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    <pageLast>27</pageLast>
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    <publisherName>Hochschule Landshut</publisherName>
    <publisherPlace>Landshut</publisherPlace>
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    <title language="eng">Integration and Evaluation of Different FPGA based Hardware Accelerators for the AES Algorithm in the TLS Protocol to be Used in Critical Automation Systems</title>
    <abstract language="eng">This thesis addresses the challenges of hardware-assisted cryptography in the context of automation systems in critical infrastructures. While hardware accelerators enhance cryptographic calculations in both performance and security, their permanence poses issues in adapting to evolving security concerns. To overcome this problem, we integrated and evaluated a hardware accelerator for the AES algorithm on an FPGA within a TLS application, comparing its performance with other accelerators. Employing a hybrid FPGA-CPU processor system, this setup achieves over 800 Mbit/s data rates, showcasing a more than 27 times acceleration in TLS transmissions compared to purely software-based solutions. The FPGA's flexibility and upgradability therefore make it an ideal platform for critical infrastructure, ensuring longevity and superior acceleration in TLS data transfers.</abstract>
    <parentTitle language="deu">4. Symposium Elektronik und Systemintegration, Tagungsband: Symposium am 17. April 2024 Hochschule Landshut</parentTitle>
    <identifier type="url">https://www.haw-landshut.de/static/ITZ/Bilder/Cluster_MST/ESI_Symposium/ESI_2024/Tagungsband/1B2_Frauenschlaeger_OTH-R_TB-ESI2024_s18-27.pdf</identifier>
    <identifier type="isbn">978-3-9818439-9-6</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Luca Horn</author>
    <author>Tobias Frauenschläger</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>
    <id>6256</id>
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    <publishedYear>2023</publishedYear>
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    <publisherName>ACM</publisherName>
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    <title language="eng">Visual Expertise in Code Reviews: Using Holistic Models of Image Perception to Analyze and Interpret Eye Movements</title>
    <abstract language="eng">This study uses holistic models of image perception to analyze and interpret eye movements during a code review. 23 participants (15 novices and 8 experts) take part in the experiment. The subjects’ task is to review six short code examples in C programming language and identify possible errors. During the experiment, their eye movements are recorded by an SMI 250 REDmobile. Additional data is collected through questionnaires and retrospective interviews. The results implicate that holistic models of image perception provide a suitable theoretical background for the analysis and interpretation of eye movements during code reviews. The assumptions of these models are particularly evident for expert programmers. Their approach can be divided into different phases with characteristic eye movement patterns. It is best described as switching between scans of the code example (global viewing) and the detailed examination of errors (focal viewing).</abstract>
    <parentTitle language="eng">ETRA '23: 2023 Symposium on Eye Tracking Research and Applications, Tubingen Germany 30 May 2023- 2 June 2023</parentTitle>
    <identifier type="doi">10.1145/3588015.3589189</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</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>
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  <doc>
    <id>6745</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>357</pageFirst>
    <pageLast>374</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>33</volume>
    <type>article</type>
    <publisherName>Czech Technical University in Prague</publisherName>
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    <title language="eng">An evolutionary fault injection settings search algorithm for attacks on safe and secure embedded systems</title>
    <parentTitle language="eng">Neural Network World (NNW)</parentTitle>
    <identifier type="doi">10.14311/NNW.2023.33.020</identifier>
    <enrichment key="opus.import.date">2023-11-20T07:29:48+00:00</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Enrico Pozzobon</author>
    <author>Nils Weiß</author>
    <author>Jürgen Mottok</author>
    <author>Václav Matoušek</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>
    <id>7842</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>51</pageFirst>
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    <completedDate>2024-12-02</completedDate>
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    <title language="eng">Assessment of a MACsec-based Security System for Use in Critical Infrastructure Communication</title>
    <abstract language="eng">This paper investigates the integration of Media Access Control Security (MACsec) into the communication of critical infrastructure, specifically within power grid applications, such as Substation Automation Systems (SAS) using the IEC 61850 standard. Building on the principles of both standards, this study aims to determine if MACsec can meet the security and performance requirements set by IEC 62351 for power system communications.&#13;
