the cognitive purposes of each analogy more explicit so that students can perform "reality checks" -such as comparing their hypotheses with the results of the laboratory experiments - on potential maps when they have some understanding of the target that is independent of the analogy. Khan (in press) observed that making a prediction before testing a model against data is beneficial. De Jong et al. (1999) detected that incorporating assignments with simulations improved student performance. Bodimer et al. (2005) suggested supporting learners in dynamic visualisations during simulation-based discovery learning with the active integration of static representations before processing dynamic visualisations. Hence, I plan to integrate prediction questions and 25 small assignments into instructional materials associated with the simulation and to present static pictorial example of the analogy to students before computer simulation activities. In the review of the above studies, there appears to be a consensus that chemistry, computer simulation tools (which are capable of visually and dynamically running models) not only provide an opportunity to envision representations of molecules and, therefore, make unobservable phenomena visual, but also assist students in linking the macromolecular with the molecular view (Kozma, 2000) and help map familiar (analogical) and new (target) processes thus encouraging student's self-reflection while assisting a teacher in scaffolding model generation and evaluation. However, an analogical view integrated in a computer simulation has not yet been studied for clarification of the connection between molecular and macromolecular phenomena and between new and familiar phenomena. This connection where the analogy view is mapped to the parameters of a chemical reaction is hypothesized to be beneficial for visualization of students' conceptual models of molecular interactions and for the facilitation of students' understanding of Le Chatelier's Principle. Therefore, the goal of my study is to determine the extent to which an analogical computer simulation designed for modeling Le Chatelier's Principle impacts on learning outcomes among high school students. 7.0 Software design and development In this section, I discuss the context in which this research was conducted. 7.1 The TEMBS project The computer simulation employed in the present study was built and designed as part of the TEMBS (Technology-Enhanced Model-Based Science) project. A team of researchers with Dr. 26 Samia Khan as the principle investigator has been working on the ongoing TEMBS project (2003-2006). The research team consists of two high school chemistry teachers, one graduate student from the Chemistry department, one graduate student from the Computer Science department, one graduate student from the Interdisciplinary Studies, one graduate student from the Curriculum Studies department, and two computer programmers. There have been three phases to the TEMBS project: The first phase had the goal of studying how students construct understanding in particular areas of science, the kinds of teacher actions and interactions involved in this process, and the possible ways in which understanding can be fostered with the use of new media technologies in the classroom. To achieve this goal, the research team investigated how several British Columbia science teachers fostered student understanding of unobservable phenomena in their science classrooms. The findings based on classroom observations led the research team into the next phase of the project - the TEMBS simulation's design and the development of the corresponding curriculum to accompany the simulation. The second phase had the goal of developing a TEMBS computer simulation for use in chemistry classrooms. The multidisciplinary team designed and built the computer simulation that incorporated techniques that were frequently used by the teachers and students in learning Le Chatelier's Principle and chemical equilibrium. The project team created a novel simulation that (1) affords opportunities for model construction with analogies, (2) facilitates model evaluation by providing multiple reaction representations, and (3) guides learning by explicitly requesting predictions from students (please see the simulation overview below). 27 In the third phase, the TEMBS computer simulation was brought to the classroom and employed in research studies that examined student learning with the use of technology. The present study is an instance of one of these studies. "7.2 TEMBS Computer Simulation The simulation interface is composed of several modules (see Figure 4): a formula view, a slider view, a graph view, an analogy view and a prediction mechanism. The Formula view shows the chemical reaction formula and the chemical states involved. The Slider view provides a control mechanism to students and supplies molarity data. The Graph view illustrates concentrations for each chemical over time. The Analogy view provides a chemical reaction analogy. The Analogy view can be switched to the Molecular view in the same interface module. The Molecular view in this study is not considered to be analogical but merely a magnified view of molecular interaction, helping to make unobservable processes observable on the computer screen. The Prediction mechanism is a 4-point multiple-choice question that must be answered before the simulation can be run. A l l modules have one common underlying computational mechanism, which provides synchronization for each view in the simulation interface. The computation is done on the scientific basis of the dynamic change in the chemical reaction mole concentration and is implemented in Java script language. 28 Formula view Slider view * Previous Vstua; 13 J) Current Value PievHM* Value: ]PCI3 (mol*sj Prevtoua Value: 11.37 Graph view P C L ( g ) C I 2 ( g ) + P C I 3 ( g ) volum* (Lltr*t) 2 . W h a t h a p p e n s t o t h e c o n c e n t r a t i o n o f P C I 5 i f t h e n u m b e r [of m o l e s o f C L 2 c h a n g e s t o 3 0 . 0 ( C L 2 a d d e d t o t h e s y s t e m a t q u l l l b r l u m ) ? please CIICK 'Continue' to resume trie simulation No change" B : "Logarithmic increase" C "Instantaneous decrease" "Linear decrease" Analogy view Prediction mechanism Figure 4. Simulation overview. In the simulation interface the Analogy View presents the chemical equilibrium simulation in terms of a weighing scale analogy. The Analogy View represents the interaction between the scale and the scale's components, where blue triangles and green squares are reactants (Cb and PCI3 as illustrated in Figure 5) and red composites are products ( PCL5 as illustrated in Figure 5 ) . Objects represent concentrations of chemicals. The scale's components are synchronized with the chemical reaction engine. Chemicals travel from the right side of the scale to the left side and make a composite red element out of blue triangles and green squares. 2 9 C h e m icals T r a v e l In C i r c u l a r Pattern in a C l o c k w i s e D i r e c t i o n S w i t c h Po in t Scale No t B alanced (Increased Product ) A Figure 5. Analogy in simulation. 8.0 Research Methodology 8.1 Introduction My ontological position on reality is influenced by a constructivist understanding of constructed and co-constructed realities. My epistemological position is consistent with a constructivist paradigm as well. However, my methodology is aligned with post positivist methods which include modified experimental design, falsification of null hypotheses and combination of qualitative and quantitative methods. Even-though a quasi-experimental design is post positivist in nature, I have selected it because I was restricted by the short duration of the 30 study (determined by the participation in the project of a high-school teacher, which did not permit longitudinal analysis, in depth interviews, and hermeneutical analysis). Specifically, in this study, I wish to inquire to what extent the analogical view in computer simulation designed for modeling Le Chatelier's Principle has an impact on learning outcomes among high school students. The research questions for my study are: 1. Are the learning outcomes for 12th -grade chemistry students affected by instructions involving the use of analogical or non-analogical computer simulations? 2. Do instructions involving the use of analogical or non-analogical computer simulations benefit both high and low achieving secondary students in chemistry equally? 8.2 Research Design To answer my research questions, I used the Posttest-Only Control Group Design (Gay & Airasian, 2003). In the Posttest-Only Control Group Design the participants in a study are randomly assigned to groups, exposed to the different treatments, and posttested. The combination of random assignment and the presence of a control group served to control all sources of threats to internal validity (such as history, testing, differential selection of participants, and maturation) except mortality. However, since the duration of my study was very short, the probability of mortality was very low. Also, the short time of the study reduces the treatment diffusion external threat. Moreover, the absence of a pre-test eliminated the threat of a possible interaction between the pre-test and the treatment, minimizing testing threats of external validity. Also, since both classes interacted with the same software, the Hawthorne and novelty effects were evened on both groups, hence minimising external validity threats. 31 I view the Posttest-Only Control Group Design as the most appropriate and effective design for my study given my research goals and available resources. 8.3 Population/Sample The study was conducted in a rural BC high school, with a student body of about 1400. The participants were students from a Chemistry 12 class. The students possess a range of ethnic backgrounds, academic abilities, and socioeconomic levels with the majority hailing from middle-class families. The students did not have any prior experience with the use of computer simulations in the chemistry classroom. A total of fifteen students were randomly assigned to two treatment groups A and B (ni=8 and n2=7). The students' levels of achievement (high or low) were obtained prior to the study. The class teacher had access to students' records and provided the researcher with a list of student IDs indicating their respective levels of achievement in the Chemistry 12 course over the year prior to the intervention study (letter 'h ' for high and letter '1' for low). An achievement level of 75% or higher was considered as high and an achievement level of 74% or lower was considered as low. Students' grades were in the range between 60 and 90%, therefore 75% was considered as a median score. The random assignment to the treatment groups was achieved based on the levels of achievement, so that low level students were randomly assigned to each of the groups and then high level students were also randomly assigned to those groups. Random selection of students at each level of achievement was accomplished by the method of simple random sampling whereby random subdivision into the two subgroups was done by flipping a coin - heads for group A and tails for group B. 32 8.4 Instrumentation Survey. Survey questions assessed students' perspectives on what instructional activities in the chemistry classroom they found to be more useful to their learning practices, and if this opinion is different in relationship to the types of instructional computer simulations (analogous and non-analogous). Survey questions were in the form of agree-disagree statements about student preferences regarding classroom instructional activities and student perceptions of learning in a chemistry class. The survey consisted of 19 questions in total. TEMBS post-test. A TEMBS post-test was created as an assessment of conceptual understanding of Le Chatelier's Principle among high school students. (See Appendix A) , There were eight multiple choice questions and two written open-ended questions for a total of 12 marks. The pre-post test questions were designed to examine students' understanding of Le Chatelier's Principle in three ways: (a) traditional Le Chatelier test questions similar to BC standardized provincial test questions employing interpretation of symbolic formulas, (b) graphical questions similar to standardized provincial test questions employing the analysis of graphs representing changes and different states in chemical systems, and (c) model evaluation questions. These questions were formulated by the TEMBS team (see Appendix A, questions 1,3,5-10; 2,4a-b; and, 4c; respectively). For example, the question testing symbolic formula understandings and the question examining student graph interpretation abilities on the TEMBS post-test were modified from standardized provincial tests in the following ways (see Tables 1 and 2): 33 Table 1. Symbolic formula understandings questions Question from standardized tests Question from the TEMBS post-test Consider reaction: C a 2 + ( a q ) + 2HCO3 1 " (aq) <*> CaC0 3 ( s) + CO 2 ( g ) + H 2 0) ( 1 ) (AH= +39kJ) Predict the effect on the equilibrium concentration of CaC03(S) i f C a C l 2 is added to the reaction vessel. C a C l 2 is soluble in water. A . Increases B. Decreases C. No change Consider the following equilibrium: 2 N 0 ( g ) + 2 C 0 ( g ) o 2C02 ( g) + N 2 ( g ) ( AH= - 747kJ/mol) (Please circle the answer) Predict the effect on the equilibrium concentration of CO 2, i f N 2 is added to the reaction A . Increases B. Decreases C. No change Table 2. Graphs interpretation abilities question Question from standardized tests Question from the TEMBS post-test Consider the following graph for the reaction: H2(g) + b(g) o 2HI(g) HI H 2 The temperature is increased at ti and equilibrium is re-established at t2. a) On the graph, sketch the line representing the [HI] between time ti and 12. Consider the following equilibrium and accompanying graph: 2NOCl ( g ) <s> 2NO ( g) + Cl2(g) 2NOCl ( g ) 2NO ( g ) CI 2(g) tl ti t3 a) Identify the stress applied at ti. b) Complete the above graph from ti to U for the 2NO ( g ) and 2NOCl ( g ) 34 To test students understanding at a molecular level, the model-based learning question was designed in the following form: 4 c) Draw what you think is happening at the molecular level. Draw and explain how molecules of 2NO (g), CI2 (g) and 2NOC1 (g) will change in concentration for the following time period. A legend and Time to model are given below i) Time to Legend: NOCl = ® NO = O C l 2 = • ii) Time ti Stress is applied 3 molecules of CI2 are added • • • iii) Time ti *** The TEMBS post-test questions were reviewed by a member of the U B C chemistry department and two practicing chemistry teachers and found to be fair and reasonable for testing th 12 grade chemistry students. One of the post-test questions reviewers was the teacher of the class participating in this study. Hence, there was a concern raised regarding the teacher's advanced knowledge of the performance of his students. Such influence was minimised by the 35 fact that the study was conducted during the preparation for the Provincial exams, when Chemical Equilibrium and Le Chatelier's Principle units had already been taught two month before the present study took place. Also, the teacher was asked not to prepare students for the post-test; moreover, the post-test results (test scores for each of the students) were not submitted to the teacher of the class and did not play any role in the grading for the chemistry course. Survey questions were reviewed by every member of the TEMBS research team in order to comply with study goals. The survey questions are of uninformative nature and are intended to solicit student opinions on the use of computer simulations and a model-based approach. 