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

Florian Gnadlinger, Florian Gnadlinger, André Selmanagić, Katharina Simbeck, Simone Kriglstein

2023

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.

Paper Citation

in Harvard Style

Gnadlinger F., Selmanagić A., Simbeck K. and Kriglstein S. (2023). Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks. In Proceedings of the 15th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-641-5, SciTePress, pages 272-280. DOI: 10.5220/0011964700003470

in Bibtex Style

@conference{csedu23,
author={Florian Gnadlinger and André Selmanagić and Katharina Simbeck and Simone Kriglstein},
title={Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks},
booktitle={Proceedings of the 15th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2023},
pages={272-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011964700003470},
isbn={978-989-758-641-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks
SN - 978-989-758-641-5
AU - Gnadlinger F.
AU - Selmanagić A.
AU - Simbeck K.
AU - Kriglstein S.
PY - 2023
SP - 272
EP - 280
DO - 10.5220/0011964700003470
PB - SciTePress