ŘIHÁK, Jiří, Radek PELÁNEK and Juraj NIŽNAN. Student Models for Prior Knowledge Estimation. Online. In Proceedings of the 8th International Conference on Educational Data Mining. Madrid: International Educational Data Mining Society, 2015, p. 109-116. ISBN 978-84-606-9425-0.
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Basic information
Original name Student Models for Prior Knowledge Estimation
Authors ŘIHÁK, Jiří (203 Czech Republic, belonging to the institution), Radek PELÁNEK (203 Czech Republic, belonging to the institution) and Juraj NIŽNAN (703 Slovakia, belonging to the institution).
Edition Madrid, Proceedings of the 8th International Conference on Educational Data Mining, p. 109-116, 8 pp. 2015.
Publisher International Educational Data Mining Society
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Spain
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14330/15:00084612
Organization unit Faculty of Informatics
ISBN 978-84-606-9425-0
Keywords in English geography fact; prior student knowledge; adaptive practice; student modeling
Tags firank_B
Changed by Changed by: doc. Mgr. Radek Pelánek, Ph.D., učo 4297. Changed: 31/3/2016 14:57.
Abstract
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 be highly 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.
Links
MUNI/A/1159/2014, interní kód MUName: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IV.
Investor: Masaryk University, Category A
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