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    <title language="eng">Towards personalized learning paths in adaptive learning management systems: bayesian modelling of psychological theories</title>
    <abstract language="eng">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.&#13;
&#13;
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.&#13;
&#13;
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.</abstract>
    <parentTitle language="eng">Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023</parentTitle>
    <identifier type="doi">10.21125/iceri.2023.1144</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Flemming Bugert</author>
    <author>Dominik Bittner</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Susanne Staufer</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
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    <language>eng</language>
    <pageFirst>4056</pageFirst>
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    <title language="eng">Deep learning for eye movement classification</title>
    <parentTitle language="eng">ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain</parentTitle>
    <identifier type="doi">10.21125/iceri.2024.1028</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Timur Ezer</author>
    <author>Moritz Plößl</author>
    <author>Lisa Grabinger</author>
    <author>Dominik Bittner</author>
    <author>Susanne Staufer</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Flemming Bugert</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
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  <doc>
    <id>1382</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>98</pageNumber>
    <edition/>
    <issue/>
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    <type>periodicalpart</type>
    <publisherName/>
    <publisherPlace>Regensburg</publisherPlace>
    <creatingCorporation>Ostbayerische Technische Hochschule Regensburg</creatingCorporation>
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    <title language="deu">Forschung 2018</title>
    <identifier type="isbn">978-3-9818209-5-9</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-13826</identifier>
    <identifier type="doi">10.35096/othr/pub-1382</identifier>
    <author>Christian Broser</author>
    <author>Thomas Falter</author>
    <author>Robert Damian Ławrowski</author>
    <author>Amelie Altenbuchner</author>
    <author>Daniel Vögele</author>
    <author>Claus Koss</author>
    <author>Matthias Schlamp</author>
    <author>Jan Dunnweber</author>
    <author>Oliver Steffens</author>
    <author>Markus Heckner</author>
    <author>Sabine Jaritz</author>
    <author>Thomas Schiegl</author>
    <author>Sabine Corsten</author>
    <author>Norina Lauer</author>
    <author>Katherine Guertler</author>
    <author>Eric Koenig</author>
    <author>Sonja Haug</author>
    <author>Dominik Huber</author>
    <author>Clemens Birkenmaier</author>
    <author>Lars Krenkel</author>
    <author>Thomas Wagner</author>
    <author>Xenia Justus</author>
    <author>Sean Patrick Saßmannshausen</author>
    <author>Nadine Kleine</author>
    <author>Karsten Weber</author>
    <author>Carina N. Braun</author>
    <author>Giuliano Giacoppo</author>
    <author>Michael Heinrich</author>
    <author>Tobias Just</author>
    <author>Thomas Schreck</author>
    <author>Andreas Schnabl</author>
    <author>Amador Téran Gilmore</author>
    <author>Samuel Roeslin</author>
    <author>Sandra Schmid</author>
    <author>Felix Wellnitz</author>
    <author>Sebastian Malz</author>
    <author>Andreas Maurial</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <author>Meike Klettke</author>
    <author>Stefanie Scherzinger</author>
    <author>Uta Störl</author>
    <author>Markus Heckner</author>
    <author>Alexander Bazo</author>
    <author>Christian Wolff</author>
    <author>Andreas Kopper</author>
    <author>Markus Westner</author>
    <author>Christian Pongratz</author>
    <author>Ingo Ehrlich</author>
    <author>Ulrich Briem</author>
    <author>Sebastian Hederer</author>
    <author>Marcus Wagner</author>
    <author>Moritz Schillinger</author>
    <author>Julien Görlach</author>
    <author>Stefan Hierl</author>
    <author>Marco Siegl</author>
    <author>Christoph Langer</author>
    <author>Matthias Hausladen</author>
    <author>Rupert Schreiner</author>
    <author>Matthias Haslbeck</author>
    <author>Reinhard Kreuzer</author>
    <author>Oliver Brückl</author>
    <author>Belal Dawoud</author>
    <author>Hans-Peter Rabl</author>
    <author>Bernd Gamisch</author>
    <author>Ottfried Schmidt</author>
    <author>Michael Heberl</author>
    <author>Bianca Gänsbauer</author>
    <author>Werner Bick</author>
    <author>Andreas Ellermeier</author>
    <author>Gareth J. Monkman</author>
    <author>Nina Prem</author>
    <author>Dirk Sindersberger</author>
    <author>Karl Tschurtschenthaler</author>
    <author>Maximilian Aurbach</author>
    <author>Sebastian Dendorfer</author>
    <author>Michael A. Betz</author>
    <author>Tamara Szecsey</author>
    <author>Wolfgang Mauerer</author>
    <author>Florian Murr</author>
    <series>
      <title>Forschungsberichte der OTH Regensburg</title>
      <number>2018</number>
    </series>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Forschung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Forschungsbericht</value>
    </subject>
    <collection role="institutes" number="HL">Hochschulleitung/Hochschulverwaltung</collection>
    <collection role="othpublikationsherkunft" number="">Von der OTH Regensburg herausgegeben</collection>
    <collection role="persons" number="weberlate">Weber, Karsten (Prof. Dr.) - Labor für Technikfolgenabschätzung und Angewandte Ethik</collection>
    <collection role="institutes" number="IAFW">Zentrum für Forschung und Transfer (ZFT ab 2024; vorher: IAFW)</collection>
    <collection role="persons" number="bruecklfenes">Brückl, Oliver (Prof. Dr.) - FENES / Forschungsgruppe Energienetze</collection>
    <collection role="persons" number="hauglasofo">Haug, Sonja (Prof. Dr.) - Labor Empirische Sozialforschung</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/1382/Forschung-OTHR_2018.pdf</file>
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  <doc>
    <id>7909</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
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    <type>preprint</type>
