ASEE PEER - Effectiveness of Active Learning Methods on Students’ Self-efficacy, Learning Motivation and Academic Performance in Numerical Methods in Mechanical Engineering
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Effectiveness of Active Learning Methods on Students’ Self-efficacy, Learning Motivation and Academic Performance in Numerical Methods in Mechanical Engineering

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Conference

2024 ASEE Annual Conference & Exposition

Location

Portland, Oregon

Publication Date

June 23, 2024

Start Date

June 23, 2024

End Date

July 12, 2024

Conference Session

MECH - Technical Session 13: Technological Advancements and Applications

Tagged Division

Mechanical Engineering Division (MECH)

Tagged Topic

Diversity

Permanent URL

https://peer.asee.org/47220

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Paper Authors

biography

Golnaz Mirfenderesgi The Ohio State University

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I am an Assistant Professor of Practice in the Department of Mechanical and Aerospace Engineering at The Ohio State University. I have been teaching Numerical Methods and Mechanics classes such as Statics, Mechanics of Materials, and Dynamics since 2018. my research interests lay in the area of engineering education, numerical modeling, optimization algorithms, and machine learning methods with the application in automation design.

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biography

Syedah Zahra Atiq The Ohio State University Orcid 16x16 orcid.org/0000-0002-7905-2553

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Dr. Zahra Atiq is an Assistant Professor of Practice of Computer Science and Engineering at The Ohio State University. At Ohio State University, Zahra is involved in teaching introductory programming courses at the undergraduate level. Her research interests include understanding the non-cognitive factors (e.g., emotions) that affect engineering and computing students’ success, particularly in the context of introductory programming courses. She is also interested in developing strategies and programs to help increase participation of women in computing and engineering. Zahra has a Ph.D. in Engineering Education from Purdue University. For her dissertation research, she was awarded the Bilsland dissertation fellowship by the graduate school of Purdue in 2018. Before starting her doctoral studies, she was an Assistant Professor of Computer Science at Forman Christian College in Lahore – Pakistan, from 2009 to 2014. Zahra also holds a Master’s and Bachelor’s degrees in Computer Science.

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Abstract

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.

Mirfenderesgi, G., & Atiq, S. Z. (2024, June), Effectiveness of Active Learning Methods on Students’ Self-efficacy, Learning Motivation and Academic Performance in Numerical Methods in Mechanical Engineering Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47220

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