TY - CHAP A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Nadimpalli, Vamsi Krishna A1 - Grabinger, Lisa A1 - Staufer, Susanne A1 - Mottok, Jürgen T1 - Towards Eye Tracking based Learning Style Identification T2 - Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023 N2 - The dropout rate at universities has been very high for years. Thereby, the inexperience and lack of knowledge of students in dealing with individual learning paths in various courses of study plays a decisive role. Adaptive learning management systems are suitable countermeasures, in which learners’ learning styles are classified using questionnaires or computationally intensive algorithms before a learning path is suggested accordingly. In this paper, a study design for student learning style classification using eye tracking is presented. Furthermore, qualitative and quantitative analyses clarify certain relationships between students’ eye movements and learning styles. With the help of classification based on eye tracking, the filling out of questionnaires or the integration of computationally or cost-intensive algorithms can be made redundant in the future. Y1 - 2023 SN - 978-1-4503-9956-2 U6 - https://doi.org/10.1145/3593663.3593680 SP - 138 EP - 147 PB - ACM ER - TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Staufer, Susanne A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Towards personalized learning paths in adaptive learning management systems: bayesian modelling of psychological theories T2 - Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023 N2 - In educational research, non-personalized learning content increases learners' cognitive load, causing them to lower their performance and sometimes drop out of the course. Personalizing learning content with learners’ unique characteristics, like learning styles, personality traits, and learning strategies, is being suggested to improve learners’ success. Several theories exist for assessing learners’ unique characteristics. By the end of 2020, 71 learning style theories have been formulated, and research has shown that combining multiple learning style theories to recommend learning paths yields better results. As of the end of 2022, there is no single research that demonstrates a relationship between the Index of Learning Styles (ILS) based Felder-Silverman learning style model (FSLSM) dimensions, Big Five (BFI-10) based personality traits, and the Learning strategies in studying (LIST-K) based learning strategies factors for personalizing learning content. In this paper, an innovative approach is proposed to estimate the relationship between these theories and map the corresponding learning elements to create personalized learning paths. Respective questionnaires were distributed to 297 higher education students for data collection. A three-step approach was formulated to estimate the relationship between the models. First, a literature search was conducted to find existing studies. Then, an expert interview was carried out with a group of one software engineering education research professor, three doctoral students, and two master’s students. Finally, the correlations between the students' questionnaire responses were calculated. To achieve this, a Bayesian Network was built with expert knowledge from the three-step approach, and the weights were learned from collected data. The probability of individual FSLSM learning style dimensions was estimated for a new test sample. Based on the literature, the learning elements were mapped to the respective FSLSM learning style dimensions and were initiated as learning paths to the learners. The next steps are proposed to extend this framework and dynamically recommend learning paths in real time. In addition, the individual levels of learning style dimensions, personality traits, and learning strategies can be considered to improve the recommendations. Further, using probabilities for mapping learning elements to learning styles can increase the chance of initiating multiple learning paths for an individual learner. Y1 - 2023 U6 - https://doi.org/10.21125/iceri.2023.1144 SP - 4593 EP - 4603 PB - IATED ER - TY - CHAP A1 - Ezer, Timur A1 - Plößl, Moritz A1 - Grabinger, Lisa A1 - Bittner, Dominik A1 - Staufer, Susanne A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Hauser, Florian A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Deep learning for eye movement classification T2 - ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain Y1 - 2024 U6 - https://doi.org/10.21125/iceri.2024.1028 SP - 4056 EP - 4065 PB - IATED ER - TY - JOUR A1 - Broser, Christian A1 - Falter, Thomas A1 - Ławrowski, Robert Damian A1 - Altenbuchner, Amelie A1 - Vögele, Daniel A1 - Koss, Claus A1 - Schlamp, Matthias A1 - Dunnweber, Jan A1 - Steffens, Oliver A1 - Heckner, Markus A1 - Jaritz, Sabine A1 - Schiegl, Thomas A1 - Corsten, Sabine A1 - Lauer, Norina A1 - Guertler, Katherine A1 - Koenig, Eric A1 - Haug, Sonja A1 - Huber, Dominik A1 - Birkenmaier, Clemens A1 - Krenkel, Lars