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.
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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
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Portugal
Confidentiality degree is not subject to a state or trade secret
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 2184-5026
Doi http://dx.doi.org/10.5220/0011964700003470
Keywords in English Adaptive Learning; Educational Technology; Virtual Learning Environments; Dynamic Bayesian Network; Evidence-Centered Design
Tags coreB, firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 8/4/2024 16:02.
Abstract
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.
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