Furthermore, a test environment containing a number of Intelligent Electronic Devices (IEDs) supporting communication compliant to all IEC 61850 message types is established to evaluate this integration. The results of the measurements executed in this environment indicate that MACsec could secure all types of messages, such as Manufacturing Message Specification (MMS), Sampled-Value (SV) and Generic Object Oriented Substation Events (GOOSE), within the required time periods without significant delays. Even with additional encryption activated in the cipher suite, the resulting transmission times are well below the required times. This suggests that MACsec can enhance the security goals for industrial communication by providing confidentiality in addition to the already mandated assurance of authenticity and integrity to all messages without compromising performance. Only the requirement for end-to-end security cannot be met by MACsec in this configuration, as the security system re-encrypts with every hop of the transmission. For this reason, we propose a hybrid approach of Transport Layer Security (TLS) and MACsec as part of future work.</abstract>
    <parentTitle language="eng">Proceedings of the 12th Prague Embedded Systems Workshop, June 27 - 29, 2024, Horoměřice, Czech Republic</parentTitle>
    <identifier type="url">https://pesw.fit.cvut.cz/2024/PESW_2024.pdf</identifier>
    <identifier type="isbn">978-80-01-07303-2</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Lukas Füreder</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MACsec</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IEC61850</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IEC62351</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>GOOSE</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Secure Communication</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>6294</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">In-depth Benchmarking of Transfer Learning Techniques for Improved Bottle Recognition</title>
    <abstract language="eng">An immense diversity in bottle types requires high accuracy during sorting for recycling purposes by breweries. This extremely complex and time-consuming procedure can result in enormous additional costs for them. This paper presents transfer learning-based algorithms for classifying beer bottle brands using camera images, applicable in individual sorting solutions for different use cases. The problem is tackled using customised EfficientNet, InceptionResNet and VGG models along with an augmented dataset. In addition, a detailed analysis of different model and parameter combinations is performed, enabling tailor-made technologies for specific conditions and resource limitations. In accompanying validations and subsequent tests, a test accuracy of 100% in the recognition of beer brands could be achieved, proving the proposed method fully contributes to the solution of the problem.</abstract>
    <parentTitle language="eng">2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS), Guayaquil, Ecuador, 04-07 July 2023</parentTitle>
    <identifier type="isbn">979-8-3503-3337-4</identifier>
    <identifier type="doi">10.1109/ICPRS58416.2023.10178995</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Dominik Bittner</author>
    <author>Ricky-Ricardo Hendricks</author>
    <author>Luca Horn</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Analytical models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Costs</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transfer learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Benchmark testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cameras</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pattern recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Recycling</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>7133</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2871</pageFirst>
    <pageLast>2880</pageLast>
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    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">PYTHIA - AI SUGGESTED INDIVIDUAL LEARNING PATHS FOR EVERY STUDENT</title>
    <abstract language="eng">During the COVID-19 pandemic, the importance of digital course rooms, where teachers provide their learning materials, increased dramatically. While these platforms are crucial for providing teaching materials, they often fall short in addressing individual student needs. A system within an academic setting, capable of creating and presenting individual learning paths for each student, can solve these issues. These paths are composed of various learning elements - defined in our previous work as units of educational content with which a learner works.&#13;
&#13;
Currently, there is no suitable system that enables the integration of learning path generating algorithms into a digital course room. Therefore we present an application that enables this integration into the Moodle Learning Management System (LMS). More precisely, this paper presents a Moodle plugin together with its framework. It describes the mechanism for effectively collecting data from Moodle, which AI algorithms then use to generate personalized learning paths. Subsequently these paths are visualized with the help of the Moodle plugin.&#13;
&#13;
We started with a set of requirements and use cases for the interface connecting Moodle to the AI system, which were established with a group of experts. Based on the requirements, various relevant technologies were assessed, and the best ones were chosen for implementation.&#13;
&#13;
Following that, the paper develops a strategy for software structuring as well as an architecture, focusing on performance, modularity, and ease of deployment for widespread use. Furthermore, the architecture ensures a simple method for integrating the algorithms. Afterwards, the framework's concrete implementation is described. A technique for enriching learning elements with metadata is presented, and additionally a concept for presenting these learning elements within a hierarchy. Moreover, it is shown how questionnaire responses and learning analytics are utilized for data collection. We cover in detail techniques for extracting and storing data from the Moodle database, as well as methods for customizing Moodle course rooms and a standard API for incorporating AI algorithms.&#13;
