Using community-based problems to increase motivation in a data science virtual internship
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.
Publication Title
Proceedings of the 15th International Conference on Educational Data Mining Edm 2022
Recommended Citation
Johnson, J., & Olney, A. (2022). Using community-based problems to increase motivation in a data science virtual internship. Proceedings of the 15th International Conference on Educational Data Mining Edm 2022 https://doi.org/10.5281/zenodo.6853167
