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

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, p. 450-461. ISBN 978-3-319-93842-4. Available from: https://dx.doi.org/10.1007/978-3-319-93843-1_33.

Basic information

Original name

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

Authors

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

Edition

New York, Artificial Intelligence in Education, p. 450-461, 12 pp. 2018

Publisher

Springer

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Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/18:00104015

Organization unit

Faculty of Informatics

ISBN

978-3-319-93842-4

ISSN

DOI

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

UT WoS

000877321800033

Keywords in English

mastery learning; student modeling

Tags

core_A, firank_A

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Abstract

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.