Faster Teaching via POMDP Planning

Anna N. Rafferty, Emma Brunskill, Thomas L. Griffiths, Patrick Shafto

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


Human and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model impacts the resulting plans. We frame the problem of optimally selecting teaching actions using a decision-theoretic approach and show how to formulate teaching as a partially observable Markov decision process planning problem. This framework makes it possible to explore how different assumptions about student learning and behavior should affect the selection of teaching actions. We consider how to apply this framework to concept learning problems, and we present approximate methods for finding optimal teaching actions, given the large state and action spaces that arise in teaching. Through simulations and behavioral experiments, we explore the consequences of choosing teacher actions under different assumed student models. In two concept-learning tasks, we show that this technique can accelerate learning relative to baseline performance.

Original languageEnglish (US)
Pages (from-to)1290-1332
Number of pages43
JournalCognitive science
Issue number6
StatePublished - Aug 1 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Artificial Intelligence
  • Cognitive Neuroscience


  • Automated teaching
  • Concept learning
  • Partially observable Markov decision process


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