Experimental Analysis of Mastery Learning Criteria

D 2017

Experimental Analysis of Mastery Learning Criteria

PELÁNEK, Radek a Jiří ŘIHÁK

Základní údaje

Originální název

Experimental Analysis of Mastery Learning Criteria

Autoři

PELÁNEK, Radek (203 Česká republika, garant, domácí) a Jiří ŘIHÁK (203 Česká republika, domácí)

Vydání

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

Nakladatel

ACM

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/17:00097866

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-4635-1

DOI

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

UT WoS

000850446100021

Klíčová slova anglicky

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

Štítky

core_B, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 25. 10. 2024 16:27, Mgr. Natálie Hílek

Anotace

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.

Návaznosti

MUNI/A/0897/2016, interní kód MU

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

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

Citovat

PELÁNEK, Radek a 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, s. 156-163. ISBN 978-1-4503-4635-1. Dostupné z: 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 a 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, s.~156-163. ISBN~978-1-4503-4635-1. Dostupné z: https://dx.doi.org/10.1145/3079628.3079667.

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