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    <title language="eng">Underreliance Harms Human-AI Collaboration More Than Overreliance in Medical Imaging</title>
    <abstract language="deu">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.</abstract>
    <identifier type="doi">10.31219/osf.io/4wv8j</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Susanne Gaube</author>
    <author>Ekaterina Jussupow</author>
    <author>Eesha Kokje</author>
    <author>Jowaria Khan</author>
    <author>Elizabeth Bondi-Kelly</author>
    <author>Andreas Schicho</author>
    <author>Felipe Campos Kitamura</author>
    <author>Timo Kevin Koch</author>
    <author>Timur Ezer</author>
    <author>Jürgen Mottok</author>
    <author>Eva Lermer</author>
    <author>Marzyeh Ghassemi</author>
    <author>Errol Colak</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
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    <id>8304</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>106</pageFirst>
    <pageLast>114</pageLast>
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    <edition/>
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    <publisherName>ACM</publisherName>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-06-01</publishedDate>
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    <title language="eng">Eye Movement Modelling Examples on Usability Heuristics</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025</parentTitle>
    <identifier type="isbn">9798400712821</identifier>
    <identifier type="doi">10.1145/3723010.3723035</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Dominik Bittner</author>
    <author>Florian Hauser</author>
    <author>Fabian Engl</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
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    <id>732</id>
    <completedYear/>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
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    <publisherName/>
    <publisherPlace>Regensburg</publisherPlace>
    <creatingCorporation>Hochschule für Angewandte Wissenschaften Regensburg</creatingCorporation>
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    <title language="deu">Forschungsbericht 2011 / Hochschule für Angewandte Wissenschaften - Fachhochschule Regensburg</title>
    <identifier type="issn">1868-3533</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-7321</identifier>
    <identifier type="doi">10.35096/othr/pub-732</identifier>
    <author>Thomas Rück</author>
    <author>Rudolf Bierl</author>
    <author>Alfred Lechner</author>
    <author>Antonia Graf</author>
    <author>Florian Dams</author>
    <author>Rupert Schreiner</author>
    <author>Eberhard Auchter</author>
    <author>Willy Kriz</author>
    <author>MIchael Deubzer</author>
    <author>Frank Schiller</author>
    <author>Jürgen Mottok</author>
    <author>Michael Niemetz</author>
    <author>Ulrich Margull</author>
    <author>Georg Hagel</author>
    <author>Matthias Utesch</author>
    <author>Franz Waldherr</author>
    <author>Matthias Böhm</author>
    <author>Judith Fraunhoffer</author>
    <author>Armin Gardeia</author>
    <author>Ralph Schneider</author>
    <author>Janet Streubel</author>
    <author>Dieter Landes</author>
    <author>Reimer Studt</author>
    <author>Dominik Peuker</author>
    <author>Georg Scharfenberg</author>
    <author>Christian Hook</author>
    <author>Dietwald Schuster</author>
    <author>Ingo Ehrlich</author>
    <author>Heinrich Dinnebier</author>
    <author>Ulrich Briem</author>
    <author>Stephan Lämmlein</author>
    <author>Alexander Koder</author>
    <author>Adam Bialek</author>
    <author>Axel Genewsky</author>
    <author>Michael Neumeier</author>
    <author>Philipp Schlosser</author>
    <author>Hans-Peter Rabl</author>
    <author>Matthias Paule</author>
    <author>Christoph Galster</author>
    <author>Michael Schiedermeier</author>
    <author>Andreas Zwickel</author>
    <author>Christoph Hobmeier</author>
    <author>Tobias Bischoff</author>
    <author>Georg Rill</author>
    <author>Thomas Schaeffer</author>
    <author>Martin Arbesmeier</author>
    <author>Andreas Groß</author>
    <author>Thomas Schlegl</author>
    <author>Mark Becker</author>
    <author>Konrad Senn</author>
    <author>Claus Schliekmann</author>
    <author>Peter Scholz</author>
    <author>Christian Sippl</author>
    <author>Martin Grill</author>
    <series>
      <title>Forschungsberichte der OTH Regensburg</title>
      <number>2011</number>
    </series>
    <collection role="institutes" number="HL">Hochschulleitung/Hochschulverwaltung</collection>
    <collection role="othpublikationsherkunft" number="">Von der OTH Regensburg herausgegeben</collection>
    <collection role="institutes" number="IAFW">Zentrum für Forschung und Transfer (ZFT ab 2024; vorher: IAFW)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/732/Forschungsbericht_OTHR_2011.pdf</file>
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  <doc>
    <id>6769</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4611</pageFirst>
    <pageLast>4620</pageLast>
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    <title language="eng">On the analysis of student learning strategies: using the LIST-K questionnaire ro generate ai-based individualized learning paths</title>
    <abstract language="deu">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.&#13;
&#13;
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.&#13;
&#13;
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.&#13;
&#13;
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.&#13;
&#13;
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).&#13;
&#13;
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.</abstract>
    <parentTitle language="eng">Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023</parentTitle>
    <identifier type="doi">10.21125/iceri.2023.1147</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Florian Hauser</author>
    <author>Susanne Staufer</author>
    <author>Lisa Grabinger</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>8306</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>40</pageFirst>
    <pageLast>48</pageLast>
