Journal
COGNITIVE SCIENCE
Volume 40, Issue 6, Pages 1290-1332Publisher
WILEY
DOI: 10.1111/cogs.12290
Keywords
Automated teaching; Partially observable Markov decision process; Concept learning
Categories
Funding
- NSF Graduate Fellowship
- Department of Defense NDSEG Fellowship
- National Science Foundation Mathematical Sciences Postdoctoral Fellowship
- NSF [IIS-0845410]
- NSF CAREER award [1149116]
- Division Of Research On Learning
- Direct For Education and Human Resources [1149116] Funding Source: National Science Foundation
- Division Of Research On Learning
- Direct For Education and Human Resources [1551172] Funding Source: National Science Foundation
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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.
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