Board 11: Predicting At-Risk Students in a Circuit Analysis Course Using Supervised Machine Learning
James P. Becker, Emily Sior, Jerad Hoy, Indika Kahanda · 2020
Abstract 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.