Investigating Chemical Engineering Students' Learning Experiences and Outcomes in a Gateway Course

Students choose engineering majors in college for multiple personally motivating reasons. However, 40 % of engineering students do not graduate from their initial engineering major, with most attrition occurring in the first two years of the degree program. This trend has personal and societal consequences. Engineering careers can be personally fulfilling and financially rewarding for many individuals. At the same time, engineering jobs provide critical infrastructure, products, and services that society relies on. In addition, these gatekeeping mechanisms exacerbate inequalities and barriers for systemically minoritized individuals (e.g., women, Black, Indigenous, Latina/o/x, students, etc.) and people at the intersection of these identities in STEM disciplines. Higher educational institutions must ensure that students enrolled in STEM degrees are well supported to pursue and achieve their career goals, especially as more students come from increasingly diverse backgrounds and different levels of prior academic preparation. Motivation can help explain why students choose engineering degrees and how they persist, considering the many challenges they face in obtaining their engineering degree. Motivation is essential for learning and predicts academic achievement and engagement. Motivated students learn more, persist longer, use appropriate learning strategies, and are more likely to achieve their learning outcomes. The following chapters investigate how students navigate one institution’s introductory chemical engineering course across multiple semesters.

In the second chapter, I used the Self-Determination Theory (SDT) to study the effects of the rapid transition to online learning during the COVID-19 pandemic on students’ motivation and well-being in this course. Surveys were administered at the beginning and end of the semester to measure motivation using the Basic Psychological Needs Scale (BPNS) and psychological distress using the Depression Anxiety Stress Scale (DASS-21). Motivation (autonomy and competence) decreased during the semester, based on the results from the paired t-tests. In addition, I predicted students’ final course grades using multiple linear regression to examine the effect of motivation on students’ final course grades. Women with positive changes in competence during the semester were predicted to perform worse than their peers, indicating a negative relationship between motivation and performance for women in the sample population. Finally, the path analysis model results showed that higher psychological distress reduced students’ motivation, higher autonomy predicted higher final grades and higher competence predicted lower final grades. Furthermore, the open-ended survey questions asked the research participants to reflect on their learning experiences. These qualitative responses contextualized the findings from the statistical results.

In Chapter 3, I examined students’ self-regulated learning beliefs in an introductory chemical engineering course across three semesters. Self-regulated students steadily monitor and assess their learning and performance to achieve their desired academic outcomes. I conceptualized motivation using the Expectancy-Value-Cost Theory to explain why students engage in academic tasks. Using confirmatory factor analysis (CFA), I examined the relationship between the theoretical constructs. Then, I used hierarchical multiple linear regression to predict students’ final course grades. The surveys were administered at the beginning and end of the semester. Motivation was measured using the Motivated Strategies for Learning (MSLQ), while cost was measured using the Flake et al. (2015) cost survey. All motivation and cost constructs were stable each semester, except for self-efficacy (Fall 2022) and loss of valued alternatives (Fall 2023). Also, gender identity, self-efficacy, and emotional costs were significant predictors of final grades for students in this class.

Then, in Chapter 4, I interviewed five students who had completed the course and conducted a qualitative study of their experiences in this course, informed by the Social Cognitive Career Theory (SCCT). The results demonstrate how positive social interactions increased students’ self-efficacy judgments, while unfavorable interactions isolated students and reduced their self-efficacy beliefs. Second, the students described strategies to achieve their desired academic performance and persist while facing obstacles. These responses provided evidence to support the proposed conceptual framework. Our findings show how social interactions and self-efficacy judgments influence students’ performance and persistence in an introductory engineering course context.

Together, these findings contribute to the literature on motivation theories by elucidating how motivation affects students’ performance in an introductory engineering class. It will also contribute to efforts to implement educational reform to retain and support students in engineering programs. To improve students' outcomes and contribute to the research and efforts to support and retain engineering students in the majors.