Using community-based problems to increase motivation in a data science virtual internship

Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. This study 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 2 had significantly greater measures of words per sentence, analytic thinking, clout (i.e. confidence), affect, positive emotion, certainty, drives, and affiliation, and significantly smaller measures of word count, cognitive processes, negative emotion, and emotional tone. These results suggest that community sourced data and problems may increase motivation for learning data science.