Using a Text Mining Assignment as an Intervention to Promote Student Engagement With DEI Issues
Using a Text Mining Assignment as an Intervention to Promote Student Engagement With DEI Issues
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使用文本挖掘作业作为干预措施来促进学生对 DEI 问题的参与
DOI:
10.1145/3408877.3432557
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发表时间:
2021
期刊:
影响因子:
--
通讯作者:
Paguyo, Christina H.
中科院分区:
文献类型:
--
作者:
Leutenegger, Scott T.;Paguyo, Christina H.
The goal of the Partnership For Equity project is to build inclusive computing and engineering professional mindsets, which describes attitudes and identities of students who value knowledge in both technical and diversity, equity, and inclusion (DEI) areas of computer science. In this paper we present results from an intervention we piloted in a text mining special topics class. This intervention is directly applicable to any data mining class. Students applied naive Bayes classification to a survey result dataset and classified responses as "technical" or "equity", where technical meant the survey question response was focused on technical issues, whereas "equity" meant the response was focused on DEI issues. The survey data came from another course where students watched Ms. Joy Buolamwini's 2016 "How I'm fighting bias in algorithms" TedX talk and then answered several survey questions about the talk. In our text mining course students were first asked to watch the same TedX talk and answer several of the same survey questions. Their answers were added to the original data set. Students were then asked to apply naive Bayes classification to the combined survey results for one question. At the end of the course students took an end of class survey and answered more open-ended questions about whether the assignment influenced their thinking about DEI in computing. Results from this intervention indicate that including a DEI focus in technical programming assignments can positively impact students' views on the importance of DEI and contribute to the development of computing and engineering professional mindsets.
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