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Using Community-Based Problems to Increase Motivation in a Data Science Virtual Internship
- Source :
-
International Educational Data Mining Society . 2022. - Publication Year :
- 2022
-
Abstract
- Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. Will introducing real-life data science problems fill this motivational deficit? To analyze this question, we contrasted learning with generic datasets and artificial problems (Phase 1) with a community-sourced dataset and authentic problems (Phase 2) in the context of an 8-week virtual internship. Retrospective survey questions indicated interns experienced increased motivation in Phase 2. Additionally, analysis of intern discourse using Linguistic Inquiry and Word Count (LIWC) indicated a significant difference in linguistic measures between the two phases. Phase 1 had significantly greater measures of pronouns with a small-medium effect size, 2nd person words with a medium-large effect size, positive emotion with a medium effect size, inter-rogations with a medium-large effect size, question marks with a medium-large effect size, risk with a medium-large effect size, and causal words with a medium effect size. These results in conjunction with a retrospective survey suggest that phase 1 had more questions asked, more causal relationships defined, and included linguistic features of success and failure. Results from Phase 2 indicated that community-sourced data and problems may increase motivation for learning data science. [For the full proceedings, see ED623995.]
Details
- Language :
- English
- Database :
- ERIC
- Journal :
- International Educational Data Mining Society
- Publication Type :
- Conference
- Accession number :
- ED624129
- Document Type :
- Speeches/Meeting Papers<br />Reports - Research