10.1184/R1/6470288.v1
Kenneth R Koedinger
Kenneth R
Koedinger
Emma Brunskill
Emma
Brunskill
Ryan S.J.d. Baker
Ryan S.J.d.
Baker
Elizabeth Mclaughlin
Elizabeth
Mclaughlin
John Stamper
John
Stamper
New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization
Carnegie Mellon University
2014
educational data mining
learning analytics
artificial intelligence in education
machine learning for student modeling
2014-01-01 00:00:00
Journal contribution
https://kilthub.cmu.edu/articles/journal_contribution/New_Potentials_for_Data-Driven_Intelligent_Tutoring_System_Development_and_Optimization/6470288
<p>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.</p>