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    <completedYear/>
    <publishedYear>2022</publishedYear>
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    <language>eng</language>
    <pageFirst>116</pageFirst>
    <pageLast>129</pageLast>
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    <issue/>
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    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
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    <completedDate>2022-11-14</completedDate>
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    <title language="eng">Towards Causal Model-Based Engineering in Automotive System Safety</title>
    <abstract language="eng">Engineering is based on the understanding of causes and effects. Thus, causality should also guide the safety assessment of complex systems such as autonomous driving cars. To ensure the safety of the intended functionality of these systems, normative regulations like ISO 21448 recommend scenario-based testing. An important task here is to identify critical scenarios, so-called edge and corner cases. Data-driven approaches to this task (e.g. based on machine learning) cannot adequately address a constantly changing operational design domain. Model-based approaches offer a remedy – they allow including different sources of knowledge (e.g. data, human experts) into safety considerations. With this paper, we outline a novel approach for ensuring automotive system safety. We propose to use structural causal models as a probabilistic modelling language to combine knowledge about an open-context environment from different sources. Based on these models, we investigate parameter configurations that are candidates for critical scenarios. In this paper, we first discuss some aspects of scenario-based testing. We then provide an informal introduction to causal models and relate their development lifecycle to the established V-model. Finally, we outline a generic workflow for using causal models to identify critical scenarios and highlight some challenges that arise in the process.</abstract>
    <parentTitle language="eng">Model-Based Safety and Assessment, 8th International Symposium, IMBSA 2022: Munich, Germany, September 5–7, 2022, Proceedings</parentTitle>
    <identifier type="doi">10.1007/978-3-031-15842-1_9</identifier>
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    <enrichment key="OtherSeries">Lecture Notes in Computer Science ; 13525</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Robert Maier</author>
    <author>Lisa Grabinger</author>
    <author>David Urlhart</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16315">Information und Kommunikation</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>6547</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>17</pageLast>
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    <type>article</type>
    <publisherName>IEEE</publisherName>
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    <title language="eng">Causal Models to Support Scenario-Based Testing of ADAS</title>
    <abstract language="eng">In modern vehicles, system complexity and technical capabilities are constantly growing. As a result, manufacturers and regulators are both increasingly challenged to ensure the reliability, safety, and intended behavior of these systems. With current methodologies, it is difficult to address the various interactions between vehicle components and environmental factors. However, model-based engineering offers a solution by allowing to abstract reality and enhancing communication among engineers and stakeholders. Applying this method requires a model format that is machine-processable, human-understandable, and mathematically sound. In addition, the model format needs to support probabilistic reasoning to account for incomplete data and knowledge about a problem domain. We propose structural causal models as a suitable framework for addressing these demands. In this article, we show how to combine data from different sources into an inferable causal model for an advanced driver-assistance system. We then consider the developed causal model for scenario-based testing to illustrate how a model-based approach can improve industrial system development processes. We conclude this paper by discussing the ongoing challenges to our approach and provide pointers for future work.</abstract>
    <parentTitle language="eng">IEEE Transactions on Intelligent Transportation Systems</parentTitle>
    <identifier type="doi">10.1109/TITS.2023.3317475</identifier>
    <identifier type="issn">1524-9050</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Robert Maier</author>
    <author>Lisa Grabinger</author>
    <author>David Urlhart</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated driving systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Causal inference</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ISO Standards</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>model-based testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Task analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Vehicles</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>5575</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
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    <publisherName>IEEE</publisherName>
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    <completedDate>2022-11-14</completedDate>
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    <title language="eng">Causality and Functional Safety - How Causal Models Relate to the Automotive Standards ISO 26262, ISO/PAS 21448, and UL 4600</title>
