TY - CONF A1 - Normann, Marc A1 - Haug, Jim A1 - Valencia, Yeimy A1 - Abke, Jörg A1 - Hagel, Georg A2 - Mottock, Jürgen A2 - Hagel, Georg T1 - Adaptive Learning Path Sequencing Based on Learning Styles within N-dimensional Spaces T2 - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education N2 - 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. KW - Learning Path Sequencing KW - Adaptive Learning Path KW - Ant Colony KW - Genetic Algorithm KW - Adaptive Learning Environment KW - Lernstil KW - Lernerfolg Y1 - 2023 UR - https://opus4.kobv.de/opus4-h-ab/frontdoor/index/index/docId/2232 UR - https://dl.acm.org/doi/abs/10.1145/3593663.3593676 VL - 2023 SP - 56 EP - 64 ER -