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 a 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, s. 272-280. ISBN 978-989-758-641-5. Dostupné z: https://dx.doi.org/10.5220/0011964700003470.

Základní údaje

• Originální název: Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks
• Autoři: GNADLINGER, Florian (40 Rakousko, garant, domácí), André SELMANAGIĆ, Katharina SIMBECK a Simone KRIGLSTEIN (40 Rakousko, domácí).
• Vydání: Prague, Proceedings of the 15th International Conference on Computer Supported Education - Volume 1, od s. 272-280, 9 s. 2023.
• Nakladatel: SciTePress

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Stať ve sborníku
• Obor: 10201 Computer sciences, information science, bioinformatics
• Stát vydavatele: Portugalsko
• Utajení: není předmětem státního či obchodního tajemství
• Forma vydání: elektronická verze "online"
• Kód RIV: RIV/00216224:14330/23:00130708
• Organizační jednotka: Fakulta informatiky
• ISBN: 978-989-758-641-5
• ISSN: 2184-5026
• Doi: http://dx.doi.org/10.5220/0011964700003470
• Klíčová slova anglicky: Adaptive Learning; Educational Technology; Virtual Learning Environments; Dynamic Bayesian Network; Evidence-Centered Design
• Štítky: coreB
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.