    <abstract language="eng">With autonomous driving, the system complexity of vehicles will increase drastically. This requires new approaches to ensure system safety. Looking at standards like ISO 26262 or ISO/PAS 21448 and their suggested methodologies, an increasing trend in the recent literature can be noticed to incorporate uncertainty. Often this is done by using Bayesian Networks as a framework to enable probabilistic reasoning. These models can also be used to represent causal relationships. Many publications claim to model cause-effect relations, yet rarely give a formal introduction of the implications and resulting possibilities such an approach may have. This paper aims to link the domains of causal reasoning and automotive system safety by investigating relations between causal models and approaches like FMEA, FTA, or GSN. First, the famous “Ladder of Causation” and its implications on causality are reviewed. Next, we give an informal overview of common hazard and reliability analysis techniques and associate them with probabilistic models. Finally, we analyse a mixed-model methodology called Hybrid Causal Logic, extend its idea, and build the concept of a causal shell model of automotive system safety.</abstract>
    <parentTitle language="eng">27th International Conference on Applied Electronics (AE): 06-07 September 2022, Pilsen, CZ</parentTitle>
    <identifier type="doi">10.1109/AE54730.2022.9920053</identifier>
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    <enrichment key="Kostentraeger">2056082</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Robert Maier</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Causality</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reliability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Functional Safety</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16315">Information und Kommunikation</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>7906</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>71</pageFirst>
    <pageLast>80</pageLast>
    <pageNumber/>
    <edition/>
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    <publisherName>IARIA</publisherName>
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    <completedDate>--</completedDate>
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    <title language="eng">Enhancing Phishing Detection: An Eye-Tracking Study on User Interaction and Oversights in Phishing Emails</title>
    <abstract language="eng">Phishing remains a significant threat to organizational security, necessitating effective countermeasures. This paper presents findings from an in-depth eye-tracking study with 103 participants, evaluating the effectiveness of phishing awareness tools and trainings. The study examines how a phishing awareness system influences user behavior, efficiency, and the ability to identify phishing attempts. By analyzing eye movements, the study reveals real-time interactions and oversights, providing insights into the decision-making process. Results indicate that while the system improves the efficiency of users already proficient in phishing detection, it does not universally enhance recognition rates. Notably, participants using the tool spent significantly less time looking at attachment-related phishing markers, indicating partial efficiency improvements. Since phishing attempts containing suspicious attachments were successful in 19% of cases, as compared to an overall phishing success rate of 15%, the phishing awareness tool is particularly useful here. A usability evaluation revealed that users reporting a higher perceived usability score profited more from the help of the tool. Additionally, no improvement in phishing detection rates was observed in users who had completed prior IT-security training, highlighting the necessity for a paradigm shift in phishing training to adequately prepare users for phishing attempts.</abstract>
    <parentTitle language="eng">SECURWARE 2024 : The Eighteenth International Conference on Emerging Security Information, Systems and Technologies, November 03-07, 2024, Nice, France</parentTitle>
    <identifier type="isbn">978-1-68558-206-7</identifier>
    <identifier type="url">https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_80_30041.html</identifier>
    <enrichment key="opus.import.date">2025-01-20T09:16:32+00:00</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Meret Kristen</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>
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  </doc>
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    <id>1383</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
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    <issue/>
    <volume/>
    <type>periodicalpart</type>
    <publisherName/>
    <publisherPlace>Regensburg</publisherPlace>
    <creatingCorporation>Ostbayerische Technische Hochschule Regensburg</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="deu">Forschungsbericht 2017</title>
    <identifier type="isbn">978-3-9818209-3-5</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-13835</identifier>
    <identifier type="doi">10.35096/othr/pub-1383</identifier>
    <author>Karsten Weber</author>
    <author>Sebastian Dendorfer</author>
    <author>Franz Süß</author>
    <author>Simone Kubowitsch</author>
    <author>Thomas Schratzenstaller</author>
    <author>Sonja Haug</author>
    <author>Christa Mohr</author>
    <author>Hans Kiesl</author>
    <author>Jörg Drechsler</author>
    <author>Markus Westner</author>
    <author>Jörn Kobus</author>
    <author>Martin J. W. Schubert</author>
    <author>Stefan Zenger</author>
    <author>Alexander Pietsch</author>
    <author>Josef Weiß</author>
    <author>Sebastian Hinterseer</author>
    <author>Roland Schieck</author>
