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 - CHAP A1 - Michael, Münch A1 - Valencia Usme, Yeimy Paola A1 - Abke, Jörg ED - Mottok, Jürgen ED - Hagel, Georg T1 - Prototypical Implementation of a Domain Model for an Adaptive Learning System T2 - Proceedings of the 6th European Conference on Software Engineering Education N2 - The goal of this paper is to present the concept and implementation of a Domain Model for an adaptive learning system (ALS) as a defined framework. The ALS is developed in a research setting. In this study, a proposition for a Domain Model based on Experience API (xAPI) protocol within a Learning Record Store (LRS) is outlined. The target is to collect and analyze student data using xAPI statements, such as average working time and learning elements (LEs) interacted with, in order to gain insights into student behavior and learning processes. The aim of this study is to develop and provide a conceptual model using xAPI and LRS in an ALS setting, an architecture that improves the flexibility of developing adaptive learning systems, enabling the creation of personalized learning experiences for a wide range of users and use cases. The combination of xAPI and LRS allows the definition of a precise Domain Model and provides contextualized and structured data. Moreover, xAPI enables the transformation of the inherent complexity of the Domain Model into something understandable and actionable, facilitating deeper analysis of learning experiences. This paper demonstrates how the proposed Domain Model can effectively represent student activities, interactions, and learning outcomes within a defined framework. The expectation is that diverse types of learners, as well as developers and researchers in educational technologies, will benefit from the use of the Domain Model in a learning environment. Here the learner’s preferences are diagnosed, and then user interfaces are customized in an adaptive manner to accommodate the preferences. The main task of the Domain Model is to provide data for the adaptive components of the adaptive learning system. KW - Software and its engineering; • Software creation and management; • Software development techniques; • Software prototyping; KW - Computerunterstütztes Lernen Y1 - 2025 UR - https://dl.acm.org/doi/pdf/10.1145/3723010.3723030 U6 - https://doi.org/https://dl.acm.org/doi/pdf/10.1145/3723010.3723030 VL - 2025 IS - ECSEE '25: Proceedings of the 6th European Conference on Software Engineering Education SP - 190 EP - 194 ER -