Conceptual Issues in Mastery Criteria: Differentiating
Uncertainty and Degrees of Knowledge

D 2018

Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge

PELÁNEK, Radek

Základní údaje

Originální název

Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge

Autoři

PELÁNEK, Radek (203 Česká republika, garant, domácí)

Vydání

New York, Artificial Intelligence in Education, od s. 450-461, 12 s. 2018

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/18:00104015

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-93842-4

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-93843-1_33

Klíčová slova anglicky

mastery learning; student modeling

Štítky

core_A, firank_A

Změněno: 30. 4. 2019 07:33, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Mastery learning is a common personalization strategy in adaptive educational systems. A mastery criterion decides whether a learner should continue practice of a current topic or move to a more advanced topic. This decision is typically done based on comparison with a mastery threshold. We argue that the commonly used mastery criteria combine two different aspects of knowledge estimate in the comparison to this threshold: the degree of achieved knowledge and the uncertainty of the estimate. We propose a novel learner model that provides conceptually clear treatment of these two aspects. The model is a generalization of the commonly used Bayesian knowledge tracing and logistic models and thus also provides insight into the relationship of these two types of learner models. We compare the proposed mastery criterion to commonly used criteria and discuss consequences for practical development of educational systems.

Citovat

PELÁNEK, Radek. Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge. Online. In Carolyn Penstein Rosé, Roberto Martínez Maldonado, Heinz Ulrich Hoppe, Rose Luckin, Manolis Mavrikis, Kaska Porayska-Pomsta, Bruce M. McLaren, Benedict du Boulay. Artificial Intelligence in Education. New York: Springer, 2018, s. 450-461. ISBN 978-3-319-93842-4. Dostupné z: https://dx.doi.org/10.1007/978-3-319-93843-1_33.

@inproceedings{1448696,
   author = {Pelánek, Radek},
   address = {New York},
   booktitle = {Artificial Intelligence in Education},
   doi = {http://dx.doi.org/10.1007/978-3-319-93843-1_33},
   editor = {Carolyn Penstein Rosé, Roberto Martínez Maldonado, Heinz Ulrich Hoppe, Rose Luckin, Manolis Mavrikis, Kaska Porayska-Pomsta, Bruce M. McLaren, Benedict du Boulay},
   keywords = {mastery learning; student modeling},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York},
   isbn = {978-3-319-93842-4},
   pages = {450-461},
   publisher = {Springer},
   title = {Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge},
   year = {2018}
}

TY  - JOUR
ID  - 1448696
AU  - Pelánek, Radek
PY  - 2018
TI  - Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge
PB  - Springer
CY  - New York
SN  - 9783319938424
KW  - mastery learning
KW  - student modeling
N2  - Mastery learning is a common personalization strategy in adaptive educational systems. A mastery criterion decides whether a learner should continue practice of a current topic or move to a more advanced topic. This decision is typically done based on comparison with a mastery threshold. We argue that the commonly used mastery criteria combine two different aspects of knowledge estimate in the comparison to this threshold: the degree of achieved knowledge and the uncertainty of the estimate. We propose a novel learner model that provides conceptually clear treatment of these two aspects. The model is a generalization of the commonly used Bayesian knowledge tracing and logistic models and thus also provides insight into the relationship of these two types of learner models. We compare the proposed mastery criterion to commonly used criteria and discuss consequences for practical development of educational systems.
ER  -

PELÁNEK, Radek. Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge. Online. In Carolyn Penstein Rosé, Roberto Martínez Maldonado, Heinz Ulrich Hoppe, Rose Luckin, Manolis Mavrikis, Kaska Porayska-Pomsta, Bruce M. McLaren, Benedict du Boulay. \textit{Artificial Intelligence in Education}. New York: Springer, 2018, s.~450-461. ISBN~978-3-319-93842-4. Dostupné z: https://dx.doi.org/10.1007/978-3-319-93843-1\_{}33.

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