    <author>Stefanie Scherzinger</author>
    <author>Meike Klettke</author>
    <author>Andreas Ringlstetter</author>
    <author>Uta Störl</author>
    <author>Tegawendé F. Bissyandé</author>
    <author>Achim Seeburger</author>
    <author>Timo Schindler</author>
    <author>Ralf Ramsauer</author>
    <author>Jan Kiszka</author>
    <author>Andreas Kölbl</author>
    <author>Daniel Lohmann</author>
    <author>Wolfgang Mauerer</author>
    <author>Johannes Maier</author>
    <author>Ulrike Scorna</author>
    <author>Christoph Palm</author>
    <author>Alexander Soska</author>
    <author>Jürgen Mottok</author>
    <author>Andreas Ellermeier</author>
    <author>Daniel Vögele</author>
    <author>Stefan Hierl</author>
    <author>Ulrich Briem</author>
    <author>Knut Buschmann</author>
    <author>Ingo Ehrlich</author>
    <author>Christian Pongratz</author>
    <author>Benjamin Pielmeier</author>
    <author>Quirin Tyroller</author>
    <author>Gareth J. Monkman</author>
    <author>Franz Gut</author>
    <author>Carina Roth</author>
    <author>Peter Hausler</author>
    <author>Rudolf Bierl</author>
    <author>Christian Prommesberger</author>
    <author>Robert Damian Ławrowski</author>
    <author>Christoph Langer</author>
    <author>Rupert Schreiner</author>
    <author>Yifeng Huang</author>
    <author>Juncong She</author>
    <author>Andreas Ottl</author>
    <author>Walter Rieger</author>
    <author>Agnes Kraml</author>
    <author>Thomas Poxleitner</author>
    <author>Simon Hofer</author>
    <author>Benjamin Heisterkamp</author>
    <author>Maximilian Lerch</author>
    <author>Nike Sammer</author>
    <author>Olivia Golde</author>
    <author>Felix Wellnitz</author>
    <author>Sandra Schmid</author>
    <author>Claudia Muntschick</author>
    <author>Wolfgang Kusterle</author>
    <author>Ivan Paric</author>
    <author>Oliver Brückl</author>
    <author>Matthias Haslbeck</author>
    <author>Ottfried Schmidt</author>
    <author>Peter Schwanzer</author>
    <author>Hans-Peter Rabl</author>
    <author>Michael Sterner</author>
    <author>Franz Bauer</author>
    <author>Sven Steinmann</author>
    <author>Fabian Eckert</author>
    <author>Andreas Hofrichter</author>
    <series>
      <title>Forschungsberichte der OTH Regensburg</title>
      <number>2017</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="sternerfenes">Sterner, Michael (Prof. Dr.) - FENES / Forschungsgruppe Energiespeicher</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/1383/Forschungsbericht_OTHR_2017.pdf</file>
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  <doc>
    <id>6772</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3121</pageFirst>
    <pageLast>3130</pageLast>
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    <publisherName>IATED</publisherName>
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    <completedDate>--</completedDate>
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    <title language="eng">Learning elements in online learning management systems</title>
    <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.2023.0815</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-67725</identifier>
    <enrichment key="opus.import.date">2023-12-14T12:10:39+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>Susanne Staufer</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Dominik Bittner</author>
    <author>Vamsi Krishna Nadimpalli</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>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/6772/STAUFER_2023_LEA_PDFA.pdf</file>
  </doc>
  <doc>
    <id>7824</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>6310</pageFirst>
    <pageLast>6319</pageLast>
    <pageNumber/>
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    <publisherName>IATED</publisherName>
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    <completedDate>--</completedDate>
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    <title language="eng">Persistence of Questionnaire Data on Learning Styles, Learning Strategies AND Personality Traits</title>
    <abstract language="eng">In the context of learning, questionnaires are carried out to gain information about learners. This learner profile has various benefits, like adapting the learning content, promoting of self-reflection, or increasing motivation in learning and therefore better learning results. Some researchers take the results of such questionnaires as ground truth or trust them blindly. Examining how persistent questionnaires are, asking the same questions twice, and comparing the results is a way to prove or falsify this statement.&#13;
&#13;
Our research procedure compares the results of three different questionnaires. The first questionnaire is the ILS (index of learning styles) according to the Felder-Silverman Learning Style Model (FSLSM) with about 44 questions. Learning strategies are examined with the LIST-K questionnaire consisting of 39 questions after Klingsieck. The third and last questionnaire is about personality traits. With only ten questions, Rammstedt et al. describe the BFI-10 questionnaire for personality traits. To compare results, the same study with the three named questionnaires is carried out twice with three to four months in between. The sample includes 35 students in their bachelor studies taking the course Software Engineering in a blended learning format. After collecting the questionnaire data from the students, a comparison is made in two different ways. First, we compare the data points based on the given answers. Second, a comparison is made based on the calculated questionnaire results because each of the three questionnaires has its own analysis method. For example, the analysis method of the ILS delivers as a result four integers representing the four characteristics of a learning style, while LIST-K makes a factor analysis. The results of the paper show that both the given answers and the calculated questionnaire results differ. Not one student has given the same answer inside each questionnaire with causes, like a change of learning style, learning strategy, and personality traits, or not filling in conscientiously.</abstract>
