Predictors of Student Academic Success in an Upper-Level Microelectronic Circuits Course

Jacqueline Rohde; Sai Paresh Karyekar; Liangliang Chen; Yiming Guo; Ying Zhang

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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: Educational Research and Methods Division (ERM) Technical Session 7
Tagged Division: Educational Research and Methods Division (ERM)
Tagged Topic: Diversity
Permanent URL: https://peer.asee.org/47860

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Paper Authors

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Jacqueline Rohde Georgia Institute of Technology

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Jacqueline (Jacki) Rohde is the Assessment Coordinator in the School of Electrical and Computer Engineering at Georgia Tech, where she guides program evaluation and discipline-based education research efforts. She earned her Ph.D. in Engineering Education Research from Purdue University. Her interests focus on sociocultural norms in engineering and the professional development of engineering students.

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Sai Paresh Karyekar Georgia Institute of Technology

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Sai Paresh Karyekar is currently pursuing her Master of Science degree in Electrical and Computer Engineering at Georgia Tech. She graduated with a Bachelor of Technology degree in Electronics and Telecommunication from the University of Mumbai. Her research interests focus on Machine Learning and Computer Vision.

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Liangliang Chen Georgia Institute of Technology

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Liangliang Chen is currently pursuing his Ph.D. degree in the School of Electrical and Computer Engineering at Georgia Tech. He received a B.B.A. degree in business administration, a B.S. degree in automation, and an M.Eng. degree in control engineering from Harbin Institute of Technology. His research interests are machine learning theory and applications.

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Yiming Guo Georgia Institute of Technology

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Yiming Guo is pursuing a Master of Science degree in Electrical Engineering at the Georgia Institute of Technology. He received his Bachelor of Science degree at University of California, Los Angeles. His primary interests involve machine learning and circuit design.

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Ying Zhang Georgia Institute of Technology orcid.org/0000-0001-5246-2141

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Dr. Ying Zhang is a Professor and Senior Associate Chair in the School of Electrical and Computer Engineering at Georgia Tech. She is the director of the Sensors and Intelligent Systems Laboratory at Georgia Tech. Her research interests are centered on systems-level interdisciplinary problems across multiple engineering disciplines, with AI-enabled personalized engineering education being one of her current research focuses.

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Abstract

This research paper describes the development and analysis of a longitudinal dataset of students’ academic performance using regression and machine learning modeling. This study is contextualized within The School of Electrical and Computer Engineering (ECE) at a large, public, research-intensive institution in the Southeast United States. Undergraduate students in this School have a diverse range of backgrounds, experiences, and needs. As such, program leaders must work to (1) provide effective, accurate, and personalized support; and (2) provide information and recommendations for curricular developments and resource management. Both efforts rely on a strong foundation of data to inform decision-making. As such, this paper describes the quantitative portion of a larger mixed-methods project, from which the authors identified initial baseline conditions of students’ academic performance and revealed potential influential factors in difficult ECE courses. This work suggests opportunities for programmatic improvements with the highest potential for success.

Many engineering educational researchers have worked with large-scale datasets of students’ academic records to understand influential factors on students’ performance. This paper contributes to those efforts by taking advantage of the size of the School to create a statistically powerful dataset while still being able to capture the nuances and legacies specific to the department. Further, the use of machine learning modeling can offer novel insights into underlying trends in the data.

At the time of submission, the project has identified a focal course to frame the analysis: [course number]. This course is a 4-credit hour, junior-level course on semiconductor architecture, and it is notorious within the department for its difficulty. This reputation will be investigated further through qualitative focus groups with former students and interviews with instructors. For the quantitative strand that is the focus of this paper, the data revealed notable baseline conditions. 1,225 students have taken the course between Fall 2016 and Spring 2023. From those students, 20.58% earned a final grade of a D, F, or W. Women made up 19.2 % of the course population and performed at the same level as their male peers, with the exception of the Spring and Fall 2022 semesters (after which their average performance recovered to parity). Students from minoritized racial or ethnic backgrounds made up 23.43% of the data. The data revealed that students from minoritized backgrounds had lower performance in Spring semesters compared to Fall semesters, a trend that is not explained by differences in instructors. Finally, students who had transferred into the institution made up 26.86% of the course population on average, although that number has fluctuated over time. Transfer students demonstrated lower average performance compared to students who had directly matriculated. Regression and machine learning modeling will be conducted and presented in the full paper.

