Attracting and retaining diverse individuals is a core goal for reform efforts within in Science, Technology, Engineering, and Mathematics (STEM) gateway courses. Evidence-based instructional strategies in STEM, including those that incorporate scientific practices, have demonstrated promising findings regarding student learning gains. Inspired by previous findings in an introductory course taught through model-based instruction (MBI), my dissertation aims to gain a better understanding of affective mechanisms involved in the way students across all achievement levels are learning in this context. My dissertation measures student’s motivation, and cognitive and emotional engagement in a model-based, introductory biology context. Using validated survey scales contextualized for a model-based context, I generated student motivational profiles at the beginning and end of a semester to examine how profiles remain stable or change over time, and the relationship between motivational profiles and student achievement level. I also developed and applied tools to measure students’ cognitive and emotional engagement during model-based tasks to study engagement in different model contexts and explore the relationship between engagement and student achievement level. My results found that achievement measures (i.e., grades) did not predict levels of motivation and engagement, which may suggest these as potential mechanisms that explain how and why MBI and other practice-based instructional methods are successful. Continued research into understanding mechanisms that may explain performance differences across student achievement levels can advance pedagogical approaches in STEM and promote persistence and diversity among STEM learners.
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