    <parentTitle language="eng">ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain</parentTitle>
    <identifier type="doi">10.21125/iceri.2024.1524</identifier>
    <identifier type="issn">2340-1095</identifier>
    <identifier type="isbn">978-84-09-63010-3</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-78246</identifier>
    <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>Susanne Staufer</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Florian Hauser</author>
    <author>Dominik Bittner</author>
    <author>Lisa Grabinger</author>
    <author>Flemming Bugert</author>
    <author>Timur Ezer</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <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>questionnaire study</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/7824/STAUFER2024PER.pdf</file>
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    <id>8340</id>
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    <publishedYear>2025</publishedYear>
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    <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>
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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>
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    <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>
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  <doc>
    <id>3376</id>
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    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <publisherPlace>Warrendale, Pennsylvania, USA</publisherPlace>
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    <contributingCorporation>Society of Automotive Engineers</contributingCorporation>
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    <title language="eng">Unsettled Topics Concerning the Impact of Quantum Technologies on Automotive Cybersecurity</title>
    <abstract language="eng">Quantum computing is considered the “next big thing” when it comes to solving computational problems impossible to tackle using conventional computers. However, a major concern is that quantum computers could be used to crack current cryptographic schemes designed to withstand traditional cyberattacks. This threat also impacts future automated vehicles as they become embedded in a vehicle-to-everything (V2X) ecosystem. In this scenario, encrypted data is transmitted between a complex network of cloud-based data servers, vehicle-based data servers, and vehicle sensors and controllers. While the vehicle hardware ages, the software enabling V2X interactions will be updated multiple times. It is essential to make the V2X ecosystem quantum-safe through use of “post-quantum cryptography” as well other applicable quantum technologies. This SAE EDGE™ Research Report considers the following three areas to be unsettled questions in the V2X ecosystem: How soon will quantum computing pose a threat to connected and automated vehicle technologies? What steps and measures are needed to make a V2X ecosystem “quantum-safe?” What standardization is needed to ensure that quantum technologies do not pose an unacceptable risk from an automotive cybersecurity perspective?</abstract>
    <identifier type="issn">2640-3536</identifier>
    <identifier type="doi">10.4271/EPR2020026</identifier>
    <enrichment key="OtherSeries">SAE Edge Research Report ; EPR2020026</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Joachim Taiber</author>
    <author>M. Chowdury</author>
    <author>G. Hammond</author>
    <author>V. Hipp</author>
    <author>B. Huttner</author>
    <author>J. Lonien</author>
    <author>Jürgen Mottok</author>
    <author>Florian Neukart</author>
    <author>T. Stengel</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
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    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
    <pageFirst>5457</pageFirst>
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    <title language="eng">Integrating deliberate practice in software engineering education</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.2024.1331</identifier>
    <enrichment key="opus.import.date">2025-01-20T09:16:32+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Timur Ezer</author>
    <author>Jürgen Mottok</author>
    <author>Hans Gruber</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>3047</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>291</pageFirst>
    <pageLast/>
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    <type>conferencepresentation</type>
    <publisherName>Earli</publisherName>
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    <completedDate>--</completedDate>
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    <title language="eng">Learning from Gaze: Eye Movement Modeling Examples in Software Engineering Education</title>
    <parentTitle language="eng">Earli Book of Abstracts</parentTitle>
    <identifier type="url">https://earli.org/assets/files/BOA-2021.pdf</identifier>
    <enrichment key="opus.import.date">2022-03-07T14:48:51+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>T. Stark</author>
    <author>Stefan Schreistetter</author>
    <author>Rebecca Reuter</author>
    <author>Florian Hauser</author>
    <author>Kenneth Holmqvist</author>
