@inproceedings{NormannHaugValenciaetal.2023, author = {Normann, Marc and Haug, Jim and Valencia, Yeimy and Abke, J{\"o}rg and Hagel, Georg}, title = {Adaptive Learning Path Sequencing Based on Learning Styles within N-dimensional Spaces}, series = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education}, volume = {2023}, booktitle = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education}, editor = {Mottock, J{\"u}rgen and Hagel, Georg}, doi = {https://doi.org/10.1145/3593663.3593676}, pages = {56 -- 64}, year = {2023}, abstract = {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.}, subject = {Lernstil}, language = {en} }