8.5 Procedures The consent of each student and parental consent were obtained in advance for the study. (Please see Chapter Ethics). For all tests, surveys and instructional sheets for simulation activities, students were asked to provide an ID number. The ID number was a specially designed number not related to their name or their school student ID. Therefore, all information students provided was anonymous in order to maintain student confidentiality. The tests and activity sheets were brought to the classroom in a sealed envelope, so that students and the class teacher did not have access to the papers until the study began. The study took place over one classroom lesson (90 min) after students had previously completed studying Chemical Equilibrium and Le Chatelier's Principle units. The study took place in a computer lab. The computer lab had 20 computer stations for each student's individual use - each workstation consisting of a Macintosh computer and a 17-inch monitor with computer simulation software in Java applet format. A l l fifteen students were present in the same computer lab at the same time. The computer workstations rows were grouped in the lab in such a way that the lab was spatially subdivided into two areas of clustered rows. The room design allowed for 36 the privacy of each treatment subgroup, and, at the same time, allowed instructions to be addressed to both groups at the beginning of the study. The teacher was present in the class, but he did not interact with the students. Three TEMBS research assistants facilitated the study and administered the tests. (See the structure of the study in the Table 3). A l l students were able to finish the post-tests; one of the students did not submit a survey. Table 3. Structure of the study • ' Time an^SeltihgKV i'„, }:»\ Researcher guidance : Introduction 5 min, Verbal presentation by one of the research assistants Both groups are addressed • Welcome to the study • Purpose of the study • Introduction of the research assistants (names and the roles in the research team) Tutorial on the use of simulation software 6 min, Demonstration of computer simulation software on a projector screen by one of the research assistants Both groups are addressed • Introduction of the software, its purpose • Each module functionality • Menu functionality Analogy instructional presentation 7 min, Power Point presentation on a projector screen by on of the research assistants Both groups are addressed • Recall an example of a scale, its purpose and use in everyday life • Connection between Chemical equilibrium and scale equilibrium • Visual representation (pictures) of each stage of the scale from out of balance until reach of equilibrium-• See Appendix E Interaction with 50 min, 37 computer simulation (treatment study) Individual activities One student per computer station See Appendix D for analogous computer simulation and Appendix C for non-analogous computer simulation Also, see the explanation of the instructional approach in the text below this table TEMBS post-test 15 min, Individual activities One student at a desk See Appendix A Survey 7 min, Individual activities One student at a desk See Appendix F The treatment study was designed to explore two chemical reactions using computer simulation software. Both groups did the same simulation activities guided by a common set of guidance sheets (see Appendix C and D). The difference between the two treatment conditions was that while one of the groups (Group B) observed the analogical example in the analogy view window of the analogous simulation (see Figure 6a); the other group had to recall the analogical example by following the guidance sheet instructions. The analogy view window was turned blank (disabled) in the non-analogous simulation (see Figure 6b). S Simulation •A CI (B)« PCI,(s) 1- 1 A? • '•'a 1 *> * L -\ — • 1 Wnm ruppenstoth. toncrnd jiion or PC IS Uttl* nunMt or noloi of CL2 Chang*! to 30.07 "WO dUMg«* "lnioai o*[(oon«" *- - i . i 1 Analogy view PC!5(B) * Clj(B) + PCI3(fl) Analogy view disabled Wlut liappuru lo (tit concentration of PCIG Vtl iofmolaiofCLZchangaito 30.0 (CL: mmdlom • quINbrlumj? p: Tooanthmic ino • (MilwaBIIIIoui dt Figure 6a. The analogy view window. Figure 6b. The analogy view window disabled. 38 The students comprising both groups had the opportunity to interact with the simulation in the molecular view window (see Figure 7 ) . P C U j ) Cl,(g) + PC1,<J) R M M V W 11.37 C U T I M V M J Molecular view 2. What happens to t h * concentrat ion of P C I 5 If the number of moles or C L 2 changes l o 30.0 (CL2 added to the system at equi l ibr ium)? please click 'Continue' to resume trie "No change" B: "Logarithmic Increase" Instantaneous decrease" Linear decrease" Figure 7. The molecular view window. The proposed in this study the instructional approach was implemented in a five-step intervention strategy that was hypothesized to activate the processes involved in mental imagery construction, as represented in Table 3 below: Table 4. Five-Step Instructional Strategy Steps Instructional sequence Student cognitive goals 1 Retrieve an analogy • Appealing to prior knowledge • Processing static representations of analogy 2 Observe new phenomena • Engaging sensory input 3 Link Familiar and New • Employing imagination • Processing dynamic visualisations of analogy 4 Apply /Predict/ Repeat • Solving a problem using mental imagery 5 Store/Veri fy/Modi fy • Consolidating knowledge • Processing dynamic visualisations of analogy 39 As summarised in Table 4, during computer simulation activities the students followed the activity guidance sheets structured in accordance with the five steps. In step 1, students retrieved an analogy before running the simulation: they were asked to recall and investigate the analogy structure (analogy of a scale) presented as narration, text and via illustrations (see Appendix D). In step 2, students observed new phenomena (e.g. changes in the concentration of the chemical components) on a computer simulation screen in the form of dynamically changing graphs and a symbolic representation. In step 3, students associated the familiar (analogy) and the new (observed) by watching an interactive animation of the analogical example which was synchronized with the changes in graphs and symbols. Group A observed the analogical example in the analogy view window of the simulation; and the students from Group B did not observe the analogy view in simulation (the view module was turned blank). They had to follow the guidance sheet instructions and recall the analogical example they saw at the beginning of the study (in step 1). In step 4, the problem-solving strategy was employed by encouraging students to apply knowledge gained in previous steps. This was accomplished by asking students to predict the output of the questions displayed on the screen. Finally, students verified their answer displayed on the computer screen before the next simulation was run. A summary of the instructional approach for associating an analogy example with a chemical process involved in Chemical Equilibrium and Le Chatelier's Principle is summarized in Table5. 40 Table 5. Instructional analogies employed in the study Instructional Analogies Group A Group B Before simulation activities Introductory: Narration, text, illustrations l e i During simulation activities Appealing to memory: Recall, introductory analogy following instructions in text No Interactive: Visual/Dynamic analogy in simulation (scale analogy) No ' Before exploring the two chemical reactions involved in the study, students were given an analogical example of a scale in a narrative format, and an explanatory text with static diagrams (see Appendix E). The analogical example in a pictorial form had a set of images presenting different stages of the scale movement according to the changes in the scale balance. During simulation activities, the investigation of each of the two chemical reactions consisted of two parts: A an investigation of the reaction using an analogical example, and, B- an investigation of the reaction using the molecular view.example. In Part One (Sections 1A and IB), the students were asked to manipulate the sliders controlling the concentration of the reactants involved in the chemical reaction. In Part Two (Sections 2A and 2B), the students did not manipulate the sliders. Although the simulation's underlying mechanism created changes in concentration (sliders moved without student participation), in the middle of the process of reaching the equilibrium, 41 the animation stopped and students were given a multiple choice for the probable reaction progress - four possible changes in the concentration graphs (see Figure 8). Figure 8. Multiple choice displays in the Graph view and in the Prediction mechanism. During the first reaction (PCI5 <^> PCI3 + Cb) exploration, the students from both groups followed the instructions and observed the changes in the concentration of the reactants and products involved in the chemical reaction. In part 1A, the changes in the chemical process were displayed in graphs and the animation window according to Le Chatelier's Principle in the simulation. While one of the groups (group B) had the animation window in analogous mode, the other group (group A) had the animation window turned blank, and students from that group were asked to recall the introductory analogical example. In part IB, students followed the same set of instructions involving the same reaction as in part 1A and the same sequence of making changes with the sliders controlling the concentration. The difference between parts 1A and IB was that the animation window was switched from analogical view to molecular view (See Figure 6a, b and Figure 7). For example, in Part 1 A , in analogous mode of the simulation, the students were asked to follow the instructions below: 8. Press RESET 9. Move slider of PC15 to 28-30 10. Press START and immediately after the scale moves down press STOP to observe the changes in the graphs and sliders. (Note: do not wait until the system reaches equilibrium) 42 11. Answer the questions below: Question 5: a) Predict what changes will happen to the system in order to re-establish equilibrium. b) In which direction does the equilibrium shift? c) Explain why the concentration of the substance PCh which was increased during the moment when a stress was applied to the system, needs to be decreased by the time when equilibrium is re-established. In another example, Part IB, in molecular mode of the simulation, the students were prompted to do the following: 1. Move slider of PCh to 27-28 2. Press START and observe for 60 seconds the changes in the graphs and sliders 3. Press STOP when reaction reaches equilibrium 4. Answer the questions below: Question 1: Was the reaction at equilibrium when you pressed start? Question 2: What dynamic changes did you see in the graphs during simulation run? Question 3: What dynamic changes did you see in the molecular view during simulation run? Question 4: What dynamic changes did you see in the position of sliders of all substances during simulation run? 43 During the second interaction, which involved a newly presented chemical reaction (N 2 + 3H 2 2NH3), the students from both groups were asked to predict the behaviour of the reactants and products using a question-answer simulation module (see Appendix L) before observing the correct behaviour of the chemical elements in the animation window. Again, in part 2A the animation window was in analogical mode (active for the experimental group B and disabled for the control group A), and in part 2B the animation window was in molecular mode (active for both groups). For example, in Part 2 A , while in analogous mode of the simulation, the students had to do the following: 24. Press START, observe for a few seconds and wait until the question pops-up for reaction N 2 + 3H 2 <=> 2NH 3 NOTE: If you pause the simulation you can double click on a dot in the graph view and the simulation rewinds to that point in time. 25. Answer question 1 (put a letter for your answer here on the paper and then select the answer on a computer screen) 26. Press PREDICT, find the correct answer. If your initial answer was not correct, can you explain the correct answer after you watched it? In Part 2B, while in molecular module, the students were asked to: 32. Press START, observe for a few seconds and wait until the question pops-up for reaction N 2 + 3H 2 2 N H 3 33. Answer question 3 (put a letter for your answer here on the paper and then select the answer on a computer screen) Press PREDICT, find the correct answer. If your initial answer was not correct, can you explain the correct answer after you watched it? A diagram of instructional sequence for the treatment study is summarized in Table 6 below. 