Data-driven proficiency profiling
Authors:
Open Access
- 25 April 2016
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 324-328
Abstract
Data-driven methods have previously been used in intelligent tutoring systems to improve student learning outcomes and predict student learning methods. We have been incorporating data-driven methods for feedback and problem selection into Deep Thought, a logic tutor where students practice constructing deductive logic proofs. In this latest study we have implemented our data-driven proficiency profiler (DDPP) into Deep Thought as a proof of concept. The DDPP determines student proficiency without expert involvement by comparing relevant student rule scores to previous students who behaved similarly in the tutor and successfully completed it. The results show that the DDPP did improve in performance with additional data and proved to be an effective proof of concept.Keywords
Funding Information
- National Science Foundation (1432156)
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