We report these trends by student groups to present baseline conditions for this abstract, but we want to be intentional in avoiding any essentialist, deficit-based conclusions. The goal is to engage in deeper analysis to identify influential factors behind these symptoms, including possible insights at the intersection of different identities. This work has implications for developing data-driven insights to help program leadership make decisions about curricular developments and resource management.

Citation
Format

Rohde, J., & Karyekar, S. P., & Chen, L., & Guo, Y., & Zhang, Y. (2024, June), Predictors of Student Academic Success in an Upper-Level Microelectronic Circuits Course Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47860

TY - CPAPER
AB - This research paper describes the development and analysis of a longitudinal dataset of students’ academic performance using regression and machine learning modeling. This study is contextualized within The School of Electrical and Computer Engineering (ECE) at a large, public, research-intensive institution in the Southeast United States. Undergraduate students in this School have a diverse range of backgrounds, experiences, and needs. As such, program leaders must work to (1) provide effective, accurate, and personalized support; and (2) provide information and recommendations for curricular developments and resource management. Both efforts rely on a strong foundation of data to inform decision-making. As such, this paper describes the quantitative portion of a larger mixed-methods project, from which the authors identified initial baseline conditions of students’ academic performance and revealed potential influential factors in difficult ECE courses. This work suggests opportunities for programmatic improvements with the highest potential for success.

Many engineering educational researchers have worked with large-scale datasets of students’ academic records to understand influential factors on students’ performance. This paper contributes to those efforts by taking advantage of the size of the School to create a statistically powerful dataset while still being able to capture the nuances and legacies specific to the department. Further, the use of machine learning modeling can offer novel insights into underlying trends in the data.

At the time of submission, the project has identified a focal course to frame the analysis: [course number]. This course is a 4-credit hour, junior-level course on semiconductor architecture, and it is notorious within the department for its difficulty. This reputation will be investigated further through qualitative focus groups with former students and interviews with instructors. For the quantitative strand that is the focus of this paper, the data revealed notable baseline conditions. 1,225 students have taken the course between Fall 2016 and Spring 2023. From those students, 20.58% earned a final grade of a D, F, or W. Women made up 19.2 % of the course population and performed at the same level as their male peers, with the exception of the Spring and Fall 2022 semesters (after which their average performance recovered to parity). Students from minoritized racial or ethnic backgrounds made up 23.43% of the data. The data revealed that students from minoritized backgrounds had lower performance in Spring semesters compared to Fall semesters, a trend that is not explained by differences in instructors. Finally, students who had transferred into the institution made up 26.86% of the course population on average, although that number has fluctuated over time. Transfer students demonstrated lower average performance compared to students who had directly matriculated. Regression and machine learning modeling will be conducted and presented in the full paper.

We report these trends by student groups to present baseline conditions for this abstract, but we want to be intentional in avoiding any essentialist, deficit-based conclusions. The goal is to engage in deeper analysis to identify influential factors behind these symptoms, including possible insights at the intersection of different identities. This work has implications for developing data-driven insights to help program leadership make decisions about curricular developments and resource management.
AU - Jacqueline Rohde
AU - Sai Paresh Karyekar
AU - Liangliang Chen
AU - Yiming Guo
AU - Ying Zhang
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Predictors of Student Academic Success in an Upper-Level Microelectronic Circuits Course
UR - https://peer.asee.org/47860
ER -