    <author>Jürgen Mottok</author>
    <author>Hans Gruber</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>5581</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-11-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Model Transformation of Application Software Component from Classic to Adaptive AUTOSAR: An Approach to Migrate Software Components</title>
    <parentTitle language="eng">SEAA 2018, the 44th EUROMICRO Conference on Software Engineering and Advanced Applications, August 29 - 31 2018, Prague, Czech Republic</parentTitle>
    <identifier type="isbn">978-3-902457-50-9</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Sangita De</author>
    <author>Michael Niklas</author>
    <author>Premek Brada</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16315">Information und Kommunikation</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>7345</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>7</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
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    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">Ariadne's Thread for Unravelling Learning Paths: Identifying Learning Styles via Hidden Markov Models</title>
    <abstract language="eng">Modern education through Learning Management Systems (LMSs) provides learners with personalized learning paths. This is achieved by first querying the learning style according to the theory of Felder and Silverman to recommend suitable learning content. However, a rigid learning style representation is lacking of adaptability to the learners' choices. Therefore, the present study evaluates the idea of providing adaption to the representation of learning styles by using Hidden Markov Models (HMMs). Thus, data is collected from participants out of the Higher Education Area. The Index of Learning Styles questionnaire is used to obtain the learning style based on the theory of Felder and Silverman. Also, a questionnaire that asks the respondents to create a preferred learning path with the sequence length of nine learning elements is provided. From the given data, we initially evaluate the probability relationships between learning styles and learning elements. Then, we use the Viterbi algorithm in HMMs to identify alterations in learning styles from the provided learning paths. The alignment is then quantified by introducing a metric called support value. The findings imply that our concept can be used to adapt the learning style based on the user's real choice of learning elements. Thus, the proposed model also offers a way to integrate a feedback loop within LMSs leading to an improvement of learning path recommendation algorithms.</abstract>
    <parentTitle language="eng">2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece</parentTitle>
    <identifier type="issn">2165-9567</identifier>
    <identifier type="doi">10.1109/EDUCON60312.2024.10578825</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Flemming Bugert</author>
    <author>Susanne Staufer</author>
    <author>Dominik Bittner</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Timur Ezer</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Surveys</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Knowledge engineering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Feedback loop</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning management systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Uncertainty</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Viterbi algorithm</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>6090</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>148</pageFirst>
    <pageLast>152</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Expert’s View: Eye Movement Modeling Examples in Software Engineering Education</title>
    <abstract language="deu">This study investigates the impact of eye movement modeling examples in Software Engineering education. Software Engineering is a highly visual domain. The daily tasks of a software engineer (e.g., formulating requirements, creating UML diagrams, or conducting a code review) require in many cases the use of certain visual strategies. Although these strategies can be found for experts, it has been observed in different eye tracking studies that students have difficulties in learning and applying them. To familiarize students with these visual strategies and to provide them with a better understanding for the cognitive processes involved, a total of seven eye movement modeling examples was created. The seven eye movement modeling examples cover relevant parts of an introductory Software Engineering lecture; they are focused on typical situations in which visual strategies are applied. The results of a questionnaire-based evaluation shows that students consider the eye movement modeling examples as useful, feel supported in their learning process, and would like to see more use of them in the Software Engineering lecture. Furthermore, the students suggested that eye movement modeling examples should also be used in other lectures.</abstract>
    <parentTitle language="eng">Proceedings of the 5th European Conference on Software Engineering Education,  Seeon/Germany, June 19 - 21, 2023</parentTitle>
    <identifier type="doi">10.1145/3593663.3593683</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>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Jürgen Mottok</author>
    <author>Sabrina Jahn</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>6091</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>48</pageFirst>
    <pageLast>55</pageLast>
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    <publisherName>ACM</publisherName>