&#13;
Finally, the paper discusses the application of the proposed framework in an actual course and how student feedback is collected, which could enhance the framework. It concludes with an assessment of the outcomes obtained and prospects for the framework's future advancements.</abstract>
    <parentTitle language="eng">INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024</parentTitle>
    <identifier type="doi">10.21125/inted.2024.0783</identifier>
    <identifier type="isbn">978-84-09-59215-9</identifier>
    <identifier type="issn">2340-1079</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>Simon Röhrl</author>
    <author>Susanne Staufer</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Flemming Bugert</author>
    <author>Flemming Bugert</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Dominik Bittner</author>
    <author>Timur Ezer</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Personalized Learning Paths</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning Management System</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software Architecture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Moodle</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</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>5716</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>106</pageFirst>
    <pageLast>118</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3&amp;4</issue>
    <volume>15</volume>
    <type>article</type>
    <publisherName>IARIA</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-12-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Cybersecurity Education Platform for Automotive Penetration Testing</title>
    <abstract language="eng">The paper presents a penetration testing framework for automotive IT security education and evaluates its realization. The automotive sector is changing due to automated driving functions, connected vehicles, and electric vehicles. This development also creates new and more critical vulnerabilities. This paper addresses a possible countermeasure, automotive IT security education. Some existing solutions are evaluated and compared with the created Automotive Penetration Testing Education Platform (APTEP) framework. In addition, the APTEP architecture is described. It consists of three layers representing different attack points of a vehicle. The realization of the APTEP is a hardware case and a virtual platform referred to as the Automotive Network Security Case (ANSKo). The hardware case contains emulated control units and different communication protocols. The virtual platform uses Docker containers to provide a similar experience over the internet. Both offer two kinds of challenges.&#13;
The first introduces users to a specific interface, while the second combines multiple interfaces, to a complex and realistic challenge. This concept is based on modern didactic theories, such as constructivism and problem-based/challenge-based learning.&#13;
Computer Science students from the Ostbayerische Technische Hochschule (OTH) Regensburg experienced the challenges as part of a elective subject. In an online survey evaluated in this paper, they gave positive feedback. Also, a part of the evaluation is the mapping of the ANSKo and the maturity levels in the Software Assurance Maturity Model (SAMM) practice Education &amp; Guidance as well as the SAMM practice Security Testing. The scientific contribution of this paper is to present an APTEP, a corresponding learning concept and an evaluation method.</abstract>
    <parentTitle language="eng">International Journal On Advances in Security</parentTitle>
    <identifier type="url">http://www.iariajournals.org/security/sec_v15_n34_2022_paged.pdf</identifier>
    <identifier type="issn">1942-2636</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Philipp Fuxen</author>
    <author>Stefan Schönhärl</author>
    <author>Jonas Schmidt</author>
    <author>Mathias Gerstner</author>
    <author>Sabrina Jahn</author>
    <author>Julian Graf</author>
    <author>Rudolf Hackenberg</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Challenge-based Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Education Framework</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Penetration Testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automotive</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IT-Security Education</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Informationssicherheit und Complience (ISC)</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>7113</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4224</pageFirst>
    <pageLast>4231</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IATED</publisherName>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learning elements in LMS - a survey among students</title>
    <abstract language="eng">In higher education, improving learning and learning success are goals of general improvement. Lecturers teaches content and students acquire that content in an efficient way. To structure content, learning element categories are evaluated from the student's point of view in higher education area. The aim is to validate given definitions of ten learning element categories within a Learning Management System (LMS).&#13;
&#13;
