Temporal data mining for educational applications

Carole R. Beal, Paul R. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationPRICAI 2008
Subtitle of host publicationTrends in Artificial Intelligence - 10th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Pages66-77
Number of pages12
DOIs
StatePublished - Dec 1 2008
Event10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008 - Hanoi, Viet Nam
Duration: Dec 15 2008Dec 19 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5351 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008
CountryViet Nam
CityHanoi
Period12/15/0812/19/08

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Beal, C. R., & Cohen, P. R. (2008). Temporal data mining for educational applications. In PRICAI 2008: Trends in Artificial Intelligence - 10th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 66-77). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5351 LNAI). https://doi.org/10.1007/978-3-540-89197-0_10