@inproceedings{SchoppelHaugManzetal.2025, author = {Schoppel, Paul and Haug, Jim and Manz, Julian and Bigler, Dimitri and Hock, Isabell and Abke, J{\"o}rg and Hagel, Georg}, title = {METHODICAL APPROACH FOR ANALYZING LEARNING PATH FITNESS IN AN AI-BASED ADAPTIVE LEARNING SYSTEM}, series = {EDULEARN Proceedings}, volume = {1}, booktitle = {EDULEARN Proceedings}, publisher = {IATED}, isbn = {978-84-09-74218-9}, issn = {2340-1117}, doi = {https://doi.org/10.21125/edulearn.2025.2430}, pages = {9438 -- 9446}, year = {2025}, abstract = {Learning paths are a cornerstone of many adaptive learning systems, particularly those focusing on adaptive navigational techniques. Evaluating and analyzing these paths is therefore crucial to ensure they effectively support both learners and instructors. For eLearning this process must be highly scalable despite minimal oversight and little to no control over learners' behavior. Consequently, learning path evaluation should be automated, user-friendly, and precise. However, current research on this topic often emphasizes simulations, performance metrics, or mathematical models, without fully considering the broader, learner-centered aspects necessary for meaningful adaptation. The authors prior findings also indicate that approaches to assessing the suitability of learning paths must be optimized. To address these gaps, this paper presents a potential methodological approach for comprehensive learning path evaluation, aiming to enhance both the precision of adaptive learning systems and the overall learning experience. Three different algorithms, derived from learning style tendencies and a lecturer recommendation, were analyzed as an illustrative example, although the method itself is not constrained by the form or data basis of these algorithms. The adaptive learning system utilizes various measures to gauge the suitability of a learning path, all gathered through real-time learner feedback. These measures include the correlation between students' preferred path and each algorithm, referred to as it's fitness, the alignment between students' actual adherence to a generated path and their own perception of their study behavior as well as their satisfaction with the path, and the connection between algorithm fitness and both actual performance and perceived performance. To collect data, students were asked to create their own preferred learning paths by digitally arranging the provided learning elements after receiving an introduction to the respective categories. Once they had completed a topic with a generated learning path, they rated their satisfaction with it and indicated whether they had followed its sequence. They also estimated whether their knowledge level had changed. Learning analytics were then employed to compare these self-reports with students' actual study behavior. Performance was measured using a rating system, while Spearman's Rho and Kendall's Tau served as the main correlation metrics for data analysis. The results indicate that all three algorithms produce paths more closely aligned with students' preferred learning paths than the lecturer recommendation, although no single algorithm demonstrated clear dominance. Student satisfaction showed some correlation with the fitness of the generated learning path. Additionally, student ratings appeared to have a slight positive correlation with learning path fitness, whereas self-perceived performance showed no discernible difference. Analysis of the link between actual student behavior and their feedback suggested that students were not reliable in judging whether or not they had followed a learning path. These findings are consistent with the authors earlier work suggesting the potential effectiveness of the learning path algorithms examined, thus supporting this new methodological approach to analyzing learning paths. The study also provided valuable insights for further development; however, its limited sample size remains a challenge for validation.}, subject = {E-Learning}, language = {en} } @inproceedings{FischerHaugSchoppeletal.2025, author = {Fischer, David Vincent and Haug, Jim and Schoppel, Paul and Abke, J{\"o}rg and Becker, Matthias and Hagel, Georg}, title = {Evaluation of a Node-based Automatic Short Answer Tool "NodeGrade"}, series = {Proceedings of the 6th European Conference on Software Engineering Education}, booktitle = {Proceedings of the 6th European Conference on Software Engineering Education}, publisher = {ACM}, address = {New York, NY, USA}, doi = {https://dl.acm.org/doi/10.1145/3723010.3723021}, pages = {20 -- 29}, year = {2025}, abstract = {NodeGrade tries to provide a suitable solution for the problem of time-intensive short answer grading. This research focuses simultaneously on performance, functionality and user experience, which is underlined by a triangulated approach. The evaluation results show comparable performance of NodeGrade on public datasets, even outperforming GPT-4 on the SemEval 2013 Task 7. Matching of NodeGrade's output with multiple human expert raters reveals some weaknesses regarding cases at the lower and upper boundary. In terms of user experience, the interviewed and observed students recognized both positive facets, like better learning support and helpful feedback, and negative sides, including technical limitations and lack of transparency. Overall, NodeGrade promises high potential for further practical use and testing in the field of software engineering education and automatic short answer grading.}, subject = {Software Engineering}, language = {en} } @inproceedings{ManzAbkeHagel2025, author = {Manz, Julian and Abke, J{\"o}rg and Hagel, Georg}, title = {Automated Tracking of User Interactions in Web-Based Adaptive Learning for Software Engineering}, series = {ECSEE '25: Proceedings of the 6th European Conference on Software Engineering Education}, booktitle = {ECSEE '25: Proceedings of the 6th European Conference on Software Engineering Education}, editor = {Mottok, J{\"u}rgen and Hagel, Georg}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, isbn = {979-8-4007-1282-1}, doi = {10.1145/3723010.3723020}, pages = {180 -- 184}, year = {2025}, abstract = {This paper explores the automation of generating and dispatching Experience API (xAPI) statements for comprehensive tracking of user interactions in e-learning environments. It introduces the react-xapi-wrapper library, an extension of the xAPI JavaScript library designed for use in web applications. Key aspects discussed include the library's features, its integration into a web-based adaptive learning system (ALS) for software engineering, and the custom verbs used. The goal is to reduce implementation effort for tutors and developers while taking advantage of xAPI's interoperability, scalability, and ability to track student learning activities and behaviors, laying the foundation for more responsive and personalized learning experiences.}, subject = {Computerunterst{\"u}tztes Lernen}, language = {en} } @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} }