Bayesian knowledge tracing, logistic models, and beyond: an
overview of learner modeling techniques

J 2017

Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques

PELÁNEK, Radek

Základní údaje

Originální název

Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques

Autoři

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

Vydání

User Modeling and User-Adapted Interaction, 2017, 0924-1868

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.808

Kód RIV

RIV/00216224:14330/17:00099575

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1007/s11257-017-9193-2

UT WoS

000414997500001

Klíčová slova česky

adaptivní učení; modelování studentů

Klíčová slova anglicky

learner modeling; evaluation; adaptive learning

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 2. 9. 2020 08:56, doc. Mgr. Radek Pelánek, Ph.D.

Anotace

V originále

Learner modeling is a basis of personalized, adaptive learning. The research literature provides a wide range of modeling approaches, but it does not provide guidance for choosing a model suitable for a particular situation. We provide a systematic and up-to-date overview of current approaches to tracing learners' knowledge and skill across interaction with multiple items, focusing in particular on the widely used Bayesian knowledge tracing and logistic models. We discuss factors that influence the choice of a model and highlight the importance of the learner modeling context: models are used for different purposes and deal with different types of learning processes. We also consider methodological issues in the evaluation of learner models and their relation to the modeling context. Overall, the overview provides basic guidelines for both researchers and practitioners and identifies areas that require further clarification in future research.

Citovat

PELÁNEK, Radek. Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques. User Modeling and User-Adapted Interaction. 2017, roč. 27, 3-5, s. 313-350. ISSN 0924-1868. Dostupné z: https://dx.doi.org/10.1007/s11257-017-9193-2.

@article{1404956,
   author = {Pelánek, Radek},
   article_number = {3-5},
   doi = {http://dx.doi.org/10.1007/s11257-017-9193-2},
   keywords = {learner modeling; evaluation; adaptive learning},
   language = {eng},
   issn = {0924-1868},
   journal = {User Modeling and User-Adapted Interaction},
   title = {Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques},
   url = {https://link.springer.com/article/10.1007/s11257-017-9193-2},
   volume = {27},
   year = {2017}
}

TY  - JOUR
ID  - 1404956
AU  - Pelánek, Radek
PY  - 2017
TI  - Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques
JF  - User Modeling and User-Adapted Interaction
VL  - 27
IS  - 3-5
SP  - 313-350
EP  - 313-350
SN  - 09241868
KW  - learner modeling
KW  - evaluation
KW  - adaptive learning
UR  - https://link.springer.com/article/10.1007/s11257-017-9193-2
L2  - https://link.springer.com/article/10.1007/s11257-017-9193-2
N2  - Learner modeling is a basis of personalized, adaptive learning. The research literature provides a wide range of modeling approaches, but it does not provide guidance for choosing a model suitable for a particular situation. We provide a systematic and up-to-date overview of current approaches to tracing learners' knowledge and skill across interaction with multiple items, focusing in particular on the widely used Bayesian knowledge tracing and logistic models. We discuss factors that influence the choice of a model and highlight the importance of the learner modeling context: models are used for different purposes and deal with different types of learning processes. We also consider methodological issues in the evaluation of learner models and their relation to the modeling context. Overall, the overview provides basic guidelines for both researchers and practitioners and identifies areas that require further clarification in future research.
ER  -

PELÁNEK, Radek. Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques. \textit{User Modeling and User-Adapted Interaction}. 2017, roč.~27, 3-5, s.~313-350. ISSN~0924-1868. Dostupné z: https://dx.doi.org/10.1007/s11257-017-9193-2.

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