Inferring learners' knowledge from their actions

Anna N. Rafferty, Michelle M. Lamar, Thomas L. Griffiths

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we develop a general framework for automatically making such inferences based on observed actions; this framework is particularly relevant for inferring student knowledge in educational games and other interactive virtual environments. Our approach relies on modeling action planning: We formalize the problem as a Markov decision process in which one must choose what actions to take to complete a goal, where choices will be dependent on one's beliefs about how actions affect the environment. We use a variation of inverse reinforcement learning to infer these beliefs. Through two lab experiments, we show that this model can recover people's beliefs in a simple environment, with accuracy comparable to that of human observers. We then demonstrate that the model can be used to provide real-time feedback and to model data from an existing educational game.

Original languageEnglish (US)
Pages (from-to)584-618
Number of pages35
JournalCognitive Science
Volume39
Issue number3
DOIs
StatePublished - Apr 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Action understanding
  • Bayesian modeling
  • Inverse reinforcement learning
  • Knowledge diagnosis

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