Shuchi Grover
Marie Bienkowski
Matthias Hauswirth
ABSTRACT
Authentic problem solving tasks in digital environments are often open-ended with ill-defined pathways to a goal state. Scaffolds and formative feedback during this process help learners develop the requisite skills and understanding, but require assessing the problem-solving process. This paper describes a hybrid approach to assessing process at scale in the context of the use of computational thinking practices during programming. Our approach combines hypothesis-driven analysis, using an evidence-centered design framework, with discovery-driven data analytics. We report on work-in-progress involving novices and expert programmers working on Blockly games.
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Cross Ref
- Mislevy, R. J., Behrens, J. T., Dicerbo, K. E., & Levy, R. 2012. Design and discovery in educational assessment: Evidence-centered design, psychometrics, and educational data mining. JEDM, 4(1), 11--48.Google Scholar
- Piech, C., Sahami, M., Huang, J., & Guibas, L. 2015. Autonomously Generating Hints by Inferring Problem Solving Policies. In Proc. of the 2nd ACM Conference on Learning@Scale. 195--204. Google ScholarDigital Library
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Index Terms
- Assessing Problem-Solving Process At Scale
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