Student Models for Prior Knowledge Estimation

D 2015

Student Models for Prior Knowledge Estimation

ŘIHÁK, Jiří, Radek PELÁNEK and Juraj NIŽNAN

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

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Spain

Confidentiality degree

není předmětem státního či obchodního tajemství

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

Abstract

V originále

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 MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IV.

Investor: Masaryk University, Category A

Citovat

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.

@inproceedings{1316789,
   author = {Řihák, Jiří and Pelánek, Radek and Nižnan, Juraj},
   address = {Madrid},
   booktitle = {Proceedings of the 8th International Conference on Educational Data Mining},
   keywords = {geography fact; prior student knowledge; adaptive practice; student modeling},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Madrid},
   isbn = {978-84-606-9425-0},
   pages = {109-116},
   publisher = {International Educational Data Mining Society},
   title = {Student Models for Prior Knowledge Estimation},
   year = {2015}
}

TY  - JOUR
ID  - 1316789
AU  - Řihák, Jiří - Pelánek, Radek - Nižnan, Juraj
PY  - 2015
TI  - Student Models for Prior Knowledge Estimation
PB  - International Educational Data Mining Society
CY  - Madrid
SN  - 9788460694250
KW  - geography fact
KW  - prior student knowledge
KW  - adaptive practice
KW  - student modeling
N2  - 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.
ER  -

ŘIHÁK, Jiří, Radek PELÁNEK and Juraj NIŽNAN. Student Models for Prior Knowledge Estimation. Online. In \textit{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.

Detailed Information on Publication Record