44 Table 6. Simulation activities instructional sequence ,» Sequence , , .- Mode Chemical reaction View Instructions Part One Section 1A Normal mode PC15 ^ PCI3 + C l 2 Analogical view (analogical view turned blank for the group with non- analogous computer simulation) (See Figure 7a&b) • Observe reaction at equilibrium • Change concentration of C l 2 • Observe reaction until it reaches the equilibrium • Change concentration of PCI5 • Observe reaction until it reaches the equilibrium • Explain the changes you observed (the questionnaire in the instructional sheets) Section IB Molecular view (see Figure 8) • Observe reaction at equilibrium • Change concentration of PCI3 • Observe reaction until it reaches the equilibrium • Explain the changes you observed (the questionnaire in the instructional sheets) 45 Sequence Mode Chemical reaction ... View Instructions Part Two Section 2A Prediction mode N 2 + 3H 2 2NH 3 Analogical view (analogical view turned blank for the group with non- analogous computer simulation) (See Figure 6a&b) • Observe reaction at equilibrium, wait until changes in the concentration happens and the question pop-ups in the simualtion • Answer question # 1 • Press button "Predict" to run simulation and observe the correct answer • Press button "Continue" and observe new changes in the system • Answer question #2 • Press button "Predict" to run simulation and observe the correct answer Section 2B Molecular view (see Figure 7) • Observe reaction at equilibrium, wait until changes in the concentration happens and the question pop-ups • Answer question #3 • Press button "Predict" to run simulation and observe the correct answer • Press button "Continue" and observe new changes in the system • Answer question #4 • Press button "Predict" to run simulation and observe the correct answer 46 8.6 Data Collection TEMBS post-test and a survey were given at the end of the treatment study on the same day. Post-tests and surveys were in paper-and-pencil format. Fifteen post-tests (100% return rate) and fourteen surveys (93% return rate) were collected. In addition, the activity sheets (15 out of 15) and video observations of classroom activities (three video tapes) were gathered. 5.7 Data Analysis The TEMBS post-test results and surveys were analysed. Surveys were in a Likert 5-point scale questions-answers format (Khan 2002, 2004). Since the surveys did not require grading, the individual answers were transferred onto data sheets of SPSS software by one of the researchers. Data were collected and evaluated using a statistical tool (SPSS package) for descriptive and graphical analysis. The TEMBS post-tests required grading, therefore, all post-tests ware marked by two researchers and their marking was tested for interrater reliability. The graders were blind to the groups (the tests offered no indication of the type of computer simulation with which the students interacted). In 99% of the cases both graders coded questions the same. The correlation between the two graders was found to be significant at p < 0.01 level. (See Table 7). Table 7. Correlations of the test scores between the two graders Grader 1 Grader 2 Grader 1 Pearson Correlation 1 .915(") Sig. (2-tailed) .000 N 15 15 Grader 2 Pearson Correlation .915(**) 1 Sig. (2-tailed) .000 N 15 15 Due to the different background of the graders some of the questions were graded differently (See Appendices J and K) . Grader 1 marked the questions on the basis of his knowledge of 12 th 47 grade chemistry students. Grader 2 marked the questions on the basis of her knowledge of college and university students. For these reasons, questions 4b and 6 had some discrepancies in marking. For example, in question 4b students were asked to draw a graph for the equation 2NOCl ( g ) <s> 2NO ( g) + Cl 2 (g) Some of the students drew the graph for 2NO(g) with insufficient downward curve. Such as in the Figure 9 below: 2 N O C L (g) 2 N O ( g ) 0 A j ; : | \ w t, t3 Figure 9. Examples of post-test answers for question 4b. In the case of Figure 9 above, grader 1 gave the student a full mark (1 point) and grader 2 gave the student only half of a mark (0.5 point). 48 Another example, in question 6, the students were asked to state two methods of shifting the equilibrium to the right for the equation CO(g) + 2H2(g) CH30H( g ) + heat. One of the correct answers could be "constantly add CO(g) + 2H2(g) ". However, many students mentioned only CO(g) without 2H2(g). Such as "increase concentration of CO." Grader 1 gave the student a half of the mark (0.5 point) and grader 2 gave the student no mark (0 point). The above discrepancies in marking were discussed between the two graders, and it was agreed to follow the grading scheme of Grader 1. The TEMBS post-tests consisted of 12 questions (1, 2, 3, 4a, 4b, 4c, 5, 6, 7, 8, 9, and 10). Each question was assigned 1 mark. There were seven multiple choice questions (2, 3, 6, 7, 8, 9, and 10) and five open format questions (1, 4a, 4b, 4c, and 5). Please see the marking scheme in Appendix B. Multiple choice questions were marked either 1 or 0, since there was only one possibility for a correct answer, while open format questions could be graded partially. For instance, Question 1. Consider the following equilibrium: C0 2( g) + H2(g) <=> H 20( g) + CO(g) a) Equilibrium shifts to the right when H 2 ( g ) is added to the system. Describe the changes in reaction rates that cause this shift to the right. In the correct answer, students had to identify that at the moment of adding H2(g), the forward rate increases, but the reverse rate does not change. As reaction progresses, the forward rate decreases and the reverse rate increases. However, most of the students were able to correctly identify only initial increase in the forward rate, and for the reverse rate they stated that it decreases. Therefore, such answers were marked as partial 0.5 mark. Question 4c . Draw what you think is happening at the molecular level at t l and t2. Draw and explain how molecules of 2NO (g), C l 2 (g) and 2NOC1 (g) will change in concentration for the following time period. The correct answers for t l is and for t2 49 ® O © 0 = 4, 0 = 4 , # =5 • <5, © > 4 , 0 <4 When marking, i f students were able to draw correctly only one of the models, and could not show conceptual understanding in the second, then partial mark of 0.5 was graded. Question 5: Write any two characteristics that apply to chemical equilibrium systems. The correct answer included the characteristics as 1) any equilibrium is a dynamic balance between reactants and products, 2) when a system at equilibrium is subjected to a disturbance, the composition of the system adjust so as to tend to minimize the effect of the disturbance. Thus, when students were able correctly to identify one of the characteristics, then a particle mark of 0.5 was graded. For example, one of the answers was: "1. equilibrium is constant, 2. but continuous unless a stress is added." As you can see in this case, a student divided one of the characteristics of a dynamic balance (correct answer number one, see above) into two parts -constant and continuous - making two statements instead of one. This answer was graded as 0.5 mark. *** The individual scores from the post-tests were transferred onto data sheets for SPSS software. Data were collected and evaluated using a statistical tool (SPSS package) for descriptive, inferential and graphical analysis. In order to analyse the effect of two variables, a two factor A N O V A analysis was used in this study. Control variables were achievement levels (high and low). Manipulated variables were types of computer- based instruction (analogous computer simulation and non-analogous computer simulation). See Table 8. Table 8. Distribution of Learning Outcomes in Two Factor ANOVA Analysis Analogous computer simulation Non-analogous computer simulation Prior High Level of Achievement X I Y l Prior Low Level of Achievement X2 Y2 Note: XI, X2, Yl , Y2, refer to learning outcomes 50 In addition, differences in understanding Le Chatelier's Principle in the various formats were analyzed in accordance with the three groups of questions: symbolic formula understandings, graphs interpretation abilities, and model drawing conceptions. See Table 9 below. Table 9. Distribution of Learning Outcomes According to Three Question Type -Analogous computer simulation Non-analogous computer simulation Symbolic formula understandings LI M l Graphs interpretation abilities L2 M2 Model drawing conceptions L3 M3 Note: Al, A2, A3, Bl, B2, B3 refer to learning outcomes The results which have been analysed at a p < 0.05 probability level revealed whether there is a significant difference in treatment and control group post test scores. However, possibly due to the limitation of the sample size (n=15), some of the test results did not have statistical power. 13.0 Ethical Considerations In order to maintain student confidentiality, all information students provided through the study was anonymous. For all tests, surveys and instructional sheets for simulation activities, students were asked to provide an ID number. The ID number was a specially designed number not related to any student's name or his/her school student ID. A chemistry teacher who had regularly taught the class participated in the study class but did not interact with the students during the treatment study. The test and survey results were not submitted to this teacher and did not play any role in student grading for the chemistry course. Moreover, the researchers did not have access to student records with grades indicating prior achievement. A class's teachers 51 provided the researcher with a list of student IDs with the corresponding level of achievement (letter 'h ' for high and letter "1' for low). The informed consent of each student and parental consent were obtained in advance, two month before the study was conducted. A l l data collected from the study, such as tests, activity papers, and digital video material is stored in the office of the primary supervisor of the research study, Dr. Samia Khan. U B C Behavioural Ethics Board approval was obtained prior to the study. 9.0 Results and Discussion 9.1 Introduction In order to investigate the extent to which the analogical view in a novel TEMBS instructional computer simulation designed for modeling Le Chatelier's Principle impacted learning outcomes among high school students, the post-tests and survey results were analyzed quantitatively and qualitatively. The discussion below is organized into three units of analysis. First, the individual post-test total scores were analyzed with achievement levels (high and low) as the control variable and the various types of computer-based instruction as manipulated variables; second, the differences in understanding Le Chatelier's Principle in open ended questions were analyzed in accordance with the three groups of post-test questions;7z>za//_K, the results of the student survey were analyzed using descriptive statistics (quantitatively) and qualitatively. 9.2 The analysis of the relationship of the types of instructional computer simulations and levels of achievement (Post-test total scores) The goal of this analysis was to investigate how the analogical view in the instructional computer simulation affects the learning outcomes for high school students who possess high and low grade 12 Chemistry academic achievements. On the day of the treatment study, at the 52 end of the instructionally based interaction with instructional computer simulation, each student completed a post-test. Research Question 1: Are the learning outcomes for 12th -grade chemistry students affected by instructions involving the use of analogical and non-analogical computer simulations? There was a significant relationship between the type of instructional computer simulation used and the total post-test score, t(13) = 2.61(sig.