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Faster Teaching via POMDP Planning.

Anna N Rafferty1, Emma Brunskill2, Thomas L Griffiths3

  • 1Department of Computer Science, Carleton College.

Cognitive Science
|September 25, 2015
PubMed
Summary

This study introduces a decision-theoretic framework for optimal teaching action selection, improving student learning. By modeling teaching as a partially observable Markov decision process, it accelerates concept learning compared to baseline methods.

Keywords:
Automated teachingConcept learningPartially observable Markov decision process

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Area of Science:

  • Artificial Intelligence in Education
  • Cognitive Science
  • Machine Learning

Background:

  • Current research focuses on student modeling but neglects planning teaching actions.
  • The impact of student models on teaching strategies is under-explored.

Purpose of the Study:

  • To develop a decision-theoretic framework for optimal pedagogical action selection.
  • To investigate how different student models influence teaching action planning.

Main Methods:

  • Formulating teaching as a partially observable Markov decision process (POMDP).
  • Developing approximate methods for optimal action selection in large state/action spaces.
  • Conducting simulations and behavioral experiments.

Main Results:

  • The proposed framework enables exploration of how student model assumptions affect teaching actions.
  • Demonstrated accelerated learning in two concept-learning tasks compared to baselines.
  • Validated the effectiveness of POMDP planning for intelligent tutoring systems.

Conclusions:

  • A decision-theoretic approach provides a principled method for optimizing teaching actions.
  • The framework highlights the importance of accurate student modeling for effective instruction.
  • This research advances the design of adaptive and effective AI tutors.