Student Models for Prior Knowledge Estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F15%3A00084612" target="_blank" >RIV/00216224:14330/15:00084612 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Student Models for Prior Knowledge Estimation
Original language description
Intelligent behavior of adaptive educational systems is based on student models. Most research in student modeling focuses on student learning (acquisition of skills). We ocus on prior knowledge, which gets much less attention in modeling and yet can behighly varied and have important consequences for the use of educational systems. We describe several models for prior knowledge estimation ? the Elo rating system, its Bayesian extension, a hierarchical model, and a networked model (multivariate Elo). We evaluate their performance on data from application for learning geography, which is a typical case with highly varied prior knowledge. The result show that the basic Elo rating system provides good prediction accuracy. More complex models do improve predictions, but only slightly and their main purpose is in additional information about students and a domain.
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 8th International Conference on Educational Data Mining
ISBN
9788460694250
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
109-116
Publisher name
International Educational Data Mining Society
Place of publication
Madrid
Event location
Madrid
Event date
Jan 1, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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