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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00104015" target="_blank" >RIV/00216224:14330/18:00104015 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-93843-1_33" target="_blank" >http://dx.doi.org/10.1007/978-3-319-93843-1_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-93843-1_33" target="_blank" >10.1007/978-3-319-93843-1_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Artificial Intelligence in Education

ISBN

9783319938424

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

450-461

Název nakladatele

Springer

Místo vydání

New York

Místo konání akce

London, United Kingdom

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—