This paper evaluates a categorization of learning elements for organizing learning content in online education within LMSs. Therefore, ten categories of learning elements and corresponding definitions were defined in a previous work as base for this paper. The learning elements to examine are manuscript, exercise, quiz, brief overview, learning goal, summary, collaboration tool, auditory additional material, textual additional material, and visual additional material. To validate the definitions and to get improvements to each learning element a survey is processed. Beside the demographic data questions, the survey consists of two questions to the acceptance of the definitions and asks for improvements. 148 students between the ages 19 and 35 participate in the survey in summer term 2023. The education level of the participants ranges from undergraduates to Ph.D. students.&#13;
&#13;
The results of this paper are that more than 80% accept the given definitions. Some definitions of the learning elements are changed, but the changes are restricted to additions of maximal four words. This categorization of learning elements could lead to improvements in learning by giving the content more structure. With the structure students get the possibility to learn with preferred learning elements which could lead to more success in learning and to a decreasing dropout rate in universities. In the future, the learning elements allow to classify content within LMSs with the goal of generating individual learning paths. Furthermore, our project will integrate these learning elements, use them to generate learning paths, and could set a new standard in the way of personalized learning.</abstract>
    <parentTitle language="eng">INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024</parentTitle>
    <identifier type="doi">10.21125/inted.2024.1087</identifier>
    <identifier type="isbn">978-84-09-59215-9</identifier>
    <identifier type="issn">2340-1079</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-71135</identifier>
    <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>Flemming Bugert</author>
    <author>Timur Ezer</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning elements</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning management system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning objects</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>questionnaire</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>higher education area</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>content organization</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/7113/STAUFER2024LEA.pdf</file>
  </doc>
  <doc>
    <id>7821</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>695</pageFirst>
    <pageLast>701</pageLast>
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    <issue/>
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    <title language="eng">The impact of eyenalyzer</title>
    <abstract language="eng">Empirical research poses numerous challenges for beginners. This is especially true for data analysis – a task that usually requires knowledge from two distinct areas: statistics and programming. To support prospective researchers with that task, we developed a web-based tool called eyenalyzer. It supports common activities in the data analysis phase of empirical studies in a way that is suitable for novices in both, statistics and programming. The present article describes a controlled experiment investigating&#13;
the impact of this tool with a total of 20 participants. All of them are given a set of common data analysis tasks. Half of the participants complete the tasks using eyenalyzer, the other half can use anything except for eyenalyzer. For each task and participant, we record the time in minutes, the task score, and the perceived difficulty. The results confirm that our tool is a valuable support for novice researchers: With eyenalyzer, the participants are significantly faster, achieve higher scores, and perceive the tasks&#13;
to be less difficult.</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.2024.0271</identifier>
    <identifier type="isbn">978-84-09-63010-3</identifier>
    <identifier type="issn">2340-1095</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-78215</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Lisa Grabinger</author>
    <author>Timur Ezer</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>tool evaluation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>eye tracking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>empirical research</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/7821/Paper_IMAPCT_PDFA.pdf</file>
  </doc>
  <doc>
    <id>3043</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>6109</pageFirst>
    <pageLast>6118</pageLast>
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    <title language="eng">Under The Hood - A Concept for Virtualized Automotive Security Education</title>
    <abstract language="eng">In recent years, our society faced a massive interconnection of computer-based everyday objects, which opens these items for cyber-attacks. Dependent on the physical capabilities, successful attacks can vary from data exposure or a loss of functionality to a threat to life and limb. Connected- and autonomous vehicles are extremely safety-critical systems with a huge damage potential. This global trend, together with existing and upcoming regulations (ISO 270xx, ISO 21434, UNECE WP.29, UNECE R155), and the lack of qualified professionals create a tension field for the entire automotive industry.&#13;
&#13;
Hence, new education concepts for engineers of safety-critical and connected systems are necessary to secure our daily and future systems against cyber-attacks and raise awareness and knowledge of the topic of IT-Security. Existing automotive security education systems have one common problem: All systems are hardware-based and therefore have very steep learning curves for beginners. Hardware-based systems, in general, are expensive in their initial costs, require regular maintenance, and add diverse operational difficulties independent of the aspired education goal. Additionally, the global pandemic increased the necessity of virtual education concepts for security training in cyber-physical systems.&#13;
