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New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization

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journal contribution
posted on 01.01.2014 by Kenneth R Koedinger, Emma Brunskill, Ryan S.J.d. Baker, Elizabeth Mclaughlin, John Stamper

Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.

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Publisher Statement

This is the accepted version of the article which has been published in final form at http://dx.doi.org/10.1609/aimag.v34i3.2484

Date

01/01/2014

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