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    <title language="eng">Towards Learning Style Prediction based on Personality</title>
    <abstract language="eng">This paper assesses the relation between personality, demographics, and learning style. Hence, data is collected from 200 participants using 1) the BFI-10 to obtain the participant’s expression of personality traits according to the five-factor model, 2) the ILS to determine the participant’s learning style according to Felder and Silverman, and 3) a demographic questionnaire. From the obtained data, we train and evaluate a Bayesian network. Using Bayesian statistics, we show that age and gender slightly influence personality and that demographics as well as personality have at least a minor effect on learning styles. We also discuss the limitations and future work of the presented approach.</abstract>
    <parentTitle language="eng">Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023</parentTitle>
    <identifier type="doi">10.1145/3593663.3593682</identifier>
    <identifier type="isbn">978-1-4503-9956-2</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
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    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Flemming Bugert</author>
    <author>Lisa Grabinger</author>
    <author>Dominik Bittner</author>
    <author>Florian Hauser</author>
    <author>Vamsi Krishna Nadimpalli</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>6086</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>138</pageFirst>
    <pageLast>147</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
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    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
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    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards Eye Tracking based Learning Style Identification</title>
    <abstract language="deu">The dropout rate at universities has been very high for years. Thereby, the inexperience and lack of knowledge of students in dealing with individual learning paths in various courses of study plays a decisive role. Adaptive learning management systems are suitable countermeasures, in which learners’ learning styles are classified using questionnaires or computationally intensive algorithms before a learning path is suggested accordingly. In this paper, a study design for student learning style classification using eye tracking is presented. Furthermore, qualitative and quantitative analyses clarify certain relationships between students’ eye movements and learning styles. With the help of classification based on eye tracking, the filling out of questionnaires or the integration of computationally or cost-intensive algorithms can be made redundant in the future.</abstract>
    <parentTitle language="eng">Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023</parentTitle>
    <identifier type="doi">10.1145/3593663.3593680</identifier>
    <identifier type="isbn">978-1-4503-9956-2</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
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    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Dominik Bittner</author>
    <author>Florian Hauser</author>
    <author>Vamsi Krishna Nadimpalli</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>6771</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4593</pageFirst>
    <pageLast>4603</pageLast>
    <pageNumber/>
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    <volume/>
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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>
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    <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>
    <enrichment key="opus.import.date">2025-01-20T09:16:32+00:00</enrichment>
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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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    <id>1382</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>98</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>periodicalpart</type>
    <publisherName/>
    <publisherPlace>Regensburg</publisherPlace>
    <creatingCorporation>Ostbayerische Technische Hochschule Regensburg</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
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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>
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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>
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  <doc>
    <id>8304</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>106</pageFirst>
    <pageLast>114</pageLast>
    <pageNumber/>
    <edition/>
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    <publisherName>ACM</publisherName>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-06-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <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>
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    <publishedYear>2023</publishedYear>
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    <language>eng</language>
    <pageFirst>4611</pageFirst>
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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>
    <enrichment key="opus.import.date">2023-12-14T12:10:39+00:00</enrichment>
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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>
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    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst>40</pageFirst>
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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>
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