ROLE OF DIFFERENT INSTRUCTIONAL STRATEGIES ON ENGINEERING STUDENTS’ ACADEMIC PERFORMANCE AND MOTIVATIONAL CONSTRUCTS

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.

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.

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.

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.