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    <publishedDate>2025-06-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">ML based Evaluation Methodology for Learning Path Recommender Systems</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025</parentTitle>
    <identifier type="isbn">9798400712821</identifier>
    <identifier type="doi">10.1145/3723010.3723022</identifier>
    <enrichment key="opus.import.date">2025-06-11T06:10:16+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Flemming Bugert</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Dominik Bittner</author>
    <author>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Robert Maier</author>
    <author>Simon Röhrl</author>
    <author>Susanne Staufer</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>7903</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>10</pageLast>
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    <title language="eng">Probabilistic Machine Learning for Simulating Complex Learner Profiles</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), 06-08 November 2024, Paris, France</parentTitle>
    <identifier type="doi">10.1109/ITHET61869.2024.10837641</identifier>
    <identifier type="isbn">979-8-3315-1663-5</identifier>
    <identifier type="issn">2473-2060</identifier>
    <enrichment key="opus.import.date">2025-01-20T09:16:32+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Flemming Bugert</author>
    <author>Dominik Bittner</author>
    <author>Susanne Staufer</author>
    <author>Simon Röhrl</author>
    <author>Florian Hauser</author>
    <author>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Robert Maier</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>probabilistic models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>synthetic data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learner characteristics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>generative AI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>higher education area</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>7901</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>6005</pageFirst>
    <pageLast>6014</pageLast>
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    <title language="eng">Predicting learner characteristics using machine learning</title>
    <abstract language="eng">In education science research, data collection is challenging due to difficulty identifying students at the higher education level, privacy concerns, and varying levels of student engagement. Importantly, psychological questionnaires can be lengthy, leading to incomplete responses. We conducted repeated studies, and over time, the focus of the research adapted, introducing new materials and consequently leading to missing learner characteristics in some datasets.&#13;
&#13;
In this research, the issue of incomplete learner characteristics is addressed using data from three different studies: winter term 2022/2023 (n=297), summer term 2023 (n=274), and winter term 2023/2024 (n=25). These studies collected various learner characteristics, such as learning styles, personalities, learning strategies, and learning element preferences. However, learning element preferences and learning strategies were missing in the winter term of 2022, and the summer term of 2023 respectively. To analyze the data and predict these missing features, statistical analysis, and machine learning techniques were employed. Then, these models are rigorously evaluated using cross-validation and performance metrics like accuracy, precision, recall, and F1-score. Our findings provide insights into the relationships between learners' learning styles, personalities, learning strategies, and learning element preferences. This offers valuable implications for the design and implementation of educational interventions, like learning path recommendations. The results imply that machine learning models can predict missing learner characteristics, thus addressing the problem of incomplete data in educational research</abstract>
    <parentTitle language="eng">ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain</parentTitle>
    <identifier type="doi">10.21125/iceri.2024.1454</identifier>
    <enrichment key="opus.import.date">2025-01-20T09:16:32+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Susanne Staufer</author>
    <author>Timur Ezer</author>
    <author>Flemming Bugert</author>
    <author>Dominik Bittner</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Simon Röhrl</author>
    <author>Robert Maier</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>8655</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Prototyping for high-performance random number generators for critical systems</title>
    <abstract language="eng">The security of modern cryptographic applications depends on the quality of their random number generators (RNGs). While physical true random number generators (PTRNGs) provide high-quality randomness, their performance is often insufficient for high-throughput applications. On the other hand, deterministic random number generators (DRNGs) offer better speed but lack true randomness. This paper presents an approach to developing a high-performance PTRNG. The research focuses on optimizing the digitization of analog noise signals. For this purpose, an ADC was selected that can digitize analog signals at a high clock rate. The functionality of the ADC could be verified by digitizing reference signals. Preliminary results by digitizing the analog noise signal demonstrates the feasibility of the proposed approach, but highlight challenges in achieving good results for Min-Entropy.</abstract>
    <parentTitle language="eng">2025 International Conference on Applied Electronics (AE), 08-09. September 2025, Pilsen Czech Republic</parentTitle>
    <identifier type="doi">10.1109/AE66163.2025.11197780</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Pascal Hammer</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
  </doc>
  <doc>
    <id>7114</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4195</pageFirst>
    <pageLast>4205</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
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    <type>conferenceobject</type>
    <publisherName>IATED Academy</publisherName>
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    <title language="eng">Tyche algorithm: Markov models for generating learning paths in learning management systems</title>