= 0.025), p < 0.05. The mean total of the post-test score for group A (90%) was significantly higher then the mean of the total post-test score for group B (68%). (See Table 10). Therefore, the first null hypothesis that there is no significant difference in the learning outcomes of 12 th -grade chemistry students who are instructed using either analogical or non-analogical computer simulation modeling Le Chatelier's Principle can be rejected. However, a significant interaction between the types of treatment and levels of achievement was not supported by the data. (See Table 11). Table 10. Post-test Results (Group Descriptive Statistics) 1 Treatment N Mean Std. Deviation Std. Error Mean Total Score Analogous (n=15) instructional computer simulation Non-8 89.58 12.400 4.384 analogous instructional 7 68.45 17.97 6.790 computer simulation 53 Table 11. Post-test Results {ANOVA: Test of Between-Subjects effects) Dependent Variable: Total Score Source Type III Sum of Squares df Mean Square F Sig. Noncent. Parameter Observed Power(a) Corrected Model 2486.689(b) 3 828.896 4.152 .034 12.455 .696 Intercept 93786.059 1 93786.059 469.746 .000 469.746 1.000 treatment 1446.314 1 1446.314 7.244 .021 7.244 .689 level 785.256 1 785.256 3.933 .073 3.933 .440 treatment * level 64.103 1 64.103 .321 .582 .321 .081 Error 2196.181 11 199.653 Total 100017.362 15 Corrected Total 4682.870 14 a Computed using alpha = .05 b R Squared = .531 (Adjusted R Squared = .403) Even though the interaction between the types of treatment and levels of achievement did not have statistical power sig. = 0.582 at p <.05 possibly due to the limitations of the sample size (n=15), I considered looking at post-test results for descriptive purposes with a view to identifying major tendencies in the output data. The breakdown of the means of the total scores according to level of achievement (high and low) and type of treatment (analogous instructional computer simulation and non-analogous instructional computer simulation) is shown in Table 12 and graphically represented in Figure 10. Table 12. Post-test Results (Distribution of the Means of the Total Scores in Two Groups according to Prior Level of Achievement) Treatment Level Analogous instructional computer simulation Non-analogous instructional computer simulation Prior High Level of Achievement 95% 79% Prior Low Level of Achievement 84% 60% 54 Estimated Marginal Means of score T r o 1 treatment Figure 10. Estimated Marginal Means of the Total Score in Two Groups. As can be seen from Figure 10 above, the mean of the total scores for students with a low level of prior achievement is affected to a greater extent by interaction with the analogous instructional computer simulation in contrast to the non-analogous instructional computer simulation (84% and 60% respectively), the difference being 24%. On the same graph, the mean of the total scores for students with a high level of prior achievement differs only by 16% from those who interacted with the analogous instructional computer simulation and non-analogous instructional computer simulation (95% and 79% respectively). Even though all students (from 55 both groups of high and low levels of achievement) performed better with the analogous instructional computer simulation, the students with a low level of achievement who interacted with the analogous instructional computer simulation scored higher on the test than students at the same level of achievement who interacted with the non-analogous instructional computer simulation. However, since the statistical power was not significant for the interaction between levels of achievement and types of instructional computer simulation, the second null hypothesis that there is no significant difference in the learning outcomes for 12th -grade chemistry students who possess high and low prior achievement in the Chemistry 12 course and who are instructed using either analogical or non-analogical computer simulations modeling Le Chatelier's Principle could not be rejected. The data analysis may suggest that student interaction with the analogical view in the instructional computer simulation has a positive impact on learning outcomes among high school students; it may also suggest that the analogical view in the instructional computer simulation is especially effective for students with a low level of achievement. It is interesting to note that the data in the present study supports Gabel et al.'s(1980) premise (see Chapter 5.1b) that analogies do allow learners to build on relationships in their prior knowledge and help make formal concepts more accessible to concrete thinkers with a low level of achievement. As suggested in the Gabel and Sherwood (1980) and Battista (1990) studies, low achievers in science operate at the concrete level of thinking and employ spatial visualization, whereas high achievers think at the formal operational level and have developed logical reasoning. Therefore, a dynamic analogical computer simulation which presents the connection between an analogical example and changes in the concentration of chemicals in a dynamic visual format was beneficial for student learning. In fact, students with a low level of achievement who interacted with the 56 analogous instructional computer simulation scored higher on the test (84%) than students at the same level of achievement who interacted with the non-analogous instructional computer simulation (60%). Moreover, I can hypothesize that analogies that are dynamic, interactive, and integrated in instructional computer simulation have a stronger effect on learning outcomes than analogies which are presented in form of text and static pictures. Both treatment groups had equal access to the text-based descriptive analogical instruction at the beginning of the treatment study (see Appendix E), and the group with access to non-analogous instructional computer simulation was instructed with an analogical example presented in the text of the Guided Instructional Sheet (see Appendices C and D) at the time when the group with access to analogous instructional computer simulation had an opportunity to interact with dynamic analogies in the analogical window of the simulation. For example, in Part 1A Step 10 of the Simulation Activity (see Appendix D), students who interacted with the analogical window, in addition to observing graphs and slider windows, followed the instruction: "Observe [on the computer screen] how the side of the scale to which the chemical element was added goes down under its weight and the amount of that element increased. " Students were asked to observe the analogical model. On the other hand, students who had to observe only graphs and slider windows while the analogical window was turned blank followed the instruction in Part 1A Step 10 of the Simulation Activity (see Appendix C): "Recall a scale analogy you have seen on the paper with the graphical example of the scale earlier. Imagine how the balance of the scale will be changed when chemical compounds are added to the system. " Students were asked to recall and imagine the analogical example. It is hypothesized that students may have been imagining and recalling the analogy according to the 57 instructional guidance sheets accompanying the computer simulation. As a result, the group of students who were relying on memory to retrieve the analogy received on average 68% of the total score on the post-test, while the group of learners who had an opportunity to observe the dynamic analogous interaction received on average 90% of the total score on the post-test. Research Question 2: Do instructions involving the use of analogical or non-analogical computer simulations benefit both high and low achieving secondary students in chemistry equally? Even though in the discussion above I explained the possible reasons why in regard to the second research question the null hypothesis was not statistically rejected, I would like to analyse the data for descriptive purposes. The difference in post-test scores between students with high and low achievement levels among those who interacted with the non-analogous instructional computer simulation and analogous instructional computer simulation (see Table 12), can be explained from the analysis of the student level of achievement in relation to the type of operational thinking levels. According to the research literature, most of the students who possess low levels of achievement are considered to be concrete thinkers and those who possess high levels of achievement are considered to be abstract thinkers (Battista, 1990, Gable & Sherwood, 1980). Gabel and Sherwood (1980) suggested that many high school students who possess low achievement level do not operate at the formal operational level defined by Piagetian developmental stages. Students' level of logical thinking accordingly was assessed using a shortened version of the Longeot test (1962, 1965), and the correlation between student scores on the test and levels of achievement was confirmed. Furthermore, Battista (1990) looked at the relationship between two types of reasoning, visual-spatial and verbal-logical, and geometry achievement in high school students. In Battista's study, students who scored poorly on the 58 geometry test exhibited a tendency toward a visual approach to problem solving as opposed to logical reasoning. Such results were explained by van Heile's theory (1986) of levels of thinking in geometry. In the van Heile hierarchy, at first, at the visual level, the students' reasoning is dominated by attention to the visual characteristics, and at higher levels, such as analysis, abstraction, etc., an analytic approach gradually replaces the visual one. Both, Gabel and Sherwood (1980) and Battista (1990) proposed that low achievers in science operate at the concrete level of thinking and employ spatial visualization, whereas high achievers think at the formal operational level and have developed logical reasoning. Hence, I hypothesize that for abstract thinkers the process of linking an analogical example from prior knowledge and observed new phenomena (dynamically changing graphs and symbolic representation of a chemical process), employing imagination is easier than is the case for concrete thinkers who benefit more from observing (rather than imagining) interactive animation of the analogical example synchronized with the changes in graphs and symbols (please see the third step in 5-step strategy employed in this study for scaffolding students in constructing mental imagery in Chapter 8.5). The above analysis may imply that analogous instructional computer simulation facilitates minimising the gap in acquired knowledge between abstract and concrete thinkers. The use of computer simulation tools provides an opportunity to make abstract and unobservable concepts visualized by facilitating the representation of students' knowledge and scaffolding students' learning processes. Synchronization of the analogy and the chemical concentrations in a graph view fosters students' understanding of the unobservable underlying chemical process by creating a visual representation of the chemical equilibrium process by means of analogy, not merely by appealing to students' memory and visual mental imagery. Thus, the data supports the 59 argument made in this study that the dynamic analogical view in instructional computer simulation as an alternative to recalling an analogy from prior knowledge is beneficial for the student learning process. 9.3 The analysis of symbolic, graphical and model-drawing conceptions (Three types of post-test question formats) In this analysis the goal was to investigate how the analogical view in the instructional computer simulation affects the student's ability to comprehend the learning material in open ended questions with three types of understanding being tested: symbolic formula understanding, graph interpretation abilities, and model drawing conceptions. For these purposes, the post-test questions were divided into three groups and analysed accordingly for the two groups of students - those who interacted with the analogous instructional computer simulation and those who interacted with the non-analogous instructional computer simulation. Due to the limitation of the sample size, an additional analysis with achievement levels (high and low) as control variables was not performed. A summary of the question types can be found in Table 13 below and the question description is included in Appendix A. The averages for the total scores for each of the questions types and the distribution of those scores among the two groups of students can be found in Table 14. Table 13. Three Types of Post-test Questions Post-test questions Symbolic formula understandings 1,3,5-10 Graphs interpretation abilities 2,4a-b Model drawing conceptions 4c 60 Table 14. Means of Post-test Scores According to Three Question Types Questions Analogous instructional computer simulation Why are there two numbers in some cells? Non-analogous instructional computer simulation Symbolic.formula understandings 88% 69% Graphs interpretation abilities 100% 76% Model drawing conceptions 70% 43% Table 15. Post-test Scores According to Three Question Types (Group Descriptive Statistics) t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Symbolic formula understandings -2.590 13 .022 -.19518 .07536 -.35799 -.03236 Graphs interpretation abilities -1.926 13 .076 -.18286 .09496 -.38801 .02230 Model drawing conceptions -1.505 13 .156 -.32143 .21360 , -.78288 .14002 As Table 15 shows, there was a significant relationship between the types of instructional computer simulation and symbolic formula understanding learning outcome t(13) = 2.59(sig.