Title

Integrating Learner Help Requests Using A Pomdp In An Adaptive Training System

Abstract

This paper describes the development and empirical testing of an intelligent tutoring system (ITS) with two emerging methodologies: (1) a partially observable Markov decision process (POMDP) for representing the learner model and (2) inquiry modeling, which informs the learner model with questions learners ask during instruction. POMDPs have been successfully applied to non-ITS domains but, until recently, have seemed intractable for large-scale intelligent tutoring challenges. New, ITS-specific representations leverage common regularities in intelligent tutoring to make a POMDP practical as a learner model. Inquiry modeling is a novel paradigm for informing learner models by observing rich features of learners' help requests such as categorical content, context, and timing. The experiment described in this paper demonstrates that inquiry modeling and planning with POMDPs can yield significant and substantive learning improvements in a realistic, scenario-based training task. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

Publication Date

11-7-2012

Publication Title

Proceedings of the National Conference on Artificial Intelligence

Volume

3

Number of Pages

2287-2292

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84868288561 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84868288561

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