    <abstract language="eng">In the intricate tapestry of the cosmos, where celestial threads weave stories of destiny, an enigmatic deity stands at the intersection of chance and fate — Tyche, the goddess of fortune. In science and probability, coincidence plays a distinctive role in Bayesian Networks (BNs) and Markov Models (MMs). This paper introduces the Tyche algorithm named after the goddess of fortune. The Tyche algorithm is a Markov model designed to generate learning paths in Learning Management Systems (LMSs). A learning path is a type of individualization that personalises the order of learning elements within an LMS course. Learning elements are fundamental components within an LMS course, depicting the learning content in diverse ways. In our case, the learning path tailoring is based on the learner’s learning style according to Felder-Silverman Learning Style Model (FSLSM) – an indicator for the ideal pathway and learning element for the learner’s optimum learning.&#13;
&#13;
The Tyche algorithm offers the advantage to provide students the most suitable learning path. Tyche is a MM structure with various matrices containing transition probabilities depending on the learning style. Nine categories of learning elements defined in a previous survey form the basis of the node structure of the MM. For a generic approach, a survey was designed to obtain the transition probabilities depending on the individual learning style. The survey with more than 100 German students participated is processed with the tool LimeSurvey. Students are asked about their learning style using the Index of Learning Styles (ILS) questionnaire according to Felder-Silverman and about the percentage probabilities of learning elements to get their individual sequence as learning path. The percentages are queried in two different ways. Firstly, the students were asked to provide a sequence of learning elements within each position of the sequence filled with probabilities for all learning elements. Secondly, a learning element is given and the students are asked to indicate which learning element they would work on next. The first way of asking for the probabilities is used to find the start node in the MM, whereas the second approach forms the probability matrices between the nodes within the MM.&#13;
&#13;
As result of the survey, the Tyche algorithm presents generic transition probabilities. It improves the learning process of individuals only by asking for their learning style: it generates individual learning paths through the learning elements within an LMS based on the MM explained above by solely getting the answers of the ILS questionnaire as input. In the future, other questionnaires such as BFI-10 for personality traits or LIST-K for learning strategies may offer a more comprehensive input. However, the next step is to evaluate Tyche with about 25 students in a software engineering lecture. This is planned for the year 2024.</abstract>
    <parentTitle language="eng">INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024</parentTitle>
    <identifier type="doi">10.21125/inted.2024.1080</identifier>
    <identifier type="isbn">978-84-09-59215-9</identifier>
    <identifier type="issn">2340-1079</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-71148</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Susanne Staufer</author>
    <author>Flemming Bugert</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Timur Ezer</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Dominik Bittner</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Markov model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning style</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning elements</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning management system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>higher education area</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7114/STAUFER2024TYC.pdf</file>
  </doc>
  <doc>
    <id>8784</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2562</pageFirst>
    <pageLast>2572</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">TYCHE ALGORITHM 2.0: Learning Paths from Questionnaire Responses and Learning Analytics</title>
    <abstract language="eng">The digitalization of learning processes has increased the need for adaptive learning paths tailored individually to learners. A novel algorithm for learning path generation is presented in this paper, namely Tyche 2.0. It extends the original Tyche approach after Staufer et al. – a Markov model for generating learning paths – by integrating additional learner data beyond learning styles (Index of Learning Styles (ILS)), including learning strategies (LIST-K questionnaire), personality traits (BFI-10 questionnaire), and learning analytics captured through screen recordings. In order to be able to use the screen recordings, a heuristic evaluates them. Furthermore, this enhanced algorithm employs Markov models to dynamically generate personalized learning paths. These are based on both questionnaire responses and real-time&#13;
engagement data, the weights of which undergo dynamic adjustment over time. We made a small evaluation of Tyche 2.0 without the learning analytics influence, which shows that there is room for further improvements. Future research will focus on evaluating whole Tyche 2.0 in another university setting to further improve personalization and user engagement.</abstract>
    <parentTitle language="eng">ICERI2025 Proceedings</parentTitle>
    <identifier type="doi">10.21125/iceri.2025.0831</identifier>
    <identifier type="isbn">978-84-09-78706-7</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-87846</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="ConferenceStatement">18th annual International Conference of Education, Research and Innovation, 10.11.2025 - 11.11.2025, Seville, Spain</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Susanne Staufer</author>
    <author>Timur Ezer</author>
    <author>Simon Röhrl</author>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Erika Antoni</author>
    <author>Jürgen Mottok</author>
    <author>Josefa Schaffer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Markov model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning paths</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning style</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning strategy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Personality traits</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning analytics</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/8784/STAUFER2025TYC.pdf</file>
