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    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>25</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>11</volume>
    <type>article</type>
    <publisherName>Institute of Electrical and Electronics Engineers (IEEE)</publisherName>
    <publisherPlace/>
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    <title language="eng">Ethical Considerations of AI in Education: A Case Study based on Pythia Learning Enhancement System</title>
    <abstract language="eng">As the use of AI in education increases, addressing ethical concerns is essential for its responsible use and implementation. This study explores the ethical considerations of AI in education by analyzing a concrete application, the Pythia Learning Enhancement System, which enables personalized and adaptive educational experiences. In this case study, the concrete implementation of Pythia, contrary to high-level abstract analyses, provides practical insights into these ethical issues. To achieve this goal, we first define Pythia and outline its implementation. Using the various topics covered by Pythia, the ethical issues associated with each of these topics are outlined, resulting in a list of ten distinct ethical issues. Because there is overlap in issues covered by similar applications, they can benefit from this identification. An ethical (human rights) and didactic (constructivism) theoretical background is then provided as a basis for further discussion. This is used to build the arguments used in the main study. This consists of analyzing each of the issues and providing recommendations to mitigate potential harm arising from these issues. This helps to provide guidelines for future applications. In the analysis, the ethical discussion is mainly based on the human rights of the stakeholders involved, emphasizing human dignity. After the analysis, each of the issues is contextualized with the recent AI Act of the European Union, providing a novel mapping of the issues of AI in education to the AI Act. Finally, the limitations of the study are stated and a plan for future research is presented.</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="doi">10.1109/ACCESS.2025.3583975</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-83403</identifier>
    <note>Corresponding author der OTH Regensburg: Simon Röhrl</note>
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    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="CorrespondingAuthor">Simon Röhrl</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Simon Röhrl</author>
    <author>Susanne Staufer</author>
    <author>Flemming Bugert</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Dominik Bittner</author>
    <author>Timur Ezer</author>
    <author>Ricardo Altweck</author>
    <author>Sabine Graf</author>
    <author>Thomas Kriza</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ethics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Education</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Stakeholders</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Psychology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Privacy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Search problems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Guidelines</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Focusing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Europe</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="oaweg" number="">Corresponding author der OTH Regensburg</collection>
    <collection role="funding" number="">Publikationsfonds der OTH Regensburg</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/8340/Roehrl_IEEE_Access_2025.pdf</file>
  </doc>
  <doc>
    <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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    <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>
    <enrichment key="opus.import.date">2025-06-11T06:10:16+00:00</enrichment>
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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>
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  </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>
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    <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>
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  </doc>
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    <id>8655</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
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    <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>
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    <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>8784</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2562</pageFirst>
    <pageLast>2572</pageLast>
    <pageNumber/>
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    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IATED</publisherName>
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    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IATED</publisherName>
    <publisherPlace/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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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>
  </doc>
  <doc>
    <id>8794</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>5027</pageFirst>
    <pageLast>5038</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
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    <publisherName>IATED</publisherName>
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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>
    <enrichment key="opus.source">sword</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>
    <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>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>
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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>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Timur Ezer</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
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      <language>eng</language>
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      <language>eng</language>
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      <value>angular degree</value>
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      <language>eng</language>
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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>
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    <author>Ayham Alhulaibi</author>
    <author>Tobias Frauenschläger</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
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      <value>Automated Device Onboarding</value>
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    <subject>
      <language>eng</language>
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      <value>BRSKI</value>
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    <subject>
      <language>eng</language>
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      <value>Operational Technology Security.</value>
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    <subject>
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      <value>Post-Quantum Cryptography</value>
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    <subject>
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      <value>Public Key Infrastructure</value>
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      <value>Security Token</value>
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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>
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    <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>
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