Student Models for Prior Knowledge Estimation

D 2015

Student Models for Prior Knowledge Estimation

ŘIHÁK, Jiří, Radek PELÁNEK a Juraj NIŽNAN

Základní údaje

Originální název

Student Models for Prior Knowledge Estimation

Autoři

ŘIHÁK, Jiří (203 Česká republika, domácí), Radek PELÁNEK (203 Česká republika, domácí) a Juraj NIŽNAN (703 Slovensko, domácí)

Vydání

Madrid, Proceedings of the 8th International Conference on Educational Data Mining, od s. 109-116, 8 s. 2015

Nakladatel

International Educational Data Mining Society

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Španělsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/15:00084612

Organizační jednotka

Fakulta informatiky

ISBN

978-84-606-9425-0

Klíčová slova anglicky

geography fact; prior student knowledge; adaptive practice; student modeling

Štítky

core_B, firank_B

Změněno: 31. 3. 2016 14:57, doc. Mgr. Radek Pelánek, Ph.D.

Anotace

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.

Návaznosti

MUNI/A/1159/2014, interní kód MU

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

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IV., DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

ŘIHÁK, Jiří, Radek PELÁNEK a 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, s. 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 a 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, s.~109-116. ISBN~978-84-606-9425-0.

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