  </doc>
  <doc>
    <id>8804</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1775</pageFirst>
    <pageLast>1784</pageLast>
    <pageNumber/>
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    <title language="eng">LEVERAGING FIVE QUESTIONNAIRES TO ANALYZE STUDENT LEARNING STRATEGIES AND GENERATE AI-POWERED INDIVIDUALIZED LEARNING PATHS</title>
    <abstract language="eng">Background:&#13;
The COVID-19 pandemic has significantly accelerated the shift toward online and blended learning in higher education, placing renewed emphasis on the individualization of learning content to meet diverse student needs. Even high-quality learning materials may fail to engage learners if they do not align with students’ personal preferences and learning styles. Identifying these learner preferences, therefore, emerges as a critical challenge.&#13;
&#13;
Objectives:&#13;
This paper presents ongoing work within a larger research project aimed at employing artificial intelligence to recommend optimal learning path for students in specific courses. Beyond mere optimization, the goal is to ensure the best possible fit between learning materials and individual learners.&#13;
&#13;
Sample &amp; Methods:&#13;
A total of 27 students from technical degree programs took part in this survey. All participation was voluntary, and data were handled in full compliance with GDPR regulations. Although our broader project integrates fine-grained learning analytics from Moodle, the present abstract focuses exclusively on the self-report questionnaire results. Participants completed five instruments:&#13;
1. Index of Learning Styles (ILS)&#13;
2. LIST-K (Learning and Study Strategies Inventory – Short version)&#13;
3. BFI-10 (Big Five Inventory – 10 items)&#13;
4. Custom Preferences Instrument, capturing preferences for specific learning elements (e.g. instructional videos, lecture notes, summaries) and basic demographic data&#13;
5. Motivational Value Systems Questionnaire (MVSQ), piloted last semester to assess value orientations and motivational drivers&#13;
&#13;
Results:&#13;
Preliminary analyses of the questionnaire data reveal:&#13;
- Learning Styles (ILS): The majority lean toward the visual learning type (M = 5.740, SD = 3.430).&#13;
- Learning Strategies (LIST-K): High scores on metacognitive strategies (M = 3.000; SD = 0.520) and collaboration with peers (M = 3.190; SD = 0.540).&#13;
- Preferred Learning Elements: Summaries, overviews, and self-checks are most favored.&#13;
- Value Orientations (MVSQ): Students are primarily driven by the pursuit of personal achievement (M = 4.400; SD = 11.140).&#13;
&#13;
Conclusion &amp; Significance:&#13;
By integrating these five standardized questionnaires, we gain valuable insights into student learning preferences—insights that complement our Moodle analytics in the broader project. Observed trends suggest that learning materials should be concise and designed to facilitate peer interaction and knowledge deepening. These findings will guide the refinement of our AI-driven recommendation engine, enhancing its ability to deliver personalized learning paths that boost both engagement and effectiveness.</abstract>
    <parentTitle language="eng">ICERI2025 Proceedings</parentTitle>
    <identifier type="doi">10.21125/iceri.2025.0658</identifier>
    <enrichment key="ConferenceStatement">18th annual International Conference of Education, Research and Innovation, 10.11.2025 - 11.11.2025, Seville, Spain</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Florian Hauser</author>
    <author>Susanne Staufer</author>
    <author>Simon Röhrl</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Jürgen Mottok</author>
    <author>Thomas Falter</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>AI in higher education</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>learning management system</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>adaptive learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>personalized learning paths</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>online and blended learning</value>
    </subject>
    <collection role="institutes" number="FakBW">Fakultät Business and Management</collection>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="2">Geistes- und Sozialwissenschaften</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
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  <doc>
    <id>8794</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
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    <title language="eng">EYE TRACKING GLASSES IN EDUCATIONAL SETTINGS: GUIDELINES ON DATA QUALITY</title>
    <abstract language="eng">Eye movement modeling examples, so-called EMME videos, are a valuable tool in education, helping learners better understand instructional content. Like conventional educational videos, EMME videos combine text, images, and voiceovers. However, they also display the instructor’s or teacher’s gaze, guiding learners attention to key elements. Although various approaches exist for creating EMME videos, there is currently no standardised guideline for ensuring gaze data quality. Eye tracking technology is essential to capture gaze behaviour, and in educational settings without a fixed computer monitor – such as when using blackboards or conducting live experiments – the usage of mobile eye tracking glasses is beneficial.&#13;
&#13;
An accuracy study is conducted using mobile eye tracking glasses to provide empirical guidance for the development of high-quality educational EMME videos and ensure that the instructor’s or teacher’s gaze is captured with high precision. The study uses the Tobii Pro Glasses 3 and involves a static and a dynamic setup with 34 participants. To gain insight into the effects of visual impairments on accuracy, we also include participants who wear contact lenses. In the static setup, participants are seated at a desk with a headrest and focus on a poster with nine fixation points. In the dynamic setup, participants are walking in a controlled half-circle around the poster while maintaining focus on its centre. Each setup is performed multiple times under varying lighting levels (300 lux, 700 lux) and distances between participant and poster (80 cm, 120 cm, 180 cm). This enables the simulation of diverse educational environments, including the possibility of a teacher’s or instructor’s movement.&#13;
