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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

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

First-Year Programs Division Poster Session

Tagged Division

First-Year Programs

Page Count

12

DOI

10.18260/1-2--36609

Permanent URL

https://peer.asee.org/36609

Download Count

40

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Paper Authors

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Sreyoshi Bhaduri McGraw Hill

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Dr. Sreyoshi Bhaduri is an Engineering Educator and People Researcher. She currently heads Global People Research and Analytics at McGraw Hill, where she leads research leveraging employee data to generate data-driven insights for decisions impacting organizational Culture and Talent. Her research interests include assessing the impact and effectiveness of inclusion initiatives as well as employing innovative, ethical and inclusive mixed-methods research approaches using AI to uncover insights about the 21st century workforce. Sreyoshi is passionate about improving belonging among women in STEM and Engineering. She was recently elected as Senator at the Society of Women Engineers - a not for profit organization with over 42,000 global members and the world’s largest advocate and catalyst for change for women in engineering and technology. She is also a member of the Society for Industrial and Organizational Psychology. Learn more about her work and get in touch at www.ThatStatsGirl.com.

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Michelle Soledad The Ohio State University Orcid 16x16 orcid.org/0000-0002-2491-6684

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Michelle Soledad is a Lecturer in the Department of Engineering Education at The Ohio State University. She holds degrees in Electrical Engineering (BS, ME) from the Ateneo de Davao University (ADDU) in Davao City, Philippines, and in Engineering Education (PhD) from Virginia Tech. Her research interests include learning experiences in fundamental engineering courses and data-informed reflective practice. Michelle's professional experience includes roles in industry and academia, having worked as a software engineer, project lead and manager before becoming Assistant Professor and Department Chair for Electrical Engineering at the Ateneo de Davao University.

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Tamoghna Roy DeepSig Inc.

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Tamoghna Roy works as a Principal Engineer at DeepSig where he is responsible for creating novel machine learning solutions to classical wireless communication problems, thus enabling the next generation of wireless systems. Tamoghna received his PhD and MS in Electrical Engineering from Virginia Tech in 2017 and 2014 respectively.

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Homero Murzi Virginia Polytechnic Institute and State University Orcid 16x16 orcid.org/0000-0003-3849-2947

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Homero Murzi is an Assistant Professor in the Department of Engineering Education at Virginia Tech with honorary appointments at the University of Queensland (Australia) and University of Los Andes (Venezuela). He holds degrees in Industrial Engineering (BS, MS), Master of Business Administration (MBA) and Engineering Education (PhD). Homero is the leader of the Engineering Competencies, Learning, and Inclusive Practices for Success (ECLIPS) Lab. His research focuses on contemporary and inclusive pedagogical practices, emotions in engineering, competency development, and understanding the experiences of Latinx and Native Americans in engineering from an asset-based perspective. Homero has been recognized as a Diggs Teaching Scholar, a Graduate Academy for Teaching Excellence Fellow, a Global Perspectives Fellow, a Diversity Scholar, a Fulbright Scholar, and was inducted in the Bouchet Honor Society.

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Tamara Knott Virginia Polytechnic Institute and State University

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Tamara Knott is Associate Professor of Engineering Education at Virginia Tech. She primarily teaches Engineering Foundations classes to first year engineering students. Her interests include assessment and pedagogy. Within ASEE, she is a member of the First-year Programs Division, the Women in Engineering Division, the Educational Research and Methods Division, and the Design in Engineering Education Division. She is also a member of the Society of Women Engineers (SWE) and is the Faculty Adviser for SWE at VT.

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Abstract

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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