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How Do Engineering Students Characterize Their Educational Experience on a Popular Social Media Platform Before and During the Covid-19 Pandemic?

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2022 ASEE Annual Conference & Exposition


Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

Computers in Education 11 - Modulus 3

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Paper Authors


Andrew Gillen Northeastern University Orcid 16x16

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Andrew L. Gillen is an Assistant Teaching Professor in First Year Engineering at Northeastern University. Previously, he was a Lecturer in the Department of Civil, Environmental, and Geomatic Engineering at University College London. He holds a Ph.D. in Engineering Education from Virginia Tech and B.S. in Civil Engineering from Northeastern University.

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Ruiqiu Yao University College London


Zheng Chen University of Hong Kong

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Zheng Chen(Bella) is a bachelor at the University of Hong Kong, majoring in Decision Analytics. She is interested in natural language processing, machine learning, smart city, and computer visions.

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Public social media platforms can supplement our understanding of student perceptions of engineering teaching. Looking to social media can help us build a picture of what steps we can take to improve the learning experience. It has the potential to provide meaningful information without requiring more data collection from students. This is particularly salient in times of crisis when contact with students may be inconsistent and when data such as survey results may be more challenging to obtain.

In this study, we analyzed social media data from Reddit towards developing an understanding of engineering students’ attitudes and focus areas around their educational experience before and during the Covid-19 pandemic. Students’ attitudes were mainly evaluated by sentiment analysis and students’ focuses were explored through topic modeling techniques. Both sentiment analysis and topic modeling are a form of natural language processing. Sentiment analysis is a tool to study the feelings expressed in text while topic modeling allows us to look for groups of related phrasings and to obtain a sense of the topics being discussed. Both are readily available through open-source Python packages. Based on the sentiment analysis, findings were categorized as positive, negative, and neutral. Within these three areas, we used topic modeling to categorize and explore the different emphasis areas brought up by students (e.g., extracurricular activities, school assignments). We present the results of the modeling using a topic visualization and interpretation tool. Although this work illustrates computational methods for analyzing social media data, these tools are seen pragmatically as a means to an end and not the sole purpose of our inquiry.

Social media analyses have limitations and ethical considerations, and this work is not meant to supersede other forms of evaluation. Rather, our study explores the use of social media as a potential complementary source of data for practitioners. Our work has implications for educators and institutions looking to develop low-impact ways to evaluate educational programming in times of crisis and beyond. We hope that by presenting this work to other researchers and practitioners in engineering education, we will engage in mutually beneficial conversations around the pros and cons of using social media data and its potential applications.

Gillen, A., & Yao, R., & Chen, Z. (2022, August), How Do Engineering Students Characterize Their Educational Experience on a Popular Social Media Platform Before and During the Covid-19 Pandemic? Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN.

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