Inducing effective pedagogical strategies using learning context features

Authors:
Min Chi;Kurt VanLehn;Diane Litman;Pamela Jordan
Affiliations:
Machine Learning Department, Carnegie Mellon University, PA;School of Computing and Informatics, Arizona State University, AZ;Department of Computer Science, University of Pittsburgh, PA;Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA
Venue:
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Year:
2010

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Abstract

Effective pedagogical strategies are important for e-learning environments While it is assumed that an effective learning environment should craft and adapt its actions to the user's needs, it is often not clear how to do so In this paper, we used a Natural Language Tutoring System named Cordillera and applied Reinforcement Learning (RL) to induce pedagogical strategies directly from pre-existing human user interaction corpora 50 features were explored to model the learning context Of these features, domain-oriented and system performance features were the most influential while user performance and background features were rarely selected The induced pedagogical strategies were then evaluated on real users and results were compared with pre-existing human user interaction corpora Overall, our results show that RL is a feasible approach to induce effective, adaptive pedagogical strategies by using a relatively small training corpus Moreover, we believe that our approach can be used to develop other adaptive and personalized learning environments.