@inproceedings{614b3ecd90a6459c80b26ce335991ebe,
title = "Student modeling from conventional test data: A bayesian approach without priors",
abstract = "Although conventional tests are often used for determining a student{\textquoteright}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.",
author = "Kurt Vanlehn and Zhendong Niu and Stephanie Siler and Gertner, {Abigail S.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1998. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 4th International Conference on Intelligent Tutoring Systems, ITS 1998 ; Conference date: 16-08-1998 Through 19-08-1998",
year = "1998",
doi = "10.1007/3-540-68716-5_49",
language = "English (US)",
isbn = "3540647708",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "434--443",
editor = "Goettl, {Barry P.} and Shute, {Valerie J.} and Halff, {Henry M.} and Redfield, {Carol L.}",
booktitle = "Intelligent Tutoring Systems - 4th International Conference, ITS 1998, Proceedings",
}