A1 - Wagner, Thomas A1 - Justus, Xenia A1 - Saßmannshausen, Sean Patrick A1 - Kleine, Nadine A1 - Weber, Karsten A1 - Braun, Carina N. A1 - Giacoppo, Giuliano A1 - Heinrich, Michael A1 - Just, Tobias A1 - Schreck, Thomas A1 - Schnabl, Andreas A1 - Gilmore, Amador Téran A1 - Roeslin, Samuel A1 - Schmid, Sandra A1 - Wellnitz, Felix A1 - Malz, Sebastian A1 - Maurial, Andreas A1 - Hauser, Florian A1 - Mottok, Jürgen A1 - Klettke, Meike A1 - Scherzinger, Stefanie A1 - Störl, Uta A1 - Heckner, Markus A1 - Bazo, Alexander A1 - Wolff, Christian A1 - Kopper, Andreas A1 - Westner, Markus A1 - Pongratz, Christian A1 - Ehrlich, Ingo A1 - Briem, Ulrich A1 - Hederer, Sebastian A1 - Wagner, Marcus A1 - Schillinger, Moritz A1 - Görlach, Julien A1 - Hierl, Stefan A1 - Siegl, Marco A1 - Langer, Christoph A1 - Hausladen, Matthias A1 - Schreiner, Rupert A1 - Haslbeck, Matthias A1 - Kreuzer, Reinhard A1 - Brückl, Oliver A1 - Dawoud, Belal A1 - Rabl, Hans-Peter A1 - Gamisch, Bernd A1 - Schmidt, Ottfried A1 - Heberl, Michael A1 - Gänsbauer, Bianca A1 - Bick, Werner A1 - Ellermeier, Andreas A1 - Monkman, Gareth J. A1 - Prem, Nina A1 - Sindersberger, Dirk A1 - Tschurtschenthaler, Karl A1 - Aurbach, Maximilian A1 - Dendorfer, Sebastian A1 - Betz, Michael A. A1 - Szecsey, Tamara A1 - Mauerer, Wolfgang A1 - Murr, Florian ED - Baier, Wolfgang T1 - Forschung 2018 T3 - Forschungsberichte der OTH Regensburg - 2018 KW - Forschung KW - Forschungsbericht Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-13826 SN - 978-3-9818209-5-9 CY - Regensburg ER - TY - INPR A1 - Gaube, Susanne A1 - Jussupow, Ekaterina A1 - Kokje, Eesha A1 - Khan, Jowaria A1 - Bondi-Kelly, Elizabeth A1 - Schicho, Andreas A1 - Kitamura, Felipe Campos A1 - Koch, Timo Kevin A1 - Ezer, Timur A1 - Mottok, Jürgen A1 - Lermer, Eva A1 - Ghassemi, Marzyeh A1 - Colak, Errol T1 - Underreliance Harms Human-AI Collaboration More Than Overreliance in Medical Imaging N2 - Importance: The use of artificial intelligence (AI) to support clinicians in diagnostic decision-making holds significant potential; however, evidence regarding its clinical utility remains mixed. In many cases, the interaction between healthcare professionals and AI systems does not improve collaborative performance compared to the standalone performance of humans or AI. Currently, the underlying mechanisms that limit human-AI collaboration are poorly understood. Objective: To examine the impact of AI advice on diagnostic decision-making among experts and novices, focusing on understanding the role of explainability (XAI) on users’ reliance on advice. Design, Setting, and Participants: A mixed-methods design combining a crossover experimental design with a think-aloud and an eye-tracking study arm was conducted in 2023. Participants were task experts (radiologists) and novices (non-radiologist physicians and medical trainees) from 10 countries, with the think-aloud and eye-tracking conducted in Germany. Intervention: Participants reviewed 50 patient cases containing head CT scans and patient information. Every case was reviewed in three time-separate sessions in randomized order. In each session, participants were exposed to a different experimental condition: (a) control, i.e., no AI prediction presented; (b) basic advice, i.e., AI prediction without annotations; and (c) XAI advice, i.e., AI prediction with annotations. For each case, participants had to determine if the patients had an intracranial hemorrhage (ICH), rate their confidence, and, if applicable, the usefulness of the AI advice. Main Outcome(s) and Measure(s): Diagnostic performance, confidence in the diagnosis, case reading time, and AI advice usefulness ratings. Results: The data analysis included 125 participants. The mean age was 28.5 years (SD = 6.72), and 55.2% identified as female. Underreliance on correct AI advice was associated with high uncertainty and had a more detrimental impact on diagnostic performance than overreliance on incorrect advice. XAI advice reduced underreliance and improved performance and confidence, particularly when reviewing more difficult cases with ICH. AI advice, particularly XAI, did not reduce reading time. XAI was perceived as more useful than basic AI advice, especially among novices. Conclusions and Relevance: Our findings indicate that underreliance on AI might be more harmful than overreliance, highlighting the need to develop efficient counterstrategies beyond current XAI methods. Y1 - 2024 U6 - https://doi.org/10.31219/osf.io/4wv8j PB - OSFPreprints ER - TY - CHAP A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Engl, Fabian A1 - Mottok, Jürgen ED - Mottok, Jürgen ED - Hagel, Georg T1 - Eye Movement Modelling Examples on Usability Heuristics T2 - Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025 N2 - The user interface (UI) and user experience (UX) design is of crucial importance for human-computer interaction (HCI), particularly in the context of web applications. In light of the high expectations of users and the competitive nature of the market, it is imperative to employ usability measurement techniques to avoid losing users. Heuristic evaluation (HE) is a cost- and resource-efficient method for evaluating the usability of websites in which evaluators are guided by heuristics. However, the level of expertise of the evaluators has a significant impact on the results, with experts identifying up to 50% more usability issues than novices. To address this gap, this paper proposes Eye Movement Modeling Examples (EMMEs) to demonstrate Jakob Nielsen’s ten usability heuristics in an easy-to-understand format for all levels of experience while also incorporating expert knowledge. In particular, the eye movements and verbal feedback of a usability expert are recorded as the expert analyses the usability of a simple website application in terms of Jakob Nielsen’s ten usability heuristics. This reveals the strategies and cognitive processes of the expert when assessing the usability of a website and makes them more tangible for non-experts or novices. The findings of a questionnaire-based assessment indicate that EMMEs are perceived as beneficial and supportive during the learning process. Ultimately, this comprehensive analysis not only enables a deeper understanding of heuristics for usability novices, but could also lead to EMMEs being applied more efficiently in diverse domains. Y1 - 2025 SN - 9798400712821 U6 - https://doi.org/10.1145/3723010.3723035 SP - 106 EP - 114 PB - ACM ER - TY - JOUR A1 - Rück, Thomas A1 - Bierl, Rudolf A1 - Lechner, Alfred A1 - Graf, Antonia A1 - Dams, Florian A1 - Schreiner, Rupert A1 - Auchter, Eberhard A1 - Kriz, Willy A1 - Deubzer, MIchael A1 - Schiller, Frank A1 - Mottok, Jürgen A1 - Niemetz, Michael A1 - Margull, Ulrich A1 - Hagel, Georg A1 - Utesch, Matthias A1 - Waldherr, Franz A1 - Böhm, Matthias A1 - Fraunhoffer, Judith A1 - Gardeia, Armin A1 - Schneider, Ralph A1 - Streubel, Janet A1 - Landes, Dieter A1 - Studt, Reimer A1 - Peuker, Dominik A1 - Scharfenberg, Georg A1 - Hook, Christian A1 - Schuster, Dietwald A1 - Ehrlich, Ingo A1 - Dinnebier, Heinrich A1 - Briem, Ulrich A1 - Lämmlein, Stephan A1 - Koder, Alexander A1 - Bialek, Adam A1 - Genewsky, Axel A1 - Neumeier, Michael A1 - Schlosser, Philipp A1 - Rabl, Hans-Peter A1 - Paule, Matthias A1 - Galster, Christoph A1 - Schiedermeier, Michael A1 - Zwickel, Andreas A1 - Hobmeier, Christoph A1 - Bischoff, Tobias A1 - Rill, Georg A1 - Schaeffer, Thomas A1 - Arbesmeier, Martin A1 - Groß, Andreas A1 - Schlegl, Thomas A1 - Becker, Mark A1 - Senn, Konrad A1 - Schliekmann, Claus A1 - Scholz, Peter A1 - Sippl, Christian A1 - Grill, Martin ED - Eckstein, Josef T1 - Forschungsbericht 2011 / Hochschule für Angewandte Wissenschaften - Fachhochschule Regensburg T3 - Forschungsberichte der OTH Regensburg - 2011 Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-7321 SN - 1868-3533 CY - Regensburg ER - TY - CHAP A1 - Hauser, Florian A1 - Staufer, Susanne A1 - Grabinger, Lisa A1 - Röhrl, Simon A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - On the analysis of student learning strategies: using the LIST-K questionnaire ro generate ai-based individualized learning paths T2 - Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023 N2 - This paper presents the results of a data collection with the LIST-K questionnaire. This questionnaire measures students’ learning strategies and shows which strategies are particularly dominant or rather weak. Learning strategies have long been a major area of research in educational science and psychology. In these disciplines, learning strategies are understood as intentional behaviors and cognitive skills that learners employ to effectively complete learning tasks, by selecting, acquiring, organizing, and integrating information into their existing knowledge for long-term retention. The LIST-K, developed by Klingsieck in 2018, was chosen for accessing learning strategies due to its thematic suitability, widespread use, and test economy. It covers a total of four main categories (i.e., cognitive strategies, metacognitive strategies, management of internal resources, and management of external resources), each of which are subdivided into further subscales. With a total of 39 items answered via a 5-step Likert scale, the LIST-K can cover the topic relatively comprehensively and at the same time be completed in a reasonable amount of time of approximately 10 minutes. The LIST-K was used as part of a combined data collection along with other questionnaires on their personal data, their preferences regarding certain learning elements, their learning style (i.e. the ILS), and personality (i.e. the BFI-10). A total of 207 students from different study programs participated via an online survey created using the survey tool "LimeSurvey". Participation in the study was voluntary, anonymously, and in compliance with the GDPR. Overall, the results of the LIST-K show that students are willing to work intensively on relevant topics intensively and to perform beyond the requirements of the course seeking additional learning material. At the same time, however, it is apparent that the organization of their own learning process could still be improved. For example, students start repeating content too late (mean=2.70; SD=0.92) and do not set goals for themselves and do not create a learning plan (mean=3.19; SD=0.90). They also learn without a schedule (mean=2.23; SD=0.97) and miss opportunities to learn together with other students (mean=3.17; SD=0.94). The findings of the data collection will be used to create an AI-based adaptive learning management system that will create individualized learning paths for students in their respective courses. From the results of the LIST-K, it appears that the adaptive learning management system should primarily support organizational aspects of student learning. Even small impulses (an individual schedule of when to learn what or a hierarchical structuring of the learning material) could help students to complete their courses more successfully and improve their learning. Y1 - 2023 U6 - https://doi.org/10.21125/iceri.2023.1147 SP - 4611 EP - 4620 PB - IATED ER - TY - CHAP A1 - Bugert, Flemming A1 - Nadimpalli, Vamsi Krishna A1 - Bittner, Dominik A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Maier, Robert A1 - Röhrl, Simon A1 - Staufer, Susanne A1 - Hauser, Florian A1 - Mottok, Jürgen ED - Mottok, Jürgen ED - Hagel, Georg T1 - ML based Evaluation Methodology for Learning Path Recommender Systems T2 - Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025 N2 - In education, recommender systems can provide students with personalized learning materials based on their preferences. When comparing various recommendation algorithms, the main question is, which algorithm provides the most suitable recommendations for each student. Answering this question requires a quantitative evaluation methodology (i.e. a concrete metric) for ranking the results of (even non-deterministic) recommender systems. While there is already literature on this topic, the uniqueness of our approach lies in the application of machine learning: we deploy a likelihood based analysis via Hidden Markov Models named Aiakos. With this strategy, we aim to provide data-driven insights about accuracy and stability of recommendations towards a more reasonable selection of the appropriate recommender system. The training data for the Hidden Markov Models is collected from 80 students. Data from another 26 students is then used to discuss the behavior of our evaluation procedure considering a single recommendation as well as the results from 100 recommendations. Furthermore, the proposed concept allows to be applied to other domains as well. Y1 - 2025 SN - 9798400712821 U6 - https://doi.org/10.1145/3723010.3723022 SP - 40 EP - 48 PB - ACM ER - TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Bittner, Dominik A1 - Staufer, Susanne A1 - Röhrl, Simon A1 - Hauser, Florian A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Maier, Robert A1 - Mottok, Jürgen T1 - Probabilistic Machine Learning for Simulating Complex Learner Profiles T2 - 2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), 06-08 November 2024, Paris, France N2 - Data is the foundation of all machine learning applications. In education science, especially for the learner characteristics that drive personalized learning, it is difficult to collect and often uncertain. It is challenging to model, train, evaluate, and analyze the underlying algorithms when developing AI-based systems and having small sample sizes. To address these problems, we present a synthetic data generator utilising probabilistic models. This generator can effectively model and simulate complex learner profiles. To achieve this, we collected extensive data on learning styles, learning strategies, personalities, and preferred learning paths from 593 students over several semesters at a higher education level. Then, Bayesian networks, Hidden Markov Models, and Markov Chains are used to model the relationships between learner profiles. Using the Bayesian information criterion, and cross-validation with log-likelihood scores, we compare various models to select the best fitting one for synthesizing the data. The synthetic data is then evaluated using statistical validation techniques. In addition, we developed a simulation module with the option to simulate learner profiles based on manual user-defined inputs. The data and code used in this work are available as open source1110.5281/zenodo.13768136 [Titel anhand dieser DOI in Citavi-Projekt übernehmen] contributing to open science and developers for customized simulated data. In the future, this data will refine the training, evaluation, analysis, and benchmarking of algorithms for personalized learning. KW - probabilistic models KW - synthetic data KW - learner characteristics KW - generative AI KW - higher education area Y1 - 2024 SN - 979-8-3315-1663-5 U6 - https://doi.org/10.1109/ITHET61869.2024.10837641 SN - 2473-2060 SP - 1 EP - 10 PB - IEEE ER -