&#13;
Therefore, we present a novel concept for the education of cyber-security professionals for automotive systems based on discovery and problem-based learning in a virtual learning environment (VLE). Our concept contains individual exercises focusing on the topics of vulnerabilities and attacks in automotive networks and systems. Each exercise relates those topics to the corresponding security goals and countermeasures for mitigation. The learners work collaboratively in a self-contained manner within the VLE to acquire the necessary information to answer questions or find a solution to the given problem. To consider the heterogeneous background (e.g. knowledge, experience, preconceptions) of the learners, the topics can be presented in different difficulties, enabling an adaptable learning environment and different learning trajectories within the exercises.&#13;
&#13;
The concept is based on a VLE, consisting of automotive networks and components, which simulate the behavior of a vehicle. This environment provides a hands-on, "real-life" scenario, which allows discovery and problem-based learning in a realistic, but cheap and scalable education environment. Furthermore, virtualization removes common difficulties, always present in training on real hardware. This aims to decrease complexity, prevent learning obstacles related to hardware handling, and enables a location-independent learning environment.&#13;
&#13;
The target group of our education concept is Bachelor and Master students of computer science, engineering (e.g. electrical engineering, mechatronics), or similar studies, and (experienced) engineers from the industry.&#13;
&#13;
In summary, our publication contains two contributions. We present an adaptable virtual learning environment for automotive security education, combined with an educational concept based on discovery and problem-based learning techniques. The goals of our concept are the education of cyber-security professionals for safety-critical, connected automotive systems and the support of life-long learning reaching from academic education to training in the industry.</abstract>
    <parentTitle language="eng">EDULEARN21: Proceedings, 13th International Conference on Education and New Learning Technologies, Online Conference. 5-6 July, 2021</parentTitle>
    <identifier type="doi">10.21125/edulearn.2021.1231</identifier>
    <identifier type="issn">2340-1117</identifier>
    <identifier type="isbn">978-84-09-31267-2</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>Sabrina Jahn</author>
    <author>Nils Weiss</author>
    <author>Ugur Akcakoca</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automotive systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>discovery learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>higher education</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>inductive teaching</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>problem-based learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>security education</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>virtual learning environment</value>
    </subject>
    <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>7132</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2955</pageFirst>
    <pageLast>2961</pageLast>
    <pageNumber/>
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    <issue/>
    <volume/>
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    <publisherName>IATED Academy</publisherName>
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    <title language="eng">EYE TRACKING AS TECHNOLOGY IN EDUCATION: FURTHER INVESTIGATION OF DATA QUALITY AND IMPROVEMENTS</title>
    <abstract language="eng">Eye tracking serves as a powerful tool across a variety of empirical research areas: From usability research over cognitive research to educational research and applications in classrooms. However, data noise in eye tracking data poses a challenge to researchers and educators, as it leads to gaze positions being measured imprecisely under unfavorable conditions.&#13;
&#13;
In our previous study, we systematically investigated factors that influence data quality and are easily controllable in a classroom or laboratory environment, such as illumination, sampling frequency, and head orientation. However, no recommendations regarding the light source and light orientation could be provided, as these influences could not be analyzed in sufficient detail. Yet, a further examination of these factors, eliminating human influences by using an artificial head, revealed significant differences between individual settings.&#13;
&#13;
Hence, in this empirical study of eye tracking as an educational technology, we delve deeper into examining the impact of both light source and light orientation on data quality. This is investigated with an artificial head together with the Tobii Pro Spectrum eye tracking device.&#13;
&#13;
To measure data quality, we use the metrics precision and standard deviation as indicators of data noise. The obtained results derive practical advice for educators and researchers, such as not to illuminate the subject from the rear, in order to gather useful data for research and future classroom applications.&#13;
&#13;
Thereby, this study serves as a complement to our previous research, answering open questions regarding best practices for researchers and educators when using eye trackers. It aims to provide valuable insights into producing data of the highest quality possible when using eye trackers, both in laboratory settings and in future classrooms applications.</abstract>
    <parentTitle language="eng">INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024</parentTitle>
