Student modeling from conventional test data: A bayesian approach without priors

Kurt Vanlehn, Zhendong Niu, Stephanie Siler, Abigail S. Gertner

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

49 Scopus citations

Abstract

Although conventional tests are often used for determining a student’s overall competence, they are seldom used for determining a fine-grained model. However, this problem does arise occasionally, such as when a conventional test is used to initialize the student model of an ITS. Existing psychometric techniques for solving this problem are intractable. Straight-forward Bayesian techniques are also inapplicable because they depend too strongly on the priors, which are often not available. Our solution is to base the assessment on the difference between the prior and posterior probabilities. If the test data raise the posterior probability of mastery of a piece of knowledge even slightly above its prior probability, then that is interpreted as evidence that the student has mastered that piece of knowledge. Evaluation of this technique with artificial students indicates that it can deliver highly accurate assessments.

Original languageEnglish (US)
Title of host publicationIntelligent Tutoring Systems - 4th International Conference, ITS 1998, Proceedings
EditorsBarry P. Goettl, Valerie J. Shute, Henry M. Halff, Carol L. Redfield
PublisherSpringer Verlag
Pages434-443
Number of pages10
ISBN (Print)3540647708, 9783540647706
DOIs
StatePublished - 1998
Externally publishedYes
Event4th International Conference on Intelligent Tutoring Systems, ITS 1998 - San Antonio, United States
Duration: Aug 16 1998Aug 19 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1452
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Intelligent Tutoring Systems, ITS 1998
CountryUnited States
CitySan Antonio
Period8/16/988/19/98

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Vanlehn, K., Niu, Z., Siler, S., & Gertner, A. S. (1998). Student modeling from conventional test data: A bayesian approach without priors. In B. P. Goettl, V. J. Shute, H. M. Halff, & C. L. Redfield (Eds.), Intelligent Tutoring Systems - 4th International Conference, ITS 1998, Proceedings (pp. 434-443). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1452). Springer Verlag. https://doi.org/10.1007/3-540-68716-5_49