Data-driven Proficiency Profiling – Proof of Concept

Type: Evidence | Proposition: B: Teaching | Polarity: | Sector: | Country:

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. These methods include data-driven hints and a data-driven mastery learning system (DDML) which calculates student proficiency based on rule scores weighted based on expert input in order to assign problem sets of appropriate difficulty.
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.

Citation: Behrooz Mostafavi and Tiffany Barnes (2016). "Data-driven Proficiency Profiling - Proof of Concept". In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York.