2024 ASEE Annual Conference & Exposition

One of the mechanical engineering program outcomes is the ability for students to identify, formulate, and solve engineering problems by applying principles of engineering, science, and mathematics. NM_MechE (name blinded) is one of the common core courses that mechanical engineering students take in their second year, which introduces numerical procedures to solve problems that are common to mechanical engineering, and their implementation using MATLAB. One major challenge in this course is that students, especially those without strong programming skills, often view it as a mathematics class, which negatively affects their motivation and performance. The existing literature has extensively verified the anticipating impact of self-efficacy beliefs on students’ academic functioning. Although self-efficacy has been well-understood for other domains, it is not well-understood in the context of numerical methods. Self-efficacy has shown to be a task-specific characteristic and thus implementing active learning in numerical methods class can provide more opportunities for students to find tasks that promote feelings of competence and success, which in return will increase their learning motivation and improve their overall performance in the course.
The purpose of this study is to investigate the effectiveness of active learning methods on students’ self-efficacy, learning motivation, and academic performance in learning numerical methods. Specifically, we ask the following research question: What is the effectiveness of active learning methodologies on the students’ self-efficacy and learning outcomes in an introductory undergraduate numerical methods course? The research will be conducted in large section with 200 students enrolled in the NM_MechE class during the Spring 2024. We will recruit our participants from this section.
We will use a sequential explanatory mixed methods approach to answer our research question. First, we will use a pre- and post-self-efficacy survey to explore the impact of active learning on these two factors. Students’ grades, and pre-post knowledge assessment will be used to investigate the effectiveness of active learning on academic performance. Once these data are analyzed, we will purposively sample select participants for a one-on-one semi-structured interview. These qualitative data will enable us to investigate these phenomena in more depth and understand the nuances associated with students’ self-efficacy beliefs, learning motivation, and performance in an undergraduate numerical methods course.
Findings of this research will help engineering educators design activities that engage students in class, promote their self-efficacy beliefs about numerical methods, and learning motivation, and improve their performance in the course.