Visual recipes for slicing and dicing data: teaching data wrangling using subgoal graphics

Funding Number

1946992

Funding Sponsor

Engineering and Physical Sciences Research Council

Second Author's Department

Computer Science & Engineering Department

Fourth Author's Department

Computer Science & Engineering Department

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https://doi.org/10.1145/3488042.3488063

Document Type

Research Article

Publication Title

ACM International Conference Proceeding Series

Publication Date

11-17-2021

doi

10.1145/3488042.3488063

Abstract

The rising demand for data wrangling skills in today's global market poses new challenges for the programming education community. Non-majors often need to learn it quickly alongside their other subjects. Previous research suggests that subgoal labels offer a powerful scaffolding strategy to help novices decompose problems. Because data wrangling is inherently easy to represent graphically, we wonder whether such labels could be augmented with subgoal graphics. To test this idea, we developed an online tutorial that features subgoal graphics in both programmatic and non-programmatic data wrangling exercises. Following an RCT paradigm, a control group is only given subgoal labels, without any graphics. The platform collects learner activity in order to evaluate the pedagogical benefits. Participants were recruited from multiple institutions (N=197, 134). Our results did not show a significant difference in various learner performance metrics, however subjective feedback from our participants suggest that learners perceive the graphics to be very helpful. We discuss possible reasons for the apparent disparity between objective and subjective data.

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