= 0.022), p < 0.05. However, for graph interpretation abilities and model drawing conceptions, a significant relationship was not found between the types of instructional computer simulation and these specific learning outcomes due to the statistical power of the sample size. Nevertheless, the tendency to score higher for a group with analogous instructional computer simulation can be noticed in each of the types of test questions. (See Table 14). This 61 difference in mean test scores may indicate that the analogical view in the instructional computer simulation is beneficial for all three question categories. The test question relating to model drawing conception was challenging for the students in both study groups (with analogical and non-analogical simulation) as model drawing was an unfamiliar concept for students. The low results for the model drawing question (43% for group A and 70% for group B) might be explained by the fact that the students did not have previous knowledge or skills to express the model in graphic form. The fact that the students from group A did not pass the test (43% was the mean of the total score) might suggest that the students who interacted with the non-analogical computer simulation not only did not know how to express the model in drawing, but also that they had not formed a clear concept of a mental model since they did not have a chance to interact with the analogical computer simulation. Model-based teaching traditionally is not given prominence in the chemistry curriculum (Khan, 2004). The intervention was considered to be an example of such a teaching approach; however, model-based teaching was not a predominant method of teaching in this class as described in a previous study by Khan (2004). However, it is very interesting to see from the mean differences that the interactive analogical view in the instructional computer simulation appeared to foster comprehending and improving learning outcomes related to chemical equilibrium and Le Chatelier's Principle. For example, in question 4c of the Post-test (see Appendix A) the learners were asked to draw what they think is happening at the molecular level for the chemical process represented by the formula 2NOCl(g) <=> 2NO(g) + Cl2(g> after stress is applied to the system. The students were encouraged to illustrate the change in concentration over three time periods (tO, t l , and t2). A legend and Time to model were given to the students as a guiding example since the model-drawing concept was new to them. Most of the students in the group with analogous instructional 62 computer simulation (87% of the students in the group) and the majority of the students with non-analogous instructional computer simulation (71% of the students in the group) were able to complete the Time t l model when stress was applied (3 molecules of one of the chemical components were added to the system at equilibrium). Since the model for Time tO was already given to the students, those students who understood the molecular representation of the Time tO model given as an example, were able to draw the model for Time t l depicturing the same Time tO model with an addition of three more molecules. TO o ® o • ® • ® o © o T l However, for the Time t2 model, a conceptual understanding of the chemical process occurring as the system returned to equilibrium was required from the students. The learners needed to evaluate the change in the number of molecules representing three chemical compounds from the formula according to Le Chatelier's Principle. The changes in the numbers of all three kinds of molecules (three chemical compounds) should have agreed with the equation 2NOCl(g) <=> 2NO(g) + Ci2( g ) . In this task a conceptualization of unobservable chemical processes at the molecular level and understanding of Le Chatelier's Principle was essential. 43% of the students with the non-analogous instructional computer simulation and 70% of the students with analogous instructional computer simulation were able to complete the task. (Please see Appendix K). Even though this result for the model-based question does not have a statistical power, possibly due to the limitation of the sample size, the difference in the post-test scores may 63 imply that analogous instructional computer simulation fosters model-based conceptions among high school students. The fact that only 43% of the students with the non-analogous instructional computer simulation and 70% of the students with the analogous instructional computer simulation were able to complete the task is promising and suggests that educators may wish to consider model-base teaching via computer simulations, such as the TEMBS simulation, along with guided instructional sheets, as a method to improve student learning. 9.4 The analysis of student perception of the different types of instructional approach in the chemistry classroom (Surveys) The goal of this analysis was to examine student perspectives on what instructional activities in the chemistry classroom students find more useful in their learning practices and whether such perspectives has difference in relationship to the types of instructional computer simulation (analogous and non-analogous). A survey was administered at the end of the treatment study after students completed the post-test. Survey questions were in the form of agree-disagree statements about student preferences with regard to classroom instructional activities and student perceptions on learning in the chemistry class. (See the survey in Appendix F). The results of the survey can be found Appendices G, H, I, and J. The mean and standard deviation for each question assigned each of the two treatment groups were examined to assist with the interpretation of the frequencies and make a comparison. The difference between the answers of the two treatment groups did not have statistical power. (See Appendix G). Therefore, I will discuss the general tendencies in students' answers based on the analysis of the modes of both groups of students' responses as a whole to each question, all with a view to measuring the central tendency. Please see Tables 16a, 16b and Figure 11. Table 16a. Survey Results Part 1 (Questions 1-9) Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 N Valid 15 15 15 15 15 15 15 15 15 Missing 0 0 0 0 0 0 0 0 0 Mean 1.8000 3.4667 1.8000 2.2667 2.0000 2.1333 1.7333 2.0667 2.6667 Median 1.0000 4.0000 2.0000 2.0000 2.0000 2.0000 2.0000 2.0000 3.0000 Mode 1.00 3.00(a) 1.00(a) 3.00 2.00 1.00 1.00 1.00 3.00 Std. Deviation 1.20712 1.35576 .77460 .79881 .84515 1.12546 .88372 1.09978 1.04654 Variance 1.457 1.838 .600 .638 .714 1.267 .781 1.210 1.095 Range 4.00 4.00 2.00 2.00 3.00 3.00 3.00 3.00 4.00 Table 16b. Survey Results Part 2 (Questions 10 -18) Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 N Valid 15 15 15 15 15 15 15 15 15 Missing 0 0 0 0 0 0 0 0 0 Mean 2.2000 3.3333 2.9333 1.8000 2.2667 3.1333 1.7333 2.4000 3.0000 Median 2.0000 4.0000 3.0000 2.0000 2.0000 4.0000 2.0000 2.0000 3.0000 Mode 2.00 4.00(a) 3.00 2.00 1.00 4.00 2.00 1.00 3.00(a) Std. Deviation 1.08233 1.49603 1.43759 .86189 1.27988 1.55226 .70373 1.50238 1.36277 Variance 1.171 2.238 2.067 .743 1.638 2.410 .495 2.257 1.857 Range 3.00 4.00 4.00 3.00 3.00 4.00 2.00 4.00 4.00 65 Figure 13. Survey Results (Frequency Bar Charts) 1 2 3 Students' responses (Likert scale) 1 2 3 Students' responses (Likert scale) mm $0m mi 4 Students" responses (Likert scale) 3 Students' responses (Likert scale) if L s 1 2 3 Students' responses (Likert scale) 4 Students' responses (Likert scale) 66 Q 7 1 2 3 Students' responses (Likert scale) 1 2 . 3 Students' responses (Likert scale) Q 9 2 3 * Students" responses (Likert scale) 2 3 Students responses (Likert scale) 2 3 * Students' responses (Likert scale) 1 2 3 ^ Students' responses (Likert scale) Q13 1 2 J Students' responses (Likert scale) 1 2 3 Students' responses (Likert scale) 2 3 4 Students' responses (Likert scale) 1 2 J Students' responses (Likert scale) 1 2 3 4 5 Students' responses (Likert scale) 1 2 3 . 4 Students' responses (Likert scale) 68 According to data gathered from the first two questions of the survey, most of the students find chemistry an interesting and understandable subject (73% for question one and 56% for question two). Only 6.7% of the students disagreed with the statement that chemistry is an interesting and understandable subject, and 20% of the students consider chemistry to be too abstract to understand deeply. (See Appendix I). In Questions 3 to 5, students were asked about their ability to solve chemistry problems. The majority of the students were confident with regard to solving chemistry problems and understanding a scientific approach for investigating chemistry processes (80% strongly and generally agree responses for Questions 3 and 5. See Appendix I). However, the students were less confident (the answers tended to be close to the neutral 3 point on the scale) about developing hypotheses themselves. This analysis suggests that the students are self-assured with regard to solving chemistry problems using a set of rules, but they have difficulty in explaining the chemical process when they encounter a new problem and need to generate an explanation. Therefore, the following survey questions investigate the students' approach to solving problems. The purpose of Questions 6 to 8 was to examine students' interest and persistence in solving chemistry problems. It is interesting to see that the students understand the importance of conceptualising chemistry processes. The learners are persistent in understanding the rules (86.7% strongly and generally agree responses for Question 7), and they value qualitative rules and concepts that are descriptive and non-mathematical as one of the aids for comprehending chemistry problems (only 13% of the students find qualitative rules not helpful as revealed in answers for Question 6) The purpose of Questions 9 to 14 and 16 was to find out what instructional and self -learning approach for understanding chemistry concepts students find effective; and the purpose 69 of Questions 15, 17, and 18 was to gain the information on how students may have employed the TEMBS instructional computer simulation in their learning process. As you can see from the data tables and bar charts (See Appendix I), even though the students did not always have a clear opinion on whether an instructional computer simulation made unobservable processes in chemistry more explicit for them (46.7% of the students had 'neutral' opinion), they were quite certain that the in-class simulation did not compound their confusion instead of clarifying the concept (only 26.7% of the students found instructional computer simulations confusing). Question 12 was important in identifying student preferences for an instructional approach, for example in determining whether they prefer an instructor to provide them ready-made data and rules or whether they prefer to investigate the concept and generate rules/hypothesis themselves. It is interesting to see that students did not have a unified opinion on this question. As you can see from the Bar Chart (Figure 13) and Frequency Distribution Table (Appendix I), many of the students (26.7%) are neutral, and there are as many who agree (40%) as those who disagree (33.3%). However, in Question 13, the students revealed that a process of generation, evaluation, and modification of the rules contributes to their understanding of the concepts in chemistry (86.7% strongly and generally agree responses). Moreover, in Question 17, the students confirmed that it was in class instructional computer simulation that was used for testing the boundaries of their rules and ideas about chemistry (53%), and in Question 15, the students stated that instructional computer simulations are useful for seeing patterns in data (53.3%). Data from the surveys seem to suggest that instructional computer simulation is a useful educational tool in model-based teaching and learning (in general, over 50% of the students found computer simulation valuable in model-building processes such as generation, evaluation and modification of their scientific hypotheses). 70 Question 19 was designed to elicit student opinion regarding the most helpful factors in learning chemistry. As you can see from Table 17 and Figure 12, the most useful learning factors identified by students were classroom notes and lectures by the teacher and teacher questions and interaction with students during class. Interaction with the simulation and reading the textbook were the least useful factors in students' opinion. Such an opinion may be explained by the fact that instructional computer simulation is a new educational tool, and students did not have enough time to form a comprehensive opinion about instructional computer simulation after only forty five minutes of interaction with it. However, the fact that students value most teacher guidance in the learning process may tell educators that instructional computer simulation would be most effective under teacher guidance, or, at least in combination with instructional guidance sheets. The responses to Question 19 of the present survey confirm Khan's (2002) study data (See Chapter 5.2c), which suggested that a guided discovery approach was most effective when students had learned as much as possible by asking questions and engaging in activities with their teacher. Table 17. Question #19 Results (statistical summary) q19a q19b q19c q19d q19e q19f N Valid 14 14 14 14 14 14 Missing 1 1 1 1 1 1 Mean 3.3571 4.7857 3.1429 ,2.4286 4.8571 2.2857 Median 3.0000 5.0000 3.0000 2.0000 5.0000 2.0000 Mode 2.00(a) 6.00 3.00 2.00 6.00 1.00 Std. Deviation 1.21574 1.47693 1.40642 1.39859 1.29241 1.63747 Variance 1.478 2.181 1.978 1.956 1.670 2.681 Range 4.00 5.00 4.00 4.00 4.00 5.00 71 Survey Q19 Demonstration using Interaction with the Laboratory Teacher questions Reading the textbook Classroom notes chemicals simulations and lectures Preferences |p1-most important; 6 - least important | Figure 12. Question #19 Results (graphs). 