    <identifier type="doi">10.21125/inted.2024.0802</identifier>
    <identifier type="isbn">978-84-09-59215-9</identifier>
    <identifier type="issn">2340-1079</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>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Susanne Staufer</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Eye tracking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data quality</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>best practices</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>lighting conditions</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>standard deviation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>precision</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>7117</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3005</pageFirst>
    <pageLast>3014</pageLast>
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    <title language="eng">EYE TRACKING METRICS FOR DISTINGUISHING GLOBAL AND FOCAL GAZE PATTERNS: A SYSTEMATIC LITERATURE REVIEW</title>
    <abstract language="eng">Global and focal eye tracking gaze patterns are distinguished in a variety of domains, such as radiology research, empirical software engineering, behavioral psychology, and cartography research. A global gaze pattern is present if a participant in an eye tracking study gains an overview of the stimulus, whereas a focal pattern emerges during a more detailed evaluation of a specific part of the stimulus.&#13;
&#13;
With this higher-level measure of global and focal gaze patterns, cognitive effort, expertise levels, and even neurological characteristics - such as autism - can be quantified. Furthermore, with this measure, cognitive efforts of students in certain tasks can be investigated and clues for targeted support can be provided. These properties in particular make this eye tracking measure valuable for the education domain.&#13;
&#13;
In the field of eye tracking research, many researchers have established distinct criteria for differentiating global and focal gaze patterns. Moreover, it is rarely quantitatively measured which of these patterns is present, but rather determined based on qualitative considerations. However, some studies explicitly use quantitative metrics like saccade-length or fixation-duration to distinguish between global and focal gaze patterns. This is the focus of the present systematic literature review: It searches for and compiles the eye tracking metrics used in the literature to distinguish global from focal gaze patterns in a quantitative way.&#13;
&#13;
In doing so, this study fills the yawning gap in quantitative metrics for determining global and focal gaze patterns. In the long run, this can be used to answer questions about cognitive load and problems in processing tasks during eye tracking studies and to provide further insights into the cognitive processes of students.&#13;
&#13;
The present paper first discusses definitions of global and focal gaze patterns and presents examples in which this metric has already been applied. Then, the research questions together with search strings and search engines used for the systematic literature review are described. Finally, the results are summarized, presenting a compilation of and connections between quantitative metrics utilized in the literature to distinguish between global and focal viewing patterns.</abstract>
    <parentTitle language="eng">INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024</parentTitle>
    <identifier type="doi">10.21125/inted.2024.0814</identifier>
    <identifier type="isbn">978-84-09-59215-9</identifier>
    <identifier type="issn">2340-1079</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>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Susanne Staufer</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Eye tracking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cognitive load</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>expertise</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>global</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>focal</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ambient</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>local</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>metrics</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>6768</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4500</pageFirst>
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    <title language="eng">Eye tracking al technology in education; data quality analysis and improvements</title>
    <abstract language="eng">Eye tracking has proven to be a powerful tool in a variety of empirical research areas; hence, it is steadily gaining attention. Driven by the expanding frontiers of Artificial Intelligence and its potential for data analysis, eye tracking technology offers promising applications in diverse fields, from usability research to cognitive research. The education sector in particular can benefit from the increased use of eye tracking technology - both indirectly, for example by studying the differences in gaze patterns between experts and novices to identify promising strategies, and directly by using the technology itself to teach in future classrooms.&#13;
&#13;
As with any empirical method, the results depend directly on the quality of the data collected. That raises the question of which parameters educators or researchers can influence to maximize the data quality of an eye tracker. This is the starting point of the present work: In an empirical study of eye tracking as an (educational) technology, we systematically examine factors that influence the data quality, such as illumination, sampling frequency, and head orientation - parameters that can be varied without much additional effort in everyday classroom or research use - using two human subjects, an artificial face, and the Tobii Pro Spectrum.&#13;
