Temporal data mining for educational applications

Carole R. Beal, Paul R Cohen

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

15 Citations (Scopus)

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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages66-77
Number of pages12
Volume5351 LNAI
DOIs
StatePublished - 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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Intelligent Tutoring Systems
Intelligent systems
Data mining
Data Mining
Students
Testing Hypotheses
Distance Metric
Predict
Evaluate
Testing
Education
Learning

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Beal, C. R., & Cohen, P. R. (2008). Temporal data mining for educational applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5351 LNAI, 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

Temporal data mining for educational applications. / Beal, Carole R.; Cohen, Paul R.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5351 LNAI 2008. p. 66-77 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5351 LNAI).

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

Beal, CR & Cohen, PR 2008, Temporal data mining for educational applications. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5351 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5351 LNAI, pp. 66-77, 10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008, Hanoi, Viet Nam, 12/15/08. https://doi.org/10.1007/978-3-540-89197-0_10
Beal CR, Cohen PR. Temporal data mining for educational applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5351 LNAI. 2008. p. 66-77. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-89197-0_10
Beal, Carole R. ; Cohen, Paul R. / Temporal data mining for educational applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5351 LNAI 2008. pp. 66-77 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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