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Adaptive Learning Path Sequencing Based on Learning Styles within N-dimensional Spaces

  • Planning adaptive learning paths for students’ progress throughout a course can be a challenging task, although it can be helpful for their learning progress. Within the HASKI-System, students should be able to get their own, personalized learning paths. In this paper, we present an approach towards the learning path sequencing problem. This idea is based on a novel proposal for arranging learning objects in a multi-dimensional space, bringing the relationship and similarities of these objects into a new relationship. We show, that we can use both, the Ant Colony Optimization Algorithm and the Genetic Algorithm with the idea of the Traveling-Salesman-Problem and get results, that are comparable with a proposed literature-based adaption mechanism. Nevertheless, the learning paths are all personalized based on the Felder & Silverman Learning Style Model and the hyperspace model will allow us later on to include more dimensions for other influencing factors.

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Metadaten
Author:Marc NormannORCiD, Jim HaugORCiD, Yeimy ValenciaORCiD, Jörg AbkeORCiD, Georg HagelORCiD
URL:https://dl.acm.org/doi/abs/10.1145/3593663.3593676
DOI:https://doi.org/https://doi.org/10.1145/3593663.3593676
Parent Title (English):ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education
Editor:Jürgen Mottock, Georg Hagel
Document Type:Conference Proceeding
Language:English
Year of Completion:2023
Date of first Publication:2023/06/19
Release Date:2024/02/19
Tag:Adaptive Learning Environment; Adaptive Learning Path; Ant Colony; Genetic Algorithm; Learning Path Sequencing
GND Keyword:Lernstil; Lernerfolg
Volume:2023
First Page:56
Last Page:64
Urheberrecht:1
Institutes:Fakultät Ingenieurwissenschaften / Labore / Datenverarbeitung
research focus :WEITERE
Reviewed:ja
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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