&#13;
The study results will be evaluated regarding lighting conditions, the distance between the person wearing eye tracking glasses and the object, and possible influences of contact lenses. Based on these findings, favourable conditions for creating EMME videos in educational settings are collected, especially when working without a fixed computer monitor. The results address the outlined research gap by providing instructors and teachers with guidelines enabling them to produce high-quality educational EMME videos.</abstract>
    <parentTitle language="eng">ICERI2025 Proceedings</parentTitle>
    <identifier type="doi">10.21125/iceri.2025.1419</identifier>
    <enrichment key="opus.import.date">2026-01-12T13:41:50+00:00</enrichment>
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    <enrichment key="ConferenceStatement">18th annual International Conference of Education, Research and Innovation, 10.11.2025 - 11.11.2025, Seville, Spain</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Josefa Schaffer</author>
    <author>Timur Ezer</author>
    <author>Simon Röhrl</author>
    <author>Florian Hauser</author>
    <author>Susanne Staufer</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Lisa Grabinger</author>
    <author>Erika Antoni</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
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  <doc>
    <id>8808</id>
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    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>6709</pageFirst>
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    <title language="eng">Towards accurate eye tracking: quantifying error in linear pixel-to-degree conversion</title>
    <abstract language="eng">Eye tracking has become a powerful tool for analyzing cognitive processes in educational research. Educators and researchers can utilize eye tracking to model learners by determining their gaze patterns. In addition, eye tracking can be used directly for teaching by extracting experts' gaze behavior in certain areas and learning from it.&#13;
&#13;
However, eye tracking data is captured in a variety of coordinate systems, which can differ across individual studies. Consequently, algorithms, such as eye movement classifiers, must frequently convert gaze data between different coordinate systems. In particular, the conversion from screen pixels into visual degrees is typically approximated by a linear conversion in current literature and standard practice. However, this approximation introduces inaccuracies, thus potentially obscuring eye movements relevant to educational research.&#13;
&#13;
This paper provides a detailed geometric and analytical examination of the commonly applied linear approximation, quantifying its error in comparison to the exact coordinate conversion from screen pixels to angular degrees. For this purpose, the exact conversion formulas are mathematically derived from geometric optics, enabling researchers and educators to use them in their work. Utilizing these derived transformations can improve the robustness of analyses, for example, when detecting subtle eye movements.&#13;
&#13;
The present work supports educational research using eye tracking to achieve more insightful findings that may have previously been obscured by measurement inaccuracies. Thereby, we provide an important contribution toward more reliable and valid research and educational practices in eye tracking.</abstract>
    <parentTitle language="eng">ICERI2025 Proceedings</parentTitle>
    <identifier type="doi">10.21125/iceri.2025.1841</identifier>
    <enrichment key="ConferenceStatement">18th annual International Conference of Education, Research and Innovation, 10.11.2025 - 11.11.2025, Seville, Spain</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Timur Ezer</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Eye tracking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>coordinate system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>conversion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>angular degree</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>pixel</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
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  <doc>
    <id>8833</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst>112</pageFirst>
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    <publisherName>IARIA</publisherName>
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    <title language="eng">Towards Post-Quantum-Ready Automated Certificate Lifecycle Management in Operational Technology</title>
    <abstract language="eng">Operational Technology (OT) systems increasingly depend on robust and automated certificate lifecycle management to maintain secure operations across long device lifespans and constrained environments. As quantum-capable adversaries emerge, these systems must also support cryptographic agility and prepare for a seamless transition to Post-Quantum Cryptography (PQC). This work presents a crypto-agile, post-quantum-ready testbed architecture that extends existing standards, such as Enrollment over Secure Transport (EST) and Bootstrapping Remote Secure Key Infrastructure (BRSKI), to support hybrid certificates, hardware-based key storage, and protocol flexibility for device bootstrapping and certificate management. A work-in-progress prototype implementation demonstrates support for both traditional and PQC algorithms across device types. Planned evaluations target performance on constrained devices, PQC readiness, and compatibility with alternative protocols. The system lays a foundation for secure and standards-compliant certificate management in future-proof OT deployments.</abstract>
    <parentTitle language="eng">SECURWARE 2025, The Nineteenth International Conference on Emerging Security Information, Systems and Technologies, 26.-30.10.2025, Barcelona</parentTitle>
    <identifier type="isbn">978-1-68558-306-4</identifier>
    <identifier type="url">https://www.thinkmind.org/library/SECURWARE/SECURWARE_2025</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Ayham Alhulaibi</author>
