Experimental Analysis of Mastery Learning Criteria

D 2017

Experimental Analysis of Mastery Learning Criteria

PELÁNEK, Radek and Jiří ŘIHÁK

Basic information

Original name

Experimental Analysis of Mastery Learning Criteria

Authors

PELÁNEK, Radek (203 Czech Republic, guarantor, belonging to the institution) and Jiří ŘIHÁK (203 Czech Republic, belonging to the institution)

Edition

New York, NY, USA, Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, p. 156-163, 8 pp. 2017

Publisher

ACM

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/17:00097866

Organization unit

Faculty of Informatics

ISBN

978-1-4503-4635-1

DOI

http://dx.doi.org/10.1145/3079628.3079667

Keywords in English

mastery learning; learner modeling; Bayesian knowledge tracing; exponential moving average

Abstract

V originále

A common personalization approach in educational systems is mastery learning. A key step in this approach is a criterion that determines whether a learner has achieved mastery. We thoroughly analyze several mastery criteria for the basic case of a single well-specified knowledge component. For the analysis we use experiments with both simulated and real data. The results show that the choice of data sources used for mastery decision and setting of thresholds are more important than the choice of a learner modeling technique. We argue that a simple exponential moving average method is a suitable technique for mastery criterion and propose techniques for the choice of a mastery threshold.

Links

MUNI/A/0897/2016, interní kód MU

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

Investor: Masaryk University, Category A

Citovat

PELÁNEK, Radek and Jiří ŘIHÁK. Experimental Analysis of Mastery Learning Criteria. Online. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. New York, NY, USA: ACM, 2017, p. 156-163. ISBN 978-1-4503-4635-1. Available from: https://dx.doi.org/10.1145/3079628.3079667.

@inproceedings{1392193,
   author = {Pelánek, Radek and Řihák, Jiří},
   address = {New York, NY, USA},
   booktitle = {Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization},
   doi = {http://dx.doi.org/10.1145/3079628.3079667},
   keywords = {mastery learning; learner modeling; Bayesian knowledge tracing; exponential moving average},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York, NY, USA},
   isbn = {978-1-4503-4635-1},
   pages = {156-163},
   publisher = {ACM},
   title = {Experimental Analysis of Mastery Learning Criteria},
   url = {https://dl.acm.org/citation.cfm?id=3079667},
   year = {2017}
}

TY  - JOUR
ID  - 1392193
AU  - Pelánek, Radek - Řihák, Jiří
PY  - 2017
TI  - Experimental Analysis of Mastery Learning Criteria
PB  - ACM
CY  - New York, NY, USA
SN  - 9781450346351
KW  - mastery learning
KW  - learner modeling
KW  - Bayesian knowledge tracing
KW  - exponential moving average
UR  - https://dl.acm.org/citation.cfm?id=3079667
N2  - A common personalization approach in educational systems is mastery learning. A key step in this approach is a criterion that determines whether a learner has achieved mastery. We thoroughly analyze several mastery criteria for the basic case of a single well-specified knowledge component. For the analysis we use experiments with both simulated and real data. The results show that the choice of data sources used for mastery decision and setting of thresholds are more important than the choice of a learner modeling technique. We argue that a simple exponential moving average method is a suitable technique for mastery criterion and propose techniques for the choice of a mastery threshold.
ER  -

PELÁNEK, Radek and Jiří ŘIHÁK. Experimental Analysis of Mastery Learning Criteria. Online. In \textit{Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization}. New York, NY, USA: ACM, 2017, p.~156-163. ISBN~978-1-4503-4635-1. Available from: https://dx.doi.org/10.1145/3079628.3079667.

Detailed Information on Publication Record