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Doing more with less: Student modeling and performance prediction with reduced content models

Huang, Y and Xu, Y and Brusilovsky, P (2014) Doing more with less: Student modeling and performance prediction with reduced content models. In: UNSPECIFIED.

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Abstract

© Springer International Publishing Switzerland 2014. When modeling student knowledge and predicting student performance, adaptive educational systems frequently rely on content models that connect learning content (i.e., problems) with its underlying domain knowledge (i.e., knowledge components, KCs) required to complete it. In some domains, such as programming, the number of KCs associated with advanced learning contents is quite large. It complicates modeling due to increasing noise and decreases efficiency. We argue that the efficiency of modeling and prediction in such domains could be improved without the loss of quality by reducing problems content models to a subset of most important KCs. To prove this hypothesis, we evaluate several KC reduction methods varying reduction size by assessing the prediction performance of Knowledge Tracing and Performance Factor Analysis. The results show that the predictive performance using reduced content models can be significantly better than using original one, with extra benefits of reducing time and space.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Huang, Yyuh43@pitt.eduYUH43
Xu, Y
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 1 January 2014
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 8538
Page Range: 338 - 349
Event Type: Conference
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISBN: 9783319087856
ISSN: 0302-9743
Date Deposited: 27 Aug 2015 19:02
Last Modified: 01 Nov 2017 12:57
URI: http://d-scholarship.pitt.edu/id/eprint/26057

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