Asee peer logo

Board 11: Predicting At-Risk Students in a Circuit Analysis Course Using Supervised Machine Learning

Download Paper |


2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

October 19, 2019

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count




Permanent URL

Download Count


Request a correction

Paper Authors


James P. Becker Montana State University

visit author page

James Becker is a professor of electrical and computer engineering at Montana State University. His professional interests include microwave circuits, radio frequency electronics, nanoelectronics, cyberlearning, metacognition, and distance education.

visit author page


Emily Sior Montana State University

visit author page

Emily Sior is a student at Montana State University, graduating in May 2019 with a major in Electrical Engineering and a minor in Computer Engineering. Her interests include software development and database integration.

visit author page

author page

Jerad Hoy Montana State University


Indika Kahanda Montana State University Orcid 16x16

visit author page

Dr. Indika Kahanda is an Assistant Professor in the Gianforte School of Computing at Montana State University. His research interests include Bioinformatics and Biomedical Natural Language Processing. He works on the application of machine learning, data mining and natural language processing techniques for solving problems related to large-scale biological data. His current work focuses on predicting mental illness categories for biomedical literature, protein function prediction and protein-function relation extraction from biomedical literature. He received his Ph.D. in Computer Science from Colorado State University in 2016 in the area of Bioinformatics, a Master of Science in Computer Engineering from Purdue University in 2010, and a Bachelor of Science in Computer Engineering from University of Peradeniya, Sri Lanka in 2007.

visit author page

Download Paper |


Writing exercises may be used in problem-centric STEM-based courses to identify common misconceptions held by the writer as well as to probe their metacognitive processes. As grading of writing samples and providing personalized feedback regarding a student’s writing can be time-intensive, opportunities to automate the process while retaining the integrity of the grading and quality of the feedback are attractive.

This paper describes the motivation and use of a writing-based exercise in a sophomore-level course on electric circuit analysis. The conversion of a paper-based writing exercise to a web-based application is detailed as is its initial use in this new format. The ultimate goal of implementing a web-based approach to administering the writing exercise is to build a fully automated application capable of evaluating student responses and providing feedback to the user in an attempt to enhance their conceptual understanding of challenging material in a manner that acknowledges instructor workload in high-enrollment, resource-constrained courses.

The first element in the planned automated evaluation aspect of the writing application is the identification of students scoring at the lowest end of a holistic scale. This is of significant value as there is evidence that such students are at-risk to fail the electric circuits course as it is currently constructed. Use of a basic natural language processing (NLP) pipeline on a dataset of more than one hundred student responses is described as are the initial results of the at-risk / not at-risk binary classification task.

Becker, J. P., & Sior, E., & Hoy, J., & Kahanda, I. (2019, June), Board 11: Predicting At-Risk Students in a Circuit Analysis Course Using Supervised Machine Learning Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32185

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2019 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015