Adapting Is Difficult! Introducing a Generic Adaptive Learning
Framework for Learner Modeling and Task Recommendation Based on
Dynamic Bayesian Networks

D 2023

Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks

GNADLINGER, Florian, André SELMANAGIĆ, Katharina SIMBECK and Simone KRIGLSTEIN

Basic information

Original name

Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks

Authors

GNADLINGER, Florian (40 Austria, guarantor, belonging to the institution), André SELMANAGIĆ, Katharina SIMBECK and Simone KRIGLSTEIN (40 Austria, belonging to the institution)

Edition

Prague, Proceedings of the 15th International Conference on Computer Supported Education - Volume 1, p. 272-280, 9 pp. 2023

Publisher

SciTePress

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Portugal

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/23:00130708

Organization unit

Faculty of Informatics

ISBN

978-989-758-641-5

ISSN

DOI

http://dx.doi.org/10.5220/0011964700003470

Keywords in English

Adaptive Learning; Educational Technology; Virtual Learning Environments; Dynamic Bayesian Network; Evidence-Centered Design

Abstract

V originále

The process of learning is a personal experience, strongly influenced by the learning environment. Virtual learning environments (VLEs) provide the potential for adaptive learning, which aims to individualize learning experiences in order to improve learning outcomes. Adaptive learning environments achieve individualization by analyzing the learners and altering the instruction according to their specific needs and goals. Despite ongoing research in adaptive learning, the effort to design, develop and implement such environments remains high. Therefore, we introduce a novel, generalized adaptive learning framework based on the methodological Evidence-Centered Design (ECD) framework. Our framework focuses on the analysis of learners’ competencies and the subsequent recommendation of tasks with an appropriate difficulty level. With this paper and the open-source adaptive learning framework, we contribute to the ongoing discussion about generalized adaptive learning technology.

Citovat

GNADLINGER, Florian, André SELMANAGIĆ, Katharina SIMBECK and Simone KRIGLSTEIN. Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks. Online. In Jelena Jovanovic, Irene-Angelica Chounta, James Uhomoibhi and Bruce McLaren. Proceedings of the 15th International Conference on Computer Supported Education - Volume 1. Prague: SciTePress, 2023, p. 272-280. ISBN 978-989-758-641-5. Available from: https://dx.doi.org/10.5220/0011964700003470.

@inproceedings{2279697,
   author = {Gnadlinger, Florian and Selmanagić, André and Simbeck, Katharina and Kriglstein, Simone},
   address = {Prague},
   booktitle = {Proceedings of the 15th International Conference on Computer Supported Education - Volume 1},
   doi = {http://dx.doi.org/10.5220/0011964700003470},
   editor = {Jelena Jovanovic, Irene-Angelica Chounta, James Uhomoibhi and Bruce McLaren},
   keywords = {Adaptive Learning; Educational Technology; Virtual Learning Environments; Dynamic Bayesian Network; Evidence-Centered Design},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Prague},
   isbn = {978-989-758-641-5},
   pages = {272-280},
   publisher = {SciTePress},
   title = {Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks},
   year = {2023}
}

TY  - JOUR
ID  - 2279697
AU  - Gnadlinger, Florian - Selmanagić, André - Simbeck, Katharina - Kriglstein, Simone
PY  - 2023
TI  - Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks
PB  - SciTePress
CY  - Prague
SN  - 9789897586415
KW  - Adaptive Learning
KW  - Educational Technology
KW  - Virtual Learning Environments
KW  - Dynamic Bayesian Network
KW  - Evidence-Centered Design
N2  - The process of learning is a personal experience, strongly influenced by the learning environment. Virtual learning environments (VLEs) provide the potential for adaptive learning, which aims to individualize learning experiences in order to improve learning outcomes. Adaptive learning environments achieve individualization by analyzing the learners and altering the instruction according to their specific needs and goals. Despite ongoing research in adaptive learning, the effort to design, develop and implement such environments remains high. Therefore, we introduce a novel, generalized adaptive learning framework based on the methodological Evidence-Centered Design (ECD) framework. Our framework focuses on the analysis of learners’ competencies and the subsequent recommendation of tasks with an appropriate difficulty level. With this paper and the open-source adaptive learning framework, we contribute to the ongoing discussion about generalized adaptive learning technology.
ER  -

GNADLINGER, Florian, André SELMANAGI$\backslash$'C, Katharina SIMBECK and Simone KRIGLSTEIN. Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks. Online. In Jelena Jovanovic, Irene-Angelica Chounta, James Uhomoibhi and Bruce McLaren. \textit{Proceedings of the 15th International Conference on Computer Supported Education - Volume 1}. Prague: SciTePress, 2023, p.~272-280. ISBN~978-989-758-641-5. Available from: https://dx.doi.org/10.5220/0011964700003470.

Detailed Information on Publication Record