&#13;
We rely on metrics derived from the raw gaze data, such as accuracy or precision, to measure data quality. The obtained results derive practical advice for educators and researchers, such as using the lowest sampling frequency appropriate for a certain purpose. Thereby, this research fills a gap in the current understanding of eye tracker performance and, by offering best practices, enables researchers or teachers to produce data of the highest quality possible and therefore best results when using eye trackers in laboratories or future classrooms.</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.2023.1127</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>Timur Ezer</author>
    <author>Matthias Greiner</author>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>6087</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>130</pageFirst>
    <pageLast>137</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
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    <completedDate>--</completedDate>
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    <title language="eng">An Eye Tracking Study on MISRA C Coding Guidelines</title>
    <abstract language="eng">C is one of the most widely used programming languages - MISRA C is one of the most known sets of coding guidelines for C. This paper examines the usefulness and comprehensibility of the MISRA C:2012 guidelines in an eye tracking study. There, subjects encounter non-compliant code in four different code review settings: with no additional reference, with an actual MISRA C guideline, with a case-specific interpretation of a MISRA C guideline, and with a compliant version of the code. The data collected was analyzed not only in terms of the four presentation styles, but also by dividing the subjects into experience levels based on their semesters of study or years of work experience. Regarding the difference between actual and interpreted guidelines, we found that for interpreted guidelines the error detection rate is higher whereas the duration and frequency of visits to the guideline itself are mainly lower. This suggest that the actual guidelines are less useful and more difficult to understand. The former is contradicted by the subjects’ opinions: when surveyed, they rated the usefulness of the actual guidelines higher.</abstract>
    <parentTitle language="eng">ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023</parentTitle>
    <identifier type="doi">10.1145/3593663.3593671</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>Alexander Homann</author>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>1386</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <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>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Forschungsbericht 2015</title>
    <identifier type="isbn">978-3-00-048589-3</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-13867</identifier>
    <identifier type="doi">10.35096/othr/pub-1386</identifier>
    <author>Georg Scharfenberg</author>
    <author>Jürgen Mottok</author>
    <author>Christina Artmann</author>
    <author>Martin Hobelsberger</author>
    <author>Ivan Paric</author>
    <author>Benjamin Großmann</author>
    <author>Clemens Pohlt</author>
    <author>Alena Wackerbarth</author>
    <author>Uli Pausch</author>
    <author>Christiane Heidrich</author>
    <author>Martin Fadanelli</author>
    <author>Michael Elsner</author>
    <author>Daniel Pöcher</author>
    <author>Lenz Pittroff</author>
    <author>Stefan Beer</author>
    <author>Oliver Brückl</author>
    <author>Matthias Haslbeck</author>
    <author>Michael Sterner</author>
    <author>Martin Thema</author>
    <author>Nicole Muggenthaler</author>
    <author>Thorsten Lenck</author>
    <author>Philipp Götz</author>
    <author>Fabian Eckert</author>
    <author>Michael Deubzer</author>
    <author>Armin Stingl</author>
    <author>Erol Simsek</author>
    <author>Stefan Krämer</author>
    <author>Benjamin Großmann</author>
    <author>Thomas Schlegl</author>
    <author>Sascha Niedersteiner</author>
    <author>Thomas Berlehner</author>
    <author>Mitchell Joblin</author>
    <author>Wolfgang Mauerer</author>
    <author>Sven Apel</author>
    <author>Janet Siegmund</author>
    <author>Dirk Riehle</author>
    <author>Joachim Weber</author>
    <author>Christoph Palm</author>
    <author>Martin Zobel</author>
    <author>Ghassan Al-Falouji</author>
    <author>Dietmar Prestel</author>
    <author>Georg Scharfenberg</author>
    <author>Roland Mandl</author>
    <author>Arnulf Deinzer</author>
    <author>W. Halang</author>
    <author>Jutta Margraf-Stiksrud</author>
    <author>Bernhard Sick</author>
    <author>Renate Deinzer</author>
    <author>Stefanie Scherzinger</author>
    <author>Meike Klettke</author>
    <author>Uta Störl</author>
    <author>Katharina Wiech</author>
    <author>Christoph Kubata</author>
    <author>Dirk Sindersberger</author>
    <author>Gareth J. Monkman</author>
    <author>Markus Dollinger</author>
    <author>Sven Dembianny</author>
    <author>Andreas Kölbl</author>
    <author>Franz Welker</author>
    <author>Matthias Meier</author>
    <author>Philipp Thumann</author>
    <author>Krzysztof Swidergal</author>
    <author>Marcus Wagner</author>
    <author>Sonja Haug</author>
    <author>Matthias Vernim</author>
    <author>Barbara Seidenstücker</author>
    <author>Karsten Weber</author>
    <author>Christian Arsan</author>
    <author>Reinhold Schone</author>
    <author>Johannes Münder</author>
    <author>Irmgard Schroll-Decker</author>
    <author>Andrea Elisabeth Dillinger</author>
    <author>Rudolf Fuchshofer</author>
    <author>Gareth J. Monkman</author>
