By Evan Dragich (Duke University) et al.
Information
As data science (DS) continues to grow in popularity among university course offerings, it is becoming crucial to successfully measure students' learning outcomes in introductory courses. To do this requires an assessment designed to which could additionally be used to evaluate pedagogical techniques or curriculum interventions in data science curriculum To develop a blueprint for the assessment, a multi-institutional team of statistics and data science education researchers identified common DS content (e.g., data wrangling, interpreting visualizations), drawing from published guidelines/recommendations and introductory DS syllabi. A draft of the assessment was written and used to conduct three think-aloud interviews with field-relevant faculty members.The interviews consisted of both open-ended brainstorming on the assessment's scope as well as individual examinations of each item for relevance, clarity, and efficacy in measuring the desired learning objective. Think-aloud interviews were also conducted with introductory DS students to gauge item clarity and gain insight into the reasoning for their responses. This poster includes the blueprint developed, as well as example items, and results from the faculty and student think aloud interviews. We also present next steps for the project including plans for larger scale piloting and further analyses.
Authors: Evan Dragich (Duke University), Matt Beckman (Penn State University), Mine Çetinkaya-Rundel (Duke University), Mine Dogucu (University College London & UC Irvine), Chelsey Legacy (University of Minnesota), Maria Tackett (Duke University), Andy Zieffler (University of Minnesota)