Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies

  • Authors:

  • Min Chi;Kurt Vanlehn;Diane Litman;Pamela Jordan

  • Affiliations:

  • Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA 15213;School of Computing, Informatics and Decision Science Engineering, Arizona State University, Tempe, USA;Department of Computer Science and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, USA and Learning Research and Development Center, University of Pittsburgh, Pittsburgh, USA 15 ...;Learning Research and Development Center, University of Pittsburgh, Pittsburgh, USA 15260

  • Venue:
  • User Modeling and User-Adapted Interaction
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

For many forms of e-learning environments, the system's behavior can be viewed as a sequential decision process wherein, at each discrete step, the system is responsible for selecting the next action to take. Pedagogical strategies are policies to decide the next system action when there are multiple ones available. In this project we present a Reinforcement Learning (RL) approach for inducing effective pedagogical strategies and empirical evaluations of the induced strategies. This paper addresses the technical challenges in applying RL to Cordillera, a Natural Language Tutoring System teaching students introductory college physics. The algorithm chosen for this project is a model-based RL approach, Policy Iteration, and the training corpus for the RL approach is an exploratory corpus, which was collected by letting the system make random decisions when interacting with real students. Overall, our results show that by using a rather small training corpus, the RL-induced strategies indeed measurably improved the effectiveness of Cordillera in that the RL-induced policies improved students' learning gains significantly.