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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00099575" target="_blank" >RIV/00216224:14330/17:00099575 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11257-017-9193-2" target="_blank" >http://dx.doi.org/10.1007/s11257-017-9193-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11257-017-9193-2" target="_blank" >10.1007/s11257-017-9193-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

User Modeling and User-Adapted Interaction

ISSN

0924-1868

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

3-5

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

38

Strana od-do

313-350

Kód UT WoS článku

000414997500001

EID výsledku v databázi Scopus

—