10.0 Conclusion The objectives of my study were to investigate: (1) whether the learning outcomes for 12 th -grade chemistry students are affected by instructions formatted in different types (either analogous or non-analogous) of computer simulations modeling Le Chatelier's Principle; and (2) whether there is a difference in the effect of an analogical view in instructional computer simulation on students who posses either high or low 12 th -grade chemistry academic achievement. To answer my first question, the analysis of the data suggested that analogous instructional computer simulations have a positive impact on the learning outcomes for 12 th -grade chemistry students. I was able to reject the null hypothesis that there is no significant difference in the 72 learning outcomes for 12 t h -grade chemistry students who are instructed using either analogical or non-analogical computer simulations modeling Le Chatelier's Principle. Students who interacted with analogous computer simulations performed with significantly higher post-test results than those who interacted with non-analogous computer simulations. Moreover, I hypothesized, based on the research in dynamic visualisation and learning (see Chapter 6.2c and 6.2d), that analogies that are dynamic, interactive, and integrated in an instructional computer simulation have a stronger effect on learning outcomes than analogies which are presented in the form of text and static pictures. The value of the dynamic animated analogy in a computer simulation compared with the static pictorial teacher analogy is not obvious. According to cognitive load/overload theory (Mayer, 2002; Bodemer et al., 2005; Hegarty, 2004) dynamic visualizations impose demands on student cognition and, potentially, may limit the effective use of computer technologies. However, as Hegarty (2004) emphasized, there is a difference in student comprehension and cognitive load demand between dynamic interactive and non-interactive displays. Moreover, as Bodemer et al. (2005) determined, integrated instruction packages can aid learning by reducing extraneous load on working memory. When learners explore dynamic and interactive visualisations they are often not able to interact with them due to missing pre-requisite knowledge such as the coherent mental integration of the pictorial and symbolic sources of information (Bodemer et al., 2005). In order to support learners in understanding dynamic visualisations during simulation-based discovery learning, the active integration of static representations before processing dynamic visualisations was suggested by Bodemer et al. (2005). In my study, the animated analogies in computer simulation were implemented in a way that reduced the possibility of cognitive overload. First, the analogical view was not only 73 dynamic, but interactive as well. Second, the computer simulation was accompanied by instructional guidance sheets and featured integrated instructions in the simulation interface. Third, the static pictorial example of the analogy was presented to the learners prior to interaction with computer simulations. Furthermore, based on my theory of Dynamic Mental Imagery Construction in Model-Based Reasoning (see Figure 3), the proposed Five-Step Instructional strategy (see Table 4), and the analysis of my study data, I suggest that dynamic interactive visualization of the link between analogy (e.g. scale balance) and learned concept (e.g. concentration change in chemical reaction) can be beneficial for student understanding. For my second question, I was not able to reject the null hypothesis (that there is no significant difference in the learning outcomes for 12th -grade chemistry students who possess high and low prior achievement in the Chemistry 12 course and who are instructed using either analogical or non-analogical computer simulations modeling Le Chatelier's Principle), possibly due to the limitations of the sample size. However, some of the tendencies for higher learning outcomes in the students' post-test scores were interesting to analyse. For example, I hypothesized, based on the Gabel and Sherwood (1980) and Battista (1990) studies (that suggested a difference in the comprehension of analogies for students who think on different operational levels), that for abstract thinkers, the process of linking an analogical example from prior knowledge and observed new phenomena by employing imagination is more accessible, than for concrete thinkers who benefit more from observing (rather than imagining) the interactive animation of analogical examples synchronized with changes in graphs and symbols. Those students with a low level of achievement who interacted with the analogous instructional computer simulation scored higher on the test than students of the same level of achievement 74 who interacted with the non-analogous instructional computer simulation. It is plausible to suggest that analogies in instructional computer simulations do allow learners to build on relationships in their prior knowledge and help make formal concepts more accessible to concrete thinkers with a low level of achievement. 11.0 Implications My study has aimed at making a theoretical contribution to the understanding of the impact of dynamic, runnable analogies on students' understanding of complex phenomena, such as Le Chatelier's Principle. According to the data analyzed, computer simulation technologies incorporating dynamic analogies were found to be advantageous for students' conceptual understanding. The results have supported the existence of a consistent difference between two groups of grade 12 Chemistry students in terms of their comprehension of chemical equilibrium and Le Chatelier's Principle provides an empirical basis for pursuing the development of a hypothesis that dynamic mapping of the analogical to observed phenomena benefits students' visualization of abstract concepts and promotes mental model development. Furthermore, the instructional computer simulation may present a prototype for the integration, extension, and enhancement of teaching strategies, such as analogies and computer simulations integrated with instructional work sheets, reported to foster positive learning outcomes in science. Using new technology in the classroom is a beneficial as well challenging process. New software requires time to learn for both teachers as well as students. The video tutorial on simulation functionality is available as an aid for becoming familiar with the software. Another challenge for student comprehension might lie in one of the mismatching characteristics of the analogical examples and chemical equilibrium concept. In the analogical example the chemicals move from one side of the scale to another while in the chemical process there are no 75 sides. A molecular view of the simulation can be used by the teacher for the purpose of clarification and for emphasizing the mismatching features of the analogical and new processes. Furthermore, the Five Step Strategy proposed in this paper is recommended as a teaching strategy for employing analogical instructional computer simulation in the classroom. The Five Step Strategy aids in enhancing student comprehension and linking the analogical example with the learned chemical equilibrium and Le Chatelier's Principle. The computer simulation, which was designed for the present study as open source educational software, will be accessible for any participating school, and this resource may contribute to the enhancement of learning with technologies for public education. As open source software the simulation is subject to modification and improvement in the design and functionality. One of the possible changes in the interface that can be done in order to minimize cognitive overload is that the colour scheme could be further improved by making all elements corresponding to particular chemical information displayed in the interface in one colour such as concentration numbers in the Slider View. Also, having the rewinding feature of the Graph View correlated with the rewinding feature of the Analogy View might be beneficial for student comprehension. 12.0 Recommendations for Future Research Based on the results and conclusions of my study, I would like to suggest several recommendations for future research. First, to eliminate Type I errors in decision-making processes relating to the elimination of the null hypothesis, the limitation of the sample size should be avoided and a larger study group should be selected. Second, to test more thoroughly the hypothesis about different study outcomes for concrete and abstract thinkers, it would be appropriate before the study to test students regarding their formal or concrete operational levels 76 of thinking. Third, to control randomization of the study, the Pretest-Posttest Control Group design could be applied. Pre-testing students could help to control the randomization of the variables. Fourth, to avoid limitations regarding the generalization of the study, more time for student interaction with the instructional computer simulation should be allocated. Fifth, it would be interesting to see in future studies the effect of the analogous instructional computer simulations during the first introduction of the concept of chemical equilibrium and Le Chatelier's principle in the curriculum. In the present study, the students interacted with the analogous instructional computer simulations during a review of the material at the end of the term. Sixth, it would be valuable to investigate whether static analogical instructions provided at the beginning of the class might be advantageous for student conceptualization of dynamic analogical examples featured in the analogous instructional computer simulations. 77 References Armbruster, B (1996) "Schema Theory and the design of content-area textbooks." Educational Psychologist, 21, 253-276. Barbour, I. (1976). 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Secondary Students' Dynamic Modeling Processes: Analyzing, Reasoning About, Synthesizing, and Testing Models of Stream Ecosystems. Journal of Science Education and Technology, 7(3), 215-234. Sweller, J. (1998). Cognitive load during problem solving: Effects on learning. Cognitive Science 12, pp. 257-285. Wu, H., Krajcik, J.S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students' use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38 (7), 821-842. 82 Appendices A. TEMBS POST TEST Please fill the answers in the spaces provided below. Your answers will be kept confidential. Your personal information is not disclosed. 1. Consider the following equilibrium: C02(g) + H2(g ) <^> H 20 ( g) + CO( g ) a) Equilibrium shifts to the right when H2(g) is added to the system. Describe the changes in reaction rates that cause this shift to the right. 2.Consider the following equilibrium and accompanying graphs: C0 2 ( g ) + H 2 ( g ) <?> H 20 ( g) + CO(g) B. h -ti time ti time ti time time 83 (Please put a letter for the graph)Which of the following graphs represents the graph of concentration change of C02(g> when H20(g) is added to the above equilibrium at time = ti ? 3. (Please circle the answer) Equilibrium is reached in all reversible chemical reactions when the: rates of the opposing reactions become equal, forward reaction stops, reverse reaction stops. concentrations of the reactants and the products become equal. 4. Consider the following equilibrium and accompanying graph: 2NOCl ( g ) <s> 2NO ( g) + Cl 2 (g) 2NOCl ( g ) 2NO t3 a) Identify the stress applied at ti. b) Complete the above graph from ti to 13 for the 2NO(g) and 2NOCl(g) c) Draw what you think is happening at the molecular level in the situation above. Draw and explain how molecules of 2NO (g), C l 2 (g> and 2NOC1 (g) will change in concentration for the following time period. A legend and Time to model are given below i) Time to Legend: NOCl = NO = O C l 2 = • 84 ii) Time ti Stress is applied 3 molecules of CI2 are added • • • iii) Time t2 5. Write any two characteristics that apply to chemical equilibrium systems. 1. 2. 6. Methanol, C H 3 O H , is produced by the following reaction: CO(g) + 2 H 2 ( g ) CH 3OH( g) + heat State two different methods of shifting the equilibrium to the right. i) : ii) . . 7. Consider the following equilibrium: 2NO (g) + 2CO(g) o . 2CO 2(g)+ N 2(g) (AH=-747kJ/mol) (Please circle the answer) Predict the effect on the equilibrium concentration of CO 2, i f N 2 is added to the reaction D. Increases E. Decreases F. No change 8. Consider the following equilibrium: 85 HCl( g) + N H 3 ( g ) o- NH 4 C1 ( S ) f (Please circle the answer) Predict the effect on the direction of equilibrium shift if some NH3 is removed from the system A. Right, favour products B. Left, favour reactants C. No change 9. Consider the following equilibrium: 2 N 2 O 5 (g) <=> 4NO 2 (g) + O 2 (g) (AH = 110.2kJ/mol) (Please circle the answer) Predict the effect on the direction of equilibrium shift i f more oxygen is added A. Right, favour products B. Left, favour reactants C. No change 10. Consider the following equilibrium: 2 N 2 O 5 (g) <=> 4NO 2 (g) + O 2 ( g ) (AH = 110.2kJ/mol) (Please circle the answer): Predict the effect on the rate of the forward reaction of more N 2 O 5 is added A. Increases B. Decreases C. No change 86 B. TEMBS POST-TEST M A R K I N G SCHEME Student ID: 1. Consider the following equilibrium: C0 2( g) + H 2 ( g ) H 2 0 ( g ) + CO(g) a)Equilibrium .shifts to the right when H 2( g) is added to the system. Describe the changes in reaction rates that cause this shift to the right. Answer: At the exact moment of adding H?. the forward rate increases and the reverse rate does not change. The reaction shifts to the right. As the reaction progresses, the forward rate decreases and the reverse rate increases. When the forward and reverse rates become equal, the equilibrium is reached. (1 mark) 2.Consider the following equilibrium and accompanying graphs: C 0 2 ( g ) + H 2 ( g ) o H 2 0( g ) + CO(g) A. C. B. ti time time D. ti time ti time (Please put a letter for the graph)Which of the following graphs represents the graph of concentration change of C0 2( g) when H 20( g) is added to the above equilibrium at time = ti ? Answer: C (1 mark) 87 3. (Please circle the answer) Equilibrium is reached in all reversible chemical reactions when the: rates of the opposing reactions become equal, forward reaction stops, reverse reaction stops. concentrations of the reactants and the products become equal. Answer: A (1 mark) 4. Consider the following equilibrium and accompanying graph: 2NOCl ( g) o 2NO ( g) + C l 2 ( g) 2NOCl ( g ) t ' 2NO ( g ) C 1 2 ( g ) 0 a) Identify the stress applied at t\. Two Possible Answers: 1) Cb(g) is added to the system. 2) the volume decreases -> the system pressure increases ->concentration of all three species increase. (1 mark) b) Complete the above graph from ti to t3 for the 2NO(g) and 2NOCl(g) 2NOCl ( g) 2NO ( g ) C 1 2 ( g ) 0 tl t2 t3 88 c) Draw what you think is happening at the molecular level in the situation above. Draw and explain how molecules of 2NO (g), C l 2 (g> and 2NOC1 (g) will change in concentration for the following time period. A legend and Time to model are given below (1 mark) i) Time to Legend: NOC1 = <g) N O = 0 C l 2 = # ii) Time ti Stress is applied 3 molecules of C l 2 are added • • • iii) Time t2 4, O = 4 ' • =5 • < 5 > ® > 4> o < 4 5. Write any two characteristics that apply to chemical equilibrium systems. Answers: 1) When a system is at equilibrium is subjected to a disturbance, the composition of the system adjust to as to tend to minimize the effect of the disturbance. 