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dc.contributor.authorHanson, Mitchell K.
dc.contributor.authorMeisner, Paul
dc.contributor.authorPearson, Cole
dc.contributor.authorSeliya, Jim
dc.descriptionColor poster with text, charts, and graphs.en_US
dc.description.abstractInstilling curiosity in students improves their learning process. How can we understand the degree of curiosity among students in a specific course? The Question Formulation Technique (QFT) lends itself toward understanding a topic-specific curious mind. In a senior-level course, we collect data using the QFT model, which is then analyzed using Natural Language Processing (NLP). Thought provoking statements are given to students to analyze, who then respond to them with answers in the form of questions. Python scripts are programmed to analyze the student responses and the WEKA data mining tool is used for feature (words) extraction and classification. We conclude that the features extracted provide excellent insight into “Propensity for Exploration” (PE) as a measure of curiosity in student text data.en_US
dc.description.sponsorshipUniversity of Wisconsin--Eau Claire Office of Research and Sponsored Programsen_US
dc.relation.ispartofseriesUSGZE AS589;
dc.subjectQuestion Formulation Technique (QFT)en_US
dc.subjectCollege studentsen_US
dc.subjectDepartment of Computer Scienceen_US
dc.titleCuriosity Detection in Student Text Data : An Empirical Investigation for a Computer Science Courseen_US

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    Posters of collaborative student/faculty research presented at CERCA

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