    <author>Mikhail Shamonin (Chamonine)</author>
    <author>Markus A. Geith</author>
    <author>Fabian Koch</author>
    <author>Christian Ühlin</author>
    <author>Thomas Schratzenstaller</author>
    <author>Sean Patrick Saßmannshausen</author>
    <author>Eberhard Auchter</author>
    <author>Willy Kriz</author>
    <author>Othmar Springer</author>
    <author>Maria Thumann</author>
    <author>Wolfgang Kusterle</author>
    <author>Andreas Obermeier</author>
    <author>Anton Udalzow</author>
    <author>Anton Schmailzl</author>
    <author>Stefan Hierl</author>
    <author>Christoph Langer</author>
    <author>Rupert Schreiner</author>
    <series>
      <title>Forschungsberichte der OTH Regensburg</title>
      <number>2015</number>
    </series>
    <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/1386/Forschungsbericht_2015.pdf</file>
  </doc>
  <doc>
    <id>799</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>80</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>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Forschungsbericht 2013</title>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-7990</identifier>
    <identifier type="doi">10.35096/othr/pub-799</identifier>
    <author>Josef Beimler</author>
    <author>Caroline Leißl</author>
    <author>Lena Ebner</author>
    <author>Michael Elsner</author>
    <author>Gerhard Mühlbauer</author>
    <author>Dieter Kohlert</author>
    <author>Martin J. W. Schubert</author>
    <author>Andreas P. Weiß</author>
    <author>Michael Sterner</author>
    <author>Thomas Raith</author>
    <author>Martin Afranseder</author>
    <author>Tobias Krapf</author>
    <author>Jürgen Mottok</author>
    <author>Christian Siemers</author>
    <author>Benjamin Großmann</author>
    <author>Johannes Höcherl</author>
    <author>Thomas Schlegl</author>
    <author>Ralph Schneider</author>
    <author>Johannes Milaev</author>
    <author>Christina Rampelt</author>
    <author>Christian Roduner</author>
    <author>Christoph Glowa</author>
    <author>Christoph Bachl</author>
    <author>Claus Schliekmann</author>
    <author>Alfons Gnan</author>
    <author>Martin Grill</author>
    <author>Karl Ruhland</author>
    <author>Thomas Piehler</author>
    <author>Daniel Friers</author>
    <author>Harald Wels</author>
    <author>Kenny Pflug</author>
    <author>Markus Kucera</author>
    <author>Thomas Waas</author>
    <author>Felix Schlachetzki</author>
    <author>Sandra Boy</author>
    <author>Josef Pemmerl</author>
    <author>Alexander Leis</author>
    <author>Andreas F.X. Welsch</author>
    <author>Franz Graf</author>
    <author>Gerhard Zenger</author>
    <author>Klaus Volbert</author>
    <author>Thomas Waas</author>
    <author>Stefanie Scherzinger</author>
    <author>Meike Klettke</author>
    <author>Uta Störl</author>
    <author>C. Heyl</author>
    <author>A. Boldenko</author>
    <author>Gareth J. Monkman</author>
    <author>Richard Kujat</author>
    <author>Ulrich Briem</author>
    <author>Stefan Hierl</author>
    <author>Sebastian Talbot</author>
    <author>Anton Schmailzl</author>
    <author>Robert Damian Ławrowski</author>
    <author>Christian Prommesberger</author>
    <author>Christoph Langer</author>
    <author>Florian Dams</author>
    <author>Rupert Schreiner</author>
    <author>Piergiorgio Valentino</author>
    <author>Marco Romano</author>
    <author>Ingo Ehrlich</author>
    <author>Franco Furgiuele</author>
    <author>Norbert Gebbeken</author>
    <author>Michael Eisenried</author>
    <author>Bastian Jungbauer</author>
    <author>Albert Hutterer</author>
    <author>Michael Bauhuber</author>
    <author>Andreas Mikrievskij</author>
    <author>Monika Argauer</author>
    <author>Helmut Hummel</author>
    <author>Alfred Lechner</author>
    <author>Thomas Liebetruth</author>
    <author>Michael Schumm</author>
    <author>Saskia Joseph</author>
    <author>Michael Reschke</author>
    <author>Alexander Soska</author>
    <author>Irmgard Schroll-Decker</author>
    <author>Michael Putzer</author>
    <author>John Rasmussen</author>
    <author>Sebastian Dendorfer</author>
    <author>Tim Weber</author>
    <author>Amir Andreas Al-Munajjed</author>
    <author>Gijsbertus Jacob Verkerke</author>
    <author>Tobias Renkawitz</author>
    <author>Sonja Haug</author>
    <author>Clarissa Rudolph</author>
    <author>Annika Zeitler</author>
    <author>Simon Schaubeck</author>
    <author>Oliver Steffens</author>
    <author>Christian Rechenauer</author>
    <author>Thekla Schulz-Brize</author>
    <author>Florian Fleischmann</author>
    <author>Wolfgang Kusterle</author>
    <author>Anne Beer</author>
    <author>Bernd Wagner</author>
    <author>Thomas Neidhart</author>
    <series>
      <title>Forschungsberichte der OTH Regensburg</title>
      <number>2013</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>
    <collection role="persons" number="sternerfenes">Sterner, Michael (Prof. Dr.) - FENES / Forschungsgruppe Energiespeicher</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/799/Forschungsbericht_2013.pdf</file>
  </doc>
  <doc>
    <id>789</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>72</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>periodicalpart</type>
    <publisherName/>
    <publisherPlace>Regensburg</publisherPlace>
    <creatingCorporation>Ostbayerische Technische Hochschule Regensburg</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-06-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Forschung 2019</title>
    <abstract language="deu">Bericht mit Forschungsprojekten aus verschiedenen Bereichen der OTH Regensburg mit dem Schwerpunktthema "Künstliche Intelligenz" und einem Gespräch zur "Medizin der Zukunft"</abstract>
    <subTitle language="deu">Thema: Künstliche Intelligenz</subTitle>
    <identifier type="isbn">