Accelerated Apprenticeship - Teaching Data Science Problem Solving Skills at Scale
Chen, L. & A. Dubrawski
It often takes years of hands-on practice to build operational problem solving skills for a data scientist to be sufficiently competent to tackle real world problems. In this research, we explore a new scalable technology-enhanced learning (TEL) platform that enables accelerated apprenticeship process via a repository of caselets - small but focused case studies with scaffolding questions and feedback. We will present some initial findings from a pilot study we ran in spring 2018 in which we collect feedback from students on the utility of this tool, and performance metrics when students interacting with the tool will also be presented. Follow up study is under the way this fall to evaluate the effectiveness of the caselets. Pilot idea of scaling up the authoring process will also be discussed. When fully realized, this framework may be adapted to be used by other data science courses across the campus.
Lujie (Karen) Chen, Information Systems Heinz
Artur Dubrawski, Robotics Institute SCS
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