An evaluation of pedagogical tutorial tactics for a natural language tutoring system: A reinforcement learning approach

Min Chi, Kurt VanLehn, Diane Litman, Pamela Jordan

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

Pedagogical strategies are policies for a tutor to decide the next action when there are multiple actions available. When the content is controlled to be the same across experimental conditions, there has been little evidence that tutorial decisions have an impact on students' learning. In this paper, we applied Reinforcement Learning (RL) to induce two sets of pedagogical policies from pre-existing human interaction data. The NormGain set was derived with the goal of enhancing tutorial decisions that contribute to learning while the InvNormGain set was derived with the goal of enhancing those decisions that contribute less or even nothing to learning. The two sets were then tested with human students. Our results show that when the content was controlled to be the same, different pedagogical policies did make a difference in learning and more specifically, the NormGain students outperformed their peers. Overall our results suggest that content exposure and practice opportunities can help students to learn even when tutors have poor pedagogical tutorial tactics. However, with effective tutorial tactics, students can learn even more.

Original languageEnglish (US)
Pages (from-to)83-113
Number of pages31
JournalInternational Journal of Artificial Intelligence in Education
Volume21
Issue number1-2
DOIs
StatePublished - Dec 1 2011

Keywords

  • Reinforcement learning
  • human learning
  • intelligent tutoring systems
  • pedagogical strategy

ASJC Scopus subject areas

  • Education
  • Computational Theory and Mathematics

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