Darryl A. Dickerson, Stephanie Masta, Matthew W. Ohland, Alice L. Pawley
{"title":"Is Carla grumpy? Analysis of peer evaluations to explore microaggressions and other marginalizing behaviors in engineering student teams","authors":"Darryl A. Dickerson, Stephanie Masta, Matthew W. Ohland, Alice L. Pawley","doi":"10.1002/jee.20606","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Teamwork has become a central element of engineering education. However, the race- and gender-based marginalization prevalent in society is also prevalent in engineering student teams. These problematic dynamics limit learning opportunities, isolate historically marginalized students, and ultimately push students away from engineering, further reinforcing the demographic imbalances in the profession.</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>While there are strategies to improve the experiences of marginalized students within teams, there are few tools for detecting marginalizing behaviors as they occur. The purpose of this work is to examine how peer evaluations collected as a normal part of an engineering course can be used as a window into team dynamics to reveal marginalization as it occurs.</p>\n </section>\n \n <section>\n \n <h3> Method</h3>\n \n <p>We used a semester of peer evaluation data from a large engineering course in which a team project is the central assignment and peer evaluation occurs four times during the course. We designed an algorithm to identify teams where marginalization may be occurring. We then performed qualitative analyses using a sociolinguistic analysis.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Results show that the algorithm helps identify teams where marginalization occurs. Qualitative analyses of four illustrative cases demonstrated the stealth appearance and evolution of marginalization, providing strong evidence that hidden within language of peer evaluation are indicators of marginalization. Based on the wider dataset, we present a taxonomy (eight categories) of linguistic marginalization appearing in peer comments.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Both peer evaluation scores and the language used in peer evaluations can reveal team inequities and may serve as a near-real-time mechanism to interrupt marginalization within engineering teams.</p>\n </section>\n </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"113 3","pages":"603-634"},"PeriodicalIF":3.9000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20606","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering Education","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jee.20606","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Teamwork has become a central element of engineering education. However, the race- and gender-based marginalization prevalent in society is also prevalent in engineering student teams. These problematic dynamics limit learning opportunities, isolate historically marginalized students, and ultimately push students away from engineering, further reinforcing the demographic imbalances in the profession.
Purpose
While there are strategies to improve the experiences of marginalized students within teams, there are few tools for detecting marginalizing behaviors as they occur. The purpose of this work is to examine how peer evaluations collected as a normal part of an engineering course can be used as a window into team dynamics to reveal marginalization as it occurs.
Method
We used a semester of peer evaluation data from a large engineering course in which a team project is the central assignment and peer evaluation occurs four times during the course. We designed an algorithm to identify teams where marginalization may be occurring. We then performed qualitative analyses using a sociolinguistic analysis.
Results
Results show that the algorithm helps identify teams where marginalization occurs. Qualitative analyses of four illustrative cases demonstrated the stealth appearance and evolution of marginalization, providing strong evidence that hidden within language of peer evaluation are indicators of marginalization. Based on the wider dataset, we present a taxonomy (eight categories) of linguistic marginalization appearing in peer comments.
Conclusion
Both peer evaluation scores and the language used in peer evaluations can reveal team inequities and may serve as a near-real-time mechanism to interrupt marginalization within engineering teams.