TY - CHAP A1 - Valencia, Yeimy A1 - Normann, Marc A1 - Sapsai, Iryna A1 - Abke, Jörg A1 - Madsen, Anders L. A1 - Weidl, Galia T1 - Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style T2 - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, June 2023 KW - Lernstil KW - Fragebogen Y1 - 2023 U6 - https://doi.org/10.1145/3593663.3593685 SP - 73 EP - 82 ER - TY - CHAP A1 - Normann, Marc A1 - Haug, Jim A1 - Valencia, Yeimy A1 - Abke, Jörg A1 - Hagel, Georg ED - Mottock, Jürgen ED - 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://dl.acm.org/doi/abs/10.1145/3593663.3593676 U6 - https://doi.org/https://doi.org/10.1145/3593663.3593676 VL - 2023 SP - 56 EP - 64 ER - TY - JOUR A1 - Sapsai, Iryna A1 - Valencia, Yeimy A1 - Abke, Jörg T1 - Learning Analytics Dashboard for Educators: Proposed Project to Design with Pedagogical Background JF - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education N2 - In this article, the authors describe a prototype of a Learning Analytics Dashboard (LAD) for educators. It is based on the analysis of pedagogical actions and taking into the process and learning style of students in an online environment based on learning analytics (LA). A description of the Dashboard structure, divided into levels and categories based on available learning analytics, will allow the educator to dive deeper into the online course themselves and explore more. It will also allow them to determine the level of student performance, identify gaps in learning materials, and research student data. The authors have identified further directions for the development of a LAD for a professor, including modeling algorithms for researching student behavior and learning style using Artificial Intelligence and presenting LA in a visualized form. This paper shows the stages of creating a professor's LAD prototype as a functional part of the adaptive learning system in the HASKI-System to analyze visual information obtained from LA and the possibilities to monitor the learning process, learning progress, student activity, and make decisions on careful intervention in the students’ learning process. KW - Lerntechnik KW - Computerunterstütztes Lernen Y1 - 2023 UR - https://dl.acm.org/doi/abs/10.1145/3593663.3593686 U6 - https://doi.org/https://doi.org/10.1145/3593663.3593686 VL - 2023 IS - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education SP - 38 EP - 47 ER -