    <author>Tobias Frauenschläger</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automated Device Onboarding</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>BRSKI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Operational Technology Security.</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Post-Quantum Cryptography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Public Key Infrastructure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Security Token</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
  </doc>
  <doc>
    <id>8793</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>5677</pageFirst>
    <pageLast>5687</pageLast>
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    <title language="eng">EXPERT SURVEYS TO REAL TIME ADAPTATION OF LEARNING PATHS</title>
    <abstract language="eng">Learning management systems rely on adaptive algorithms that use learner preferences to personalize the instructional content in form of learning paths. However, these preferences are uncertain in nature, and change over time. The present solutions are either static or purely data-driven missing the dynamic adaption to changes in the preferences and infusion of pedagogical nuances respectively.&#13;
&#13;
This paper introduces an extended variant of Nestor, our Bayesian network engine that models personality traits, learning styles, and learning strategies. This extension overlays a lightweight rule-based mechanism whose “secret recipe’’ lies in the infusion of expert-derived weights adapting learning paths dynamically whenever a learner selects new material in Moodle.&#13;
&#13;
To parameterise these rules, we conducted a structured survey with 12 hand-picked professors and researchers in educational science. Each expert responded to 4 demographic items and 12 item that are distributed across algorithm-overview, scenario-based, and example-based categories, thereby supplying the nuanced weightings that result the personalised recommendations.&#13;
&#13;
This hybrid system (Nestor plus the expert-infused rule layer) operated during the winter term of 2025. 18 students completed an end-of-term questionnaire. Although their learning gains were not recorded, the majority of respondents reported positive or neutral experiences with the dynamically adapted learning paths.&#13;
&#13;
The {Future work} will compare three engines:&#13;
(i) the present dynamic, expert-infused rule layer on top of the static Bayesian network,&#13;
(ii) purely data-driven machine-learning models that neglect expert weighting, and&#13;
(iii) the original static-adaptation Bayesian network without rules.&#13;
&#13;
Analyses of log files, intermediate satisfaction surveys, and pre/post term surveys will clarify whether this on-the-fly adaptation and pedagogical nuance lead to measurable learning benefits.</abstract>
    <parentTitle language="eng">ICERI2025 Proceedings</parentTitle>
    <identifier type="doi">10.21125/iceri.2025.1571</identifier>
    <enrichment key="opus.import.date">2026-01-12T13:41:50+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
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    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Robert Maier</author>
    <author>Susanne Staufer</author>
    <author>Simon Röhrl</author>
    <author>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
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  <doc>
    <id>4351</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
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    <edition/>
    <issue/>
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    <publisherName>IARIA</publisherName>
    <publisherPlace>[Wilmington, DE, USA]</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-06-12</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">An Automotive Penetration Testing Framework for IT-Security Education</title>
    <abstract language="eng">Automotive Original Equipment Manufacturer (OEM) and suppliers started shifting their focus towards the security of their connected electronic programmable products recently since cars used to be mainly mechanical products. However, this has changed due to the rising digitalization of vehicles. Security and functional safety have grown together and need to be addressed as a single issue, referred to as automotive security, in the following article. One way to accomplish security is automotive security education. The scientific contribution of this paper is to establish an Automotive Penetration Testing Education Platform (APTEP). It consists of three layers representing different attack points of a vehicle. The layers are the outer, inner, and core layers. Each of those contains multiple interfaces, such as Wireless Local Area Network (WLAN) or electric vehicle charging interfaces in the outer layer, message bus systems in the inner layer, and debug or diagnostic interfaces in the core layer. One implementation of APTEP is in a hardware case and as a virtual platform, referred to as the Automotive Network Security Case (ANSKo). The hardware case contains emulated control units and different communication protocols. The virtual platform uses Docker containers to provide a similar experience over the internet. Both offer two kinds of challenges. The first introduces users to a specific interface, while the second combines multiple interfaces, to a complex and realistic challenge. This concept is based on modern didactic theory, such as constructivism and problem-based learning. Computer Science students from the Ostbayerische Technische Hochschule (OTH)Regensburg experienced the challenges as part of a special topic course and provided positive feedback.</abstract>
    <parentTitle language="eng">Cloud Computing 2022: The Thirteenth International Conference on Cloud Computing, GRIDs, and Virtualization, Special Track FAST-CSP, Barcelona, Spain, 24.-28.04.2022</parentTitle>
    <identifier type="isbn">978-1-61208-948-5</identifier>
    <identifier type="url">https://www.thinkmind.org/index.php?view=article&amp;articleid=cloud_computing_2022_1_10_28001</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Stefan Schönhärl</author>
    <author>Philipp Fuxen</author>
    <author>Julian Graf</author>
    <author>Jonas Schmidt</author>