2) Any equilibrium is a dynamic balance between reactants and products (1 mark for both, or 0.5 for each) 6. Methanol, C H 3 O H , is produced industrially by the following reaction: CO(g) + 2 H 2 ( g ) C H 3 O H ( g ) + heat State two different methods of shifting the equilibrium to the right. 89 Possible Answers: 1) Increase pressure, 2) Lower temperature, 3)Constantly remove CH 3 OH( g ) , 4) constantly add CO(g> + 2FL;(g) (1 mark for any two) 7. Consider the following equilibrium: 2NO(g) + 2CO(g) <=> 2CO 2(g)+N 2(g) (AH=-747kJ/mol) (Please circle the answer) Predict the effect on the equilibrium concentration of CO 2, i f N 2 is added to the reaction A. Increases B. Decreases C. No change Answer: B (1 mark) 8. Consider the following equilibrium: H C l ( g ) + N H 3 ( g ) o . N H 4 C 1 ( S ) (Please circle the answer) Predict the effect on the direction of equilibrium shift i f some NH3 is removed from the system A . Right, favour products B. Left, favour reactants C. No change Answer: B (1 mark) 9. Consider the following equilibrium: 2 N 2 O.5 _) <#> 4NO 2 ( g) + O 2 (g) (AH = 110.2kJ/mol) (Please circle the answer) Predict the effect on the direction of equilibrium shift if more oxygen is added A. Right, favour products B. Left, favour reactants C. No change Answer: B (1 mark) 10. Consider the following equilibrium: 2 N 2 O 5 (g) <=> 4NO 2 (g) + O 2 (g) (AH = 110.2kJ/mol) (Please circle the answer): Predict the effect on the rate of the forward reaction of more N 2 is added A. Increases B. Decreases C. No change Answer: A (1 mark) 91 C. SIMULATION ACTIVITY #1 ID Please fill the survey below. Your answers will be kept confidential. Your personal information is not disclosed. PART1A: Go to the simulation on your computer. 1. Press START and observe for 30 seconds the positions of sliders and graphs for reaction PC15 o PC13 + C l 2 2. Press STOP Question 1: Is the above reaction at equilibrium? Question 2: How can you tell (what characteristics can you identify to confirm your answer) ? Question 3: Draw a model (representation of molecules) of what you think the chemical equilibrium looks like at the molecular level. The legend is given below. The box represents the system. Please draw up to 10 molecules in the box. 92 3. Press START in the simulation window: Recall a scale analogy you have seen on the paper earlier. Imagine chemical compounds from left side of the reaction stored on one side of the scale and chemical compounds from right side of the reaction stored on another side of the scale. Question 4: State an equilibrium property about forward and reverse reaction rates. 4. Press STOP 5. Move slider o f C l 2 to 28-30 6. Press START and observe for 60 seconds the changes in the graphs and sliders 7. Press STOP when reaction reaches equilibrium. 8. Press RESET 9. Move slider o f P C l 5 to 28-30 10. Press START and immediately after the scale moves down press STOP to observe the changes in the graphs and sliders. (Note: do not wait until the system reaches equilibrium) Recall a scale analogy you have seen on the paper earlier. Imagine how the balance of the scale will be changed when chemical compounds are added to the system. Question 5: a) Predict what changes will happen to the system in order to re-establish equilibrium. b) In which direction does the equilibrium shift? c) Explain why the concentration of the substance PCI5 which was increased during the moment when a stress was applied to the system, needs to be decreased by the time when equilibrium is re-established. 93 11. Press START and observe how the equilibrium is re-established 12. Press STOP when reaction reaches equilibrium Question 6: State an equilibrium property about continuous reaction rates. P A R T I B : 13. Press RESET 14. Go to the Menu bar -> VIEW -> select Molecular 15. Press START and observe for 30 seconds the positions of sliders and graphs for reaction PC1 5 PC13 + C l 2 16. Press STOP Question 1: Is the reaction at equilibrium? Question 2: How can you tell (what characteristics can you identify to confirm your answer)? 17. Move slider of PC13 to 27-28 18. Press START and observe for 60 seconds the changes in the graphs and sliders 19. Press STOP when reaction reaches equilibrium Note: If you'd like to repeat simulation run in order to answer questions - press RESET , move slider of PC13 to 27-28 and press START (and then STOP when reaction reaches equilibrium) Question 1: Was the reaction at equilibrium when you pressed start? Question 2: What dynamic changes did you see in the graphs during simulation run? Question 3: What dynamic changes did you see in the molecular view during simulation run? 94 Question 4: What dynamic changes did you see in the position of sliders of all substances during simulation run? PART 2A: 20. Press RESET 21. Go to the Menu bar -> VIEW -> select Analogy 22. Go to the Menu bar -> EDIT -> select Prediction 23. Go to the Menu bar -> FILE -> Open Reaction -> select tembs_config_N2_3H2_NH3.xml, press OPEN 24. Press START, observe for a few seconds and wait until the question pops-up for reaction N 2 + 3H2 <*> 2NH3 NOTE: If you pause the simulation you can double click on a dot in the graph view and the simulation rewinds to that point in time. 25. Answer question 1 (put a letter for your answer here on the paper and then select the answer on a computer screen) 26. Press PREDICT, find the correct answer. If your initial answer was not correct, can you explain the correct answer after you watched it? 27. Press CONTINUE and observe the simulation until next question pops-up 28. Answer question 2 (put a letter for your answer here on the paper and then select the answer on a computer screen) 29. Press PREDICT, find the correct answer. If your initial answer was not correct, can you explain the correct answer after you watched it? 30. Press CONTINUE and, then STOP. PART 2B: 31. Go to the Menu bar -> VIEW -> select Molecular 32. Press START, observe for a few seconds and wait until the question pops-up for reaction N2 + 3H2 <^> 2NH3 95 33. Answer question 3 (put a letter for your answer here on the paper and then select the answer on a computer screen) 34. Press PREDICT, find the correct answer. If your initial answer was not correct, can you explain the correct answer after you watched it? 35. Press CONTINUE and observe the simulation until next question pops-up 36. Answer question 4 (put a letter for your answer here on the paper and then select the answer on a computer screen) 37. Press PREDICT, observe the correct answer. If your initial answer was not correct, can you explain the correct answer after you watched it? 38. Press CONTINUE and, then RESET 39. Raise your hand when you finished. 96 D. SIMULATION ACTIVITY #2 ID • Please fill the survey below. Your answers will be kept confidential. Your personal information is not disclosed. PART1A: Go to the simulation on your computer. 1. Press START and observe for 30 seconds the positions of sliders and graphs and a scale for reaction PCI5 <=> PCI3 + CI2 2. Press STOP Question 1: Is the above reaction at equilibrium? Question 2: How can you tell (what characteristics can you identify to confirm your answer)? Question 3: Draw a model (representation of molecules) of what you think the chemical equilibrium looks like at the molecular level. The legend is given below. The box represents the system. Please draw up to 10 molecules in the box. Legend: PC15 = ® PC13 = C l 2 = • 97 3. Press START in the simulation window: Observe elements moving from one side of the scale to another. Notice how red composite element moves to the right side of the scale where it decomposes to blue and green. Also, blue and green elements moves from left side and transforms to red element in order to keep a scale in balance. Question 4: State an equilibrium property about forward and reverse reactions rate which corresponds to moving objects on the scale. 4. Press STOP 5. Move slider o f C l 2 to 28-30 6. Press START and observe for 60 seconds the changes in the graphs and sliders 7. Press STOP when reaction reaches equilibrium. 8. Press RESET 9. Move slider o f P C l 5 to 28-30 10. Press START and immediately after the scale moves down press STOP to observe the changes in the graphs and sliders. (Note: do not wait until the system reaches equilibrium) Observe how the side of the scale to which the chemical element was added goes down under its weight and amount of that element increased. Notice changes on the graphs corresponding to each element's concentration. Question 5: a) Predict what changes will happen to the system in order to re-establish equilibrium. b) Elements of what colour will move to the other side more intensively (faster) at the beginning? c) In which direction does the equilibrium shift? 98 d) Explain why the concentration of the substance PCh which was added/increased when a stress was applied to the system, needs to be decreased by the time when equilibrium is re-established. 11. Press START and observe how the equilibrium is re-established 12. Press STOP when reaction reaches equilibrium Question 6: a) Explain why elements of green and blue colour also continue to move towards the end of the moment when equilibrium is re-established (scale horizontally balanced). b) : State an equilibrium property about continuous reaction rates which corresponds to moving objects on the scale. P A R T I B : 13. Press RESET 14. Go to the Menu bar -> VIEW -> select Molecular 15. Press START and observe for 30 seconds the positions of sliders and graphs for reaction PC1 5 PC13 + C l 2 16. Press STOP Question 1: Is the reaction at equilibrium? • Question 2: How can you tell (what characteristics can you identify to confirm your answer)? 17. Move slider of PC1 3 to 27-28 18. Press START and observe for 60 seconds the changes in the graphs and sliders 19. Press STOP when reaction reaches equilibrium Note: If you'd like to repeat simulation run in order to answer questions - press RESET , move slider of PCI3 to 27-28 and press START (and then STOP when reaction reaches equilibrium) Question 1: Was the reaction at equilibrium when you pressed start? 99 Question 2: What dynamic changes did you see in the graphs during simulation run? Question 3: What dynamic changes did you see in the molecular view graphs during simulation run? Question 4: What dynamic changes did you see in the position of sliders of all substances graphs during simulation run? P A R T 2A: 20. Press RESET 21. Go to the Menu bar -> VIEW -> select Analogy 22. Go to the Menu bar -> EDIT -> select Prediction y 23. Go to the Menu bar -> FILE -> Open Reaction -> select tembs_config_N2_3H2_NH3.xml, press OPEN 24. Press START, observe for a few seconds and wait until the question pops-up for reaction N 2 + 3H 2 <=> 2NH 3 -NOTE: If you pause the simulation you can double click on a dot in the graph view and the simulation rewinds to that point in time. 25. Answer question 1 (put a letter for your answer here on the paper and then select the answer on a computer screen) 26. Press PREDICT, find the correct answer. If your initial answer was not correct, can you explain the correct answer after you watched it? 27. Press CONTINUE and observe the simulation until next question pops-up 28. Answer question 2 (put a letter for your answer here on the paper and then select the answer on a computer screen) 29. Press PREDICT, find the correct answer. If your initial answer was not correct, can you explain the correct answer after you watched it? 30. Press CONTINUE and, then STOP. 100 P A R T 2B: 31. Go to the Menu bar -> VIEW -> select Molecular 32. Press START, observe for a few seconds and wait until the question pops-up for reaction N 2 + 3H 2 «*• 2NH 3 33. Answer question 3 (put a letter.for your answer here on the paper and then select the answer on a computer screen) 34. Press PREDICT, find the correct answer. If your initial answer was not correct, can you explain the correct answer after you watched it? 35. Press CONTINUE and observe the simulation until next question pops-up 36. Answer question 4 (put a letter for your answer here on the paper and then select the answer on a computer screen) 37. Press PREDICT, observe the correct answer. If your initial answer was not correct, can you explain the correct answer after you watched it? 38. Press CONTINUE and, then RESET 39. Raise your hand when you finished 101 E. A N A L O G Y OF A S C A L E - TEXT INSTRUCTIONS. Figure 1: Scale is balanced. 1 A A n A Figure 2: Stress applied to the system A A A A Figure 3: System is in a process to re-establishing balance. Please note how elements from one side move to another side and compose new compounds Figure 4: Equilibrium is re-established i A A 102 F. ? S U R V E Y This survey will be used to inform us about your learning preferences in chemistry in order to improve general chemistry education. It is completely anonymous, but we request your ID# so that they may correlate your responses with simulation study results. At no time will ID#s be matched to names, so please be honest about your opinions. ID Please respond to the statements using the 1 to 5 legend below. Circle the response number next to the question. l=strongly agree 2=generally agree 3=neutral 4= disagree 5=strongly disagree Agree Neutral Disagree 1 2 3 4 5 1 Chemistry is one of the most interesting sciences to study. 1 2 3 4 5 2 Chemistry is too abstract to understand deeply. 1 2 3 4 5 3 I am confident about my ability to solve chemistry problems. 1 2 3 4 5 4 I am comfortable developing hypotheses in chemistry. 1 2 3 4 5 5 I understand how scientists assess and modify theories about unobservable processes. 1 2 3 4 5 6 Qualitative rules or concepts that are descriptive and non-mathematical help me understand chemistry. 1 2 3 4 5 7 When something in science does not behave according to my expectations, I persist until I understand the rules. 