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Data Science Self-Efficacy Assessment Tools: A Foundational Guide to Evaluating Progress

Safia Malallah, Ejiro Osiobe, Shamir

Kansas State University, Baker University




Data science education research faces a notable gap in assessment methodologies, leading to uncertainty and unexplored avenues for enhancing learning experiences. Practical assessment is crucial for educators to tailor teaching strategies and support student confidence in data science skills. We address this gap by developing a data science self-efficacy survey to empower educators by identifying areas where students lack confidence, enabling the design of targeted plans to bolster data science education. Collaboration among computer science, business, and statistics experts was instrumental in crafting a comprehensive survey that caters to the interdisciplinary nature of data science education. The survey evaluates 13 essential skills and knowledge areas, synthesized from literature reviews and industry demands, to provide a holistic assessment framework for educators in the field. Rigorous reliability and validity tests were conducted to ensure the survey's robustness and efficacy in accurately assessing student proficiency.


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