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.
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.
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.
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.
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