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Effects Of Instructions Based On Cognitive Bridging And Cognitive Conflict On 9th Grade StudentsYaman, Ibrahim 01 January 2013 (has links) (PDF)
The aim of this study is to compare the effects of the instructions based on cognitive bridging and cognitive conflict approaches and gender on 9th grade students&lsquo / conceptual understanding of force and motion, self-efficacy, and epistemological beliefs. The study was conducted with a sample of 206 students in two different schools. The instructional method (traditional, cognitive conflict, and cognitive bridging) and students&lsquo / gender were used as independent variables in a 3x2 factorial design. Within this design, three treatment groups were constructed, one was control group with no researcher intervention and the other two were used as experimental groups. In one of the experimental groups, students received cognitive bridging instruction and students in the other group received cognitive conflict instruction. Pretests and posttests were administered to assess students&lsquo / conceptual understanding of force and motion, self-efficacy, and epistemological beliefs. Multivariate Analysis of Covariance (MANCOVA) was used to explore the main effects of teaching methods and gender, and possible interactions between them. The results showed that teaching method had a significant effect on students&lsquo / conceptual understanding of force and motion in favor of experimental groups. Nevertheless, no significant difference was detected between the effects of cognitive conflict and cognitive bridging. There was no significant difference between male and female students either on the dependent variables of conceptual understandings of force and motion, self-efficacy, and epistemological beliefs. Finally, the present study couldn&lsquo / t capture any significant interaction between teaching method and gender on the combined dependent variables.
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ROLE OF DIFFERENT INSTRUCTIONAL STRATEGIES ON ENGINEERING STUDENTS’ ACADEMIC PERFORMANCE AND MOTIVATIONAL CONSTRUCTSSaira Anwar (9154622) 24 July 2020 (has links)
<p>he use of student-centered instructional strategies is a common practice in engineering classes. However, understanding which instructional strategies have a more profound effect on students’ performance and motivation is fundamental in course design. Such comparisons would allow instructors to design and plan their courses with better learning activities, which could lead to better student engagement and learning. In this three-paper dissertation, I explored the relative effectiveness of two instructional strategies 1) reflective thinking, and 2) teamwork participation by primarily using quantitative methods. Self-regulated learning theory and the Interactive-Constructive-Active-Passive (ICAP) framework guided the selection of these two strategies.</p><p>The first study investigated the relationship of an instructional strategy and a motivational construct through the following research questions: 1) Do students with high academic self-efficacy generate high-quality reflections? 2) To what degree do students’ self-efficacy beliefs and reflection quality scores predict their learning outcomes? Bivariate Pearson product-moment correlation and multiple linear regression were used to analyze the relationships.</p><p>In the second study, I focused on studying the relative effectiveness of two instructional strategies on a motivational construct in a larger engineering class. More specifically, the second study focused on understanding change in students’ participation in two instructional strategies (i.e., reflective thinking and teamwork) and students’ achievement goals. Further, the study investigated the unique contribution of instructional strategies on students’ academic performance and changes in achievement goals. I used stepwise hierarchical regression, simultaneous regression, and repeated measures ANOVA to analyze the data.</p><p></p><p>The third study focused on investigating the role of the same two instructional strategies on students’ academic performance and multiple motivational constructs (i.e., self-efficacy, task value, and engagement). I used structural equation modeling, and repeated measures ANOVA to analyze the data.</p>
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