1 2 3 4 5 8 It is important for me to understand where the concepts come from in chemistry. 1 2 3 4 5 9 An advantage of the computer simulations is that they make unobservable processes in chemistry more explicit to me. 1 2 3 4 5 10 I have been asked to construct explanations about scientific information that was presented in a computer simulation 1 2 3 4 5 103 11 If I do not understand the concept beforehand, the in-class simulation compounds my confusion instead of clarifying the concept. 1 2 3 4 5 12 This class would be more effective for me if the instructor provided the data and rules instead of asking me to gather data from the simulations in class and generate rules myself. 1 2 3 4 5 13 Having us generate, evaluate, and modify rules in class contributes to my understanding of the concepts in chemistry. 1 2 3 4 5 14 Teacher guidance is necessary for the effective use of simulations. 1 2 3 4 5 15 I find it difficult to see the patterns in the data from the computer simulations. 1 2 3 4 5 16 I generally understand the rules that other students generate and describe in class. 1 2 3 4 5 17 I Sometimes input extreme case data in the class simulations to test the boundaries of my rules and ideas about chemistry. 1 2 3 4 5 18 I modified at least once the initial rules I generated in class when using the simulation. 1 2 3 4 5 19. Rank the most helpful factors from 1 - 6 for you to learn in chemistry using the legend below. Write your response beside each one. (Use each number only once). 1 = most important 2 = 2 n d important 3 = 3 r d important 4 = 4 t h important 5 = 5 t h important 6 = least important Demonstration using chemicals Interaction with the simulations Laboratory Teacher questions and interaction with students during class Reading the textbook Classroom notes and lectures by the teacher 104 G. SURVEY RESUTLS (INDEPENDENT SAMPLES TEST) Levene's Test for Equality of Variances t-test for Equalin / of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Q1 Equal variances 3.772 .074 1.525 13 .151 .91071 .59710 -.37924 2.20067 assumed Equal variances not 1.460 8.535 .180 .91071 .62364 -.51184 2.33327 assumed Q2 Equal variances 1.979 .183 -1.275 13 .225 -.87500 .68653 -2.35816 .60816 assumed Equal variances not -1.313 12.051 .214 -.87500 .66648 -2.32646 .57646 assumed Q3 Equal variances 2.478 .139 1.710 13 .111 .64286 .37588 -.16919 1.45490 assumed Equal variances not 1.652 9.501 .131 .64286 .38905 -.23021 1.51592 assumed Q4 Equal variances .198 .664 1.433 13 .175 .57143 .39868 -.28987 1.43273 assumed Equal variances not 1.429 12.575 .177 .57143 .39983 -.29533 1.43819 assumed Q5 Equal variances .277 .608 1.249 13 .234 .53571 .42891 -.39090 1.46233 assumed Equal variances not 1.223 11.001 .247 .53571 .43789 -.42808 1.49951 assumed Q6 Equal variances .136 .718 -.882 13 .394 -.51786 .58716 -1.78634 .75062 assumed Equal variances not -.873 12.070 .399 -.51786 .59288 -1.80881 .77310 assumed Q7 Equal variances .989 .338 .494 13 .630 .23214 .47024 -.78376 1.24804 assumed Equal variances not .508 12.106 .620 .23214 .45678 -.76214 1.22643 assumed 105 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Equal variances 1.305 .274 1.789 13 .097 .94643 .52915 -.19673 2.08959 assumed Equal variances not 1.839 12.201 .090 .94643 .51456 -.17266 2.06551 assumed Equal variances .807 .385 -.319 13 .755 -.17857 .55989 -1.38815 1.03101 assumed Equal variances not -.311 10.547 .762 -.17857 .57403 -1.44864 1.09150 assumed Equal variances .488 .497 .277 13 .786 .16071 .57959 -1.09142 1.41284 assumed Equal variances not .283 12.802 .782 .16071 .56873 -1.06987 1.39130 assumed Equal variances .723 .411 -.448 13 .662 -.35714 .79737 -2.07975 1.36546 assumed Equal variances not -.455 12.910 .657 -.35714 .78463 -2.05344 1.33915 assumed Equal variances .156 .699 -1.748 13 .104 -1.21429 .69479 -2.71529 .28671 assumed Equal variances not -1.763 12.993 .101 -1.21429 .68883 -2.70250 .27393 assumed Equal variances .022 .884 -.349 13 .733 -.16071 .46076 -1.15612 .83470 assumed Equal variances not -.355 12.801 .728 -.16071 .45211 -1.13898 .81755 assumed Equal variances 3.831 .072 .854 13 .408 .57143 .66889 -.87361 2.01647 assumed / Equal variances not .834 10.651 .423 .57143 .68512 -.94256 2.08542 assumed Equal variances .041 .843 -.631 13 .539 -.51786 .82123 -2.29203 1.25631 assumed Equal variances not -.637 13.000 .535 -.51786 .81304 -2.27432 1.23861 assumed Equal variances .017 .899 2.459 13 .029 .76786 .31225 .09327 1.44244 assumed 106 Equal variances not 2.410 11.063 .034 .76786 .31861 .06710 1.46862 assumed Q17 Equal variances assumed Equal .204 .659 1.112 13 .286 .85714 .77109 -.80870 2.52299 variances not 1.126 12.987 .281 .85714 .76153 -.78822 2.50250 assumed Q18 Equal variances assumed Equal .117 .738 -.747 13 .468 -.53571 .71669 -2.08402 1.01259 variances not -.748 12.758 .468 -.53571 .71637 -2.08632 1.01489 assumed q19a Equal variances assumed Equal .404 .537 -.061 12 .952 -.04167 .68328 -1.53040 1.44707 variances not -.058 8.567 .955 -.04167 .72000 -1.68305 1.59972 assumed q19b Equal variances assumed Equal .197 .665 .456 12 .657 .37500 .82311 -1.41841 2.16841 variances not .476 11.992 .643 .37500 .78840 -1.34290 2.09290 assumed q19c Equal variances assumed Equal 3.223 .098 .053 12 .959 .04167 .79048 -i.68064 1.76397 variances not .049 7.780 .962 .04167 .84829 -1.92416 2.00749 assumed q19d Equal variances assumed Equal .233 .638 -.992 12 .341 -.75000 .75576 -2.39667 .89667 variances not -1.017 11.735 .329 -.75000 .73719 -2.36023 .86023 assumed q19e Equal variances assumed Equal 3.818 .074 -1.355 12 .200 -.91667 .67658 -2.39080 .55747 variances not -1.244 7.203 .252 -.91667 .73665 -2.64869 .81535 assumed q19f Equal variances assumed Equal .016 .902 1.092 12 .296 .95833 .87789 -.95442 2.87109 variances not 1.119 11.726 .286 .95833 ,85667 -.91304 2.82970 assumed H . - S U R V E Y RESUTLS (GROUP STATISTICS) treatment N Mean Std. Deviation Std. Error Mean Q1 0 7 2.2857 1.49603 .56544 1 8 1.3750 .74402 .26305 Q2 0 7 3.0000 1.00000 .37796 1 8 3.8750 1.55265 .54894 Q3 0 7 2.1429 .89974 .34007 1 8 1.5000 .53452 .18898 Q4 0 7 2.5714 .78680 .29738 1 8 2.0000 .75593 .26726 Q5 0 7 2.2857 .95119 .35952 1 8 1.7500 .70711 .25000 Q6 0 7 1.8571 1.21499 .45922 1 8 2.3750 1.06066 .37500 Q7 0 7 1.8571 .69007 .26082 1 8 1.6250 1.06066 .37500 Q8 0 7 2.5714 .78680 .29738 1 8 1.6250 1.18773 .41993 Q9 0 7 2.5714 1.27242 .48093 1 8 2.7500 .88641 .31339 Q10 0 7 2.2857 .95119 .35952 1 8 2.1250 1.24642 .44068 Q11 0 7 3.1429 1.34519 .50843 1 8 3.5000 1.69031 .59761 Q12 0 7 2.2857 1.25357 .47380 1 8 3.5000 1.41421 .50000 Q13 0 7 1.7143 .75593 .28571 1 8 1.8750 .99103 .35038 Q14 0 7 2.5714 1.51186 .57143 1 8 2.0000 1.06904 .37796 Q15 0 7 2.8571 1.46385 .55328 1 8 3.3750 1.68502 .59574 Q16 0 7 2.1429 .69007 .26082 1 8 1.3750 .51755 .18298 Q17 0 7 2.8571 1.34519 .50843 1 8 2.0000 1.60357 .56695 Q18 0 7 2.7143 1.38013 .52164 1 8 3.2500 1.38873 .49099 q19a 0 6 3.3333 1.50555 .61464 1 8 3.3750 1.06066 .37500 q19b 0 6 5.0000 1.26491 .51640 1 8 4.6250 1.68502 .59574 q19c 0 6 3.1667 1.83485 .74907 1 8 3.1250 1.12599 .39810 q19d 0 6 2.0000 1.26491 .51640 V 108 1 8 2.7500 1.48805 .52610 q19e 0 6 4.3333 1.63299 .66667 1 8 5.2500 .88641 .31339 q19f 0 6 2.8333 1.47196 .60093 1 8 1.8750 1.72689 .61055 I. S U R V E Y RESUTLS (FREQUENCY TABLES) Q1 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 9 60.0 60.0 60.0 2.00 2 13.3 13.3 73.3 3.00 3 20.0 20.0 93.3 5.00 1 6.7 6.7 100.0 Total 15 100.0 100.0 Q2 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 2 13.3 13.3 13.3 2.00 1 6.7 6.7 20.0 3.00 4 26.7 26.7 46.7 4.00 4 26.7 26.7 73.3 5.00 4 26.7 26.7 100.0 Total 15 100.0 100.0 Q3 1 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 6 40.0 ' 40.0 40.0 2.00 6 40.0 40.0 80.0 3.00 3 20.0 20.0 100.0 Total 15 100.0 100.0 Q4 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 3 20.0 20.0 20.0 2.00 5 33.3 33.3 53.3 3.00 7 46.7 46.7 100.0 Total 15 100.0 100.0 110 Q5 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 4 26.7 26.7 26.7 2.00 8 53.3 53.3 80.0 3.00 2 13.3 13.3 93.3 4.00 1 6.7 6.7 100.0 Total 15 100.0 100.0 Q6 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 6 40.0 40.0 40.0 2.00 3 20.0 20.0 60.0 3.00 4 26.7 26.7 86.7 4.00 2 13.3 13.3 100.0 Total 15 100.0 100.0 i Q7 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 7 46.7 46.7 46.7 2.00 6 40.0 40.0 86.7 3.00 1 6.7 6.7 93.3 4.00 1 6.7 6.7 100.0 Total 15 100.0 100.0 Q8 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 7 46.7 46.7 46.7 2.00 1 6.7 6.7 53.3 3.00 6 40.0 40.0 93.3 4.00 1 6.7 6.7 100.0 Total 15 100.0 100.0 I l l Q9 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 2 13.3 13.3 13.3 2.00 4 26.7 26.7 40.0 3.00 7 46.7 46.7 86.7 4.00 1 6.7 6.7 93.3 5.00 1 6.7 6.7 100.0 Total 15 100.0 100.0 Q10 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 4 26.7 26.7 26.7 2.00 7 46.7 46.7 73.3 3.00 1 6.7 6.7 80.0 4.00 3 20.0 20.0 100.0 Total 15 100.0 100.0 Q11 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 3 20.0 20.0 20.0 2.00 1 6.7 6.7 26.7 3.00 3 20.0 20.0 46.7 4.00 4 26.7 26.7 73.3 5.00 4 26.7 26.7 100.0 Total 15 100.0 100.0 Q12 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 3 20.0 20.0 20.0 2.00 3 20.0 20.0 40.0 3.00 4 26.7 26.7 66.7 4.00 2 13.3 13.3 80.0 5.00 3 20.0 20.0 100.0 Total 15 100.0 100.0 112 Q13 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 6 40.0 40.0 40.0 2.00 7 46.7 46.7 86.7 3.00 1 6.7 6.7 93.3 4.00 1 6.7 6.7 100.0 Total 15 100.0 100.0 Q14 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 6 40.0 40.0 40.0 2.00 3 20.0 20.0 60.0 3.00 2 13.3 13.3 73.3 4.00 4 26.7 26.7 100.0 Total 15 100.0 100.0 Q15 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 4 26.7 .26.7 26.7 2.00 1 6.7 6.7 33.3 3.00 2 13.3 13.3 46.7 4.00 5 33.3 33.3 80.0 5.00 3 20.0 20.0 100.0 Total 15 100.0 100.0 Q16 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 6 40.0 40.0 40.0 2.00 7 46.7 46.7 86.7 3.00 2 13.3 13.3 100.0 Total 15 100.0 100.0 Q17 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 7 46.7 46.7 46.7 2.00 1 6.7 6.7 53.3 3.00 2 13.3 13.3 66.7 4.00 4 26.7 26.7 93.3 5.00 1 6.7 6.7 100.0 Total 15 100.0 100.0 Q18 Frequency Percent Valid Percent Cumulative Percent Valid 1.00 3 20.0 20.0 20.0 2.00 . 2 13.3 13.3 33.3 3.00 4 26.7 26.7 60.0 4.00 4 26.7 26.7 86.7 5.00 2 13.3 13.3 100.0 Total 15 100.0 100.0 Q19a Frequency Percent Valid Percent Cumulative Percent Valid 2.00 4 26.7 28.6 28.6 3.00 4 26.7 28.6 57.1 4.00 4 26.7 28.6 85.7 5.00 1 6.7 7.1 92.9 6.00 1 6.7 7.1 100.0 Total 14 93.3 100.0 Missing System 1 6.7 Total 15 100.0 — Q19b Frequency Percent Valid Percent Cumulative Percent Valid 1.00 1 6.7 7.1 7.1 3.00 1 6.7 7.1 14.3 4.00 3 20.0 21.4 35.7 5.00 3 20.0 21.4 57.1 6.00 6 40.0 42.9 100.0 Total 14 93.3 100.0 Missing System 1 6.7 Total 15 100.0 Q19c Frequency Percent Valid Percent Cumulative Percent Valid 1.00 3 20.0 21.4 21.4 3.00 6 40.0 42.9 64.3 4.00 2 13.3 14.3 78.6 5.00 3 20.0 21.4 100.0 Total 14 93.3 100.0 Missing System 1 6.7 Total 15 100.0 Q19d Frequency Percent Valid Percent Cumulative Percent Valid 1.00 4 26.7 28.6 28.6 2.00 5 33.3 35.7 64.3 3.00 2 13.3 14.3 78.6 4.00 1 6.7 7.1 „ 85.7 5.00 2 13.3 14.3 100.0 Total 14 93.3 100.0 Missing System 1 6.7 Total . 15 100.0 Q19e Frequency Percent Valid Percent Cumulative Percent Valid 2.00 1 6.7 7.1 7.1 3.00 1 6.7 7.1 14.3 4.00 3 20.0 21.4 35.7 5.00 3 20.0 21.4 57.1 6.00 6 40.0 42.9 100.0 Total 14 93.3 100.0 Missing System 1 6.7 Total 15 100.0 115 Q19f Frequency Percent Valid Percent Cumulative Percent Valid 1.00 6 40.0 42.9 42.9 2.00 4 26.7 28.6 71.4 3.00 1 6.7 7.1 78.6 4.00 1 6.7 7.1 85.7 5.00 1 6.7 7.1 92.9 6.00 1 6.7 7.1 100.0 Total 14 93.3 100.0 Missing System 1 6.7 Total 15 100.0 J. TEMBS POST-TEST RESUTLS (Grader 1) Mm ;2fJit3 ^4b$ •5fl - i - .IPSO/ 10q l \ 1 . 0.5 1 1 1 1 1 1 1 1 1 1 3 0.5 1 1 1 1 0.5 1 1 1 1 1 0 5 1 1 1 1 1 1 1 1 1 1 1 1 7 1 1 1 1 1 1 1 1 1 1 1 1 10 0.5 1 1 1 1 1 1 1 1 1 1 1 13 0.5 1 1 1 1 0.5 0.5 1 1 1 1 1 15 0.5 1 1 1 1 0.5 1 1 1 1 1 1 17 , 0.5 1 0 1 1 0 0 1 1 1 1 0 1 1 1 1 1 0.5 1 0 1 1 1 11 1 1 1 1 1 1 1 1 0 1 1 1 12 0 1 1 1 1 0 0 1 1 1 0 1 14 0.5 1 1 0.5 1 0.5 1 1 1 1 0 1 16 0 0 0 0.5 0 0 0 0 1 1 1 1 18 0 0 1 1 0.5 0.5 0.5 1 0 1 1 0 24 0 1 1 1 0.5 0 0.5 0.5 1 1 1 1 117 K. TEMBS POST-TEST RESUTLS (Grader 2) ,.1q. *q 3q 4a. _ 4b 4c,-*., ,5q ,6q -, -7q 8c L . .9q_ JOg J 1 0.5 1 1 1 1 1 1 1 1 3 0.5 1 ' 1 0 1 0.5 1 0.5 1 1 0 5 1 1 1 1 0.5 1 1 0.5 1 1 1 7 1 1 1 1 1 1 1 1 1 1 1 10 0.5 1 1 1 0.5 1 1 0.5 1 1 1 13 0.5 1 1 1 1 0.5 0.5 1 1 I 1 1 15 0.5 1 1 0.5 0.5 0.5 1 0.5 1 I 1 1 17 0.5 1 0 0 0.5 0 0 0 1 I 1 1 8 0 1 1 1 0.5 1 0.5 0.5 0 I 1 1 11 1 1 1 1 1 1 1 1 0 I 1 1 12 0 1 1 1 1 0 0 0.5 1 I 0 1 14 0.5 1 1 0 0.5 0.5 1 0.5 1 I 0 1 16 0 0 0 0 0 0 0 0 1 1 1 1 18 0 0 1 1 1 0.5 0.5 0.5 0 1 1 0 24 0 1 1 1 0 0 0.5 0.5 1 ! 1 1 118 L. QUESTION - ANSWERS PREDICTION M O D U L E 1. What happens to concentration of NH3 if the number of moles of H2 changes to 20.0 (H2 added to the. system at equilibrium)? Please click the correct curve on the concentration graph, (or answers below) then click 'Predict' A. Logarithmic increase B. No change C. Logarithmic decrease D. Linear increase 2. What happen to the concentration of H2 if the number of moles of N2 changes to 2.0 (N2 decreased to the system at equilibrium)? Please click the correct curve on the concentration graph, (or answers below) then click 'Predict' A . No change B. Linear increase C. Logarithmic decrease D. Logarithmic increase 3. What happens to the concentration of H2 if the number of moles of NH3 changes to 70.0(NH3 added to the system at equilibrium)? Please click the correct curve on the concentration graph, (or answers below) then click 'Predict' A . Linear decrease B. No change C. Logarithmic increase D. Linear increase 4. What happens to the concentration of H2 if the volume of the system changes to 3.0(Pressure decreased when the system is at equilibrium)? Please click the correct curve on the concentration graph, (or answers below) then click 'Predict' A . No change B. Instantaneous decrease C. Logarithmic decrease D. Logarithmic increase 

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