    <author>Rudolf Hackenberg</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IT-Security</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Education</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automotive</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Penetration testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Education framework</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Informationssicherheit und Complience (ISC)</collection>
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  <doc>
    <id>6105</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>83</pageFirst>
    <pageLast>92</pageLast>
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    <publisherName>Association for Computing Machinery</publisherName>
    <publisherPlace>New York</publisherPlace>
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    <title language="eng">Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning Management Systems</title>
    <abstract language="eng">Nowadays, learning management systems are widely employed in all educational institutions to instruct students as a result of the increasing in online usage. Today’s learning management systems provide learning paths without personalizing them to the characteristics of the learner. Therefore, research these days is concentrated on employing AI-based strategies to personalize the systems. However, there are many different AI algorithms, making it challenging to determine which ones are most suited for taking into account the many different features of learner data and learning contents. This paper conducts a systematic literature review in order to discuss the AI-based methods that are frequently used to identify learner characteristics, organize the learning contents, recommend learning paths, and highlight their advantages and disadvantages.</abstract>
    <parentTitle language="eng">Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023</parentTitle>
    <identifier type="doi">10.1145/3593663.3593681</identifier>
    <identifier type="isbn">978-1-4503-9956-2</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Florian Hauser</author>
    <author>Dominik Bittner</author>
    <author>Lisa Grabinger</author>
    <author>Susanne Staufer</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>6765</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>5153</pageFirst>
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    <title language="eng">Unveiling the secrets of learning styles: decoding eye movements via machine learning</title>
    <abstract language="eng">Universities are faced with a rising number of dropouts in recent years. This is largely due to students' limited capability of finding individual learning paths through various course materials. However, a possible solution to this problem is the introduction of adaptive learning management systems, which recommend tailored learning paths to students – based on their individual learning styles. For the classification of learning styles, the most commonly used methods are questionnaires and learning analytics. Nevertheless, both methods are prone to errors: questionnaires may give superficial answers due to lack of time or motivation, while learning analytics do not reflect offline learning behavior. This paper proposes an alternative approach to classify students' learning styles by integrating eye tracking in combination with Machine Learning (ML) algorithms.&#13;
&#13;
Incorporating eye tracking technology into the classification process eliminates the potential problems arising from questionnaires or learning analytics by providing a more objective and detailed analysis of the subject's behavior. Moreover, this approach allows for a deeper understanding of subconscious processes and provides valuable insights into the individualized learning preferences of students.&#13;
&#13;
In order to demonstrate this approach, an eye tracking study is conducted with 117 participants using the Tobii Pro Fusion. Using qualitative and quantitative analyses, certain patterns in the subjects' gaze behavior are assigned to their learning styles given by the validated Index of Learning Styles (ILS) questionnaire.&#13;
&#13;
In short, this paper presents an innovative solution to the challenges associated with classifying students' learning styles. By combining eye tracking data with ML algorithms, an accurate and insightful understanding of students' individual learning paths can be achieved, ultimately leading to improved educational outcomes and reduced dropout rates.</abstract>
    <parentTitle language="eng">Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023</parentTitle>
    <identifier type="doi">10.21125/iceri.2023.1291</identifier>
    <identifier type="isbn">978-84-09-55942-8</identifier>
    <note>Zugehöriges Datenset: https://doi.org/10.5281/zenodo.8349468</note>
    <enrichment key="opus.import.date">2023-12-14T12:10:39+00:00</enrichment>
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    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Dominik Bittner</author>
    <author>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>8112</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <publishedDate>2023-09-21</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Eye Tracking based Learning Style Identification for Learning Management Systems [Data set]</title>
    <abstract language="eng">In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.</abstract>
    <identifier type="doi">10.5281/zenodo.8349468</identifier>
    <note>Related works: &#13;
Is new version of publication: 10.1145/3593663.3593680 (DOI) &#13;
Is published in publication: 10.21125/iceri.2023.1291 (DOI)</note>
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    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Dominik Bittner</author>
    <author>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
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    <title language="eng">Study: MISRA C coding guidelines [Data set]</title>
    <abstract language="eng">This repository contains the material and obtained data of an eye tracking study on the topic "MISRA C coding guidelines".</abstract>
    <identifier type="doi">10.5281/zenodo.7898606</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Lisa Grabinger</author>
    <author>Alexander Homann</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
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