PRICAI 2008: Trends in Artificial Intelligence

Volume 5351 of the series Lecture Notes in Computer Science pp 66-77

Temporal Data Mining for Educational Applications

  • Carole R. BealAffiliated withUniversity of Arizona
  • , Paul R. CohenAffiliated withUniversity of Arizona

* Final gross prices may vary according to local VAT.

Get Access


Intelligent tutoring systems (ITSs) acquire rich data about studentsÖ behavior during learning; data mining techniques can help to describe, interpret and predict student behavior, and to evaluate progress in relation to learning outcomes. This paper surveys a variety of data mining techniques for analyzing how students interact with ITSs, including methods for handling hidden state variables, and for testing hypotheses. To illustrate these methods we draw on data from two ITSs for math instruction. Educational datasets provide new challenges to the data mining community, including inducing action patterns, designing distance metrics, and inferring unobservable states associated with learning.