July 26, 2021
July 26, 2021
July 19, 2022
In response to campus closures due to COVID-19, the learning environment in a foundational engineering course unexpectedly shifted from hands-on, collaborative work to remote delivery, accomplished within a short period of time. Through end-of-semester course surveys, students were asked open-ended questions to get feedback about their experience with the goal of using student feedback for curriculum planning and improvement should there be continued need to facilitate the course remotely in subsequent semesters. However, with 1,170 responses, the volume of data made it challenging to analyze, interpret and use the feedback for decision-making for following semesters. To address this challenge, we utilized Natural Language Processing (NLP) based techniques - algorithmic ways to analyze, interpret, and present words and sentiments from student responses visually, to inform a novice-led analysis to ultimately help with course planning for future semesters.
Keywords: COVID-19, First-Year Engineering, Machine Learning, Sentiment Analysis, Assessment
Bhaduri, S., & Soledad, M., & Roy, T., & Murzi, H., & Knott, T. (2021, July), A Semester Like No Other: Use of Natural Language Processing for Novice-Led Analysis on End-of-Semester Responses on Students’ Experience of Changing Learning Environments Due to COVID-19 Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36609
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