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

Basic information

Original name

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

Authors

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

Edition

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

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 2.808

RIV identification code

RIV/00216224:14330/17:00099575

Organization unit

Faculty of Informatics

DOI

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

UT WoS

000414997500001

Keywords (in Czech)

adaptivní učení; modelování studentů

Keywords in English

learner modeling; evaluation; adaptive learning

Abstract

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, vol. 27, 3-5, p. 313-350. ISSN 0924-1868. Available from: 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}
}

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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, vol.~27, 3-5, p.~313-350. ISSN~0924-1868. Available from: https://dx.doi.org/10.1007/s11257-017-9193-2.

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