Detailed Information on Publication Record

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.

Other formats: BibTeX LaTeX RIS

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
Original language	English
Type of outcome	Article in a journal
Field of Study	10201 Computer sciences, information science, bioinformatics
Country of publisher	Germany
Confidentiality degree	is not subject to a state or trade secret
WWW	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
Tags	International impact, Reviewed
Changed by	Changed by: doc. Mgr. Radek Pelánek, Ph.D., učo 4297. Changed: 2/9/2020 08:56.

Abstract
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.

Abstract

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.