Exploring the Link between Cognitive Abilities and Data Science Skills using Alternative Raven's Progressive Matrices

Abstract

This study explored the relationship between performance on an alternative Raven's Progressive Matrices (aRPM) test and data science problem solving abilities, hypothesizing a strong link to relational thinking. In the experiment, 31 undergraduates engaged in a 2.5-hour session, including a worked example and four problem solving tasks, followed by data science problems. Our regression analysis confirmed that aRPM scores significantly predict data science problem solving performance, effectively capturing a moderate to strong variance in posttest out-comes. Additionally, aRPM was more predictive of performance than experience in related subjects. An investigation of model fairness indicated that the model may underestimate problem solving performance for male and non-white sub-groups. The findings of this study highlight the potential of using aRPM in traditional or intelligent tutoring systems for data science education to enhance personalization. aRPM can predict initial learning outcomes and identify students who may need additional support. However, further research is necessary to validate aRPM's effectiveness across different demographic groups.

Publication Title

Ceur Workshop Proceedings

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