New Potentials for Data-Driven Development and Optimization

  • Koedinger K
  • Brunskill E
  • Baker R
 et al. 
  • 57

    Readers

    Mendeley users who have this article in their library.
  • N/A

    Citations

    Citations of this article.

Abstract

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.

Author-supplied keywords

  • artificial intelligence in education
  • educational data mining
  • learning analytics
  • machine learning for student modeling

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Authors

  • Kenneth R Koedinger

  • Emma Brunskill

  • Ryan S J Baker

  • Elizabeth a Mclaughlin

  • John Stamper

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free