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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.
Author: | Marc NormannORCiD, Jim HaugORCiD, Yeimy ValenciaORCiD, Jörg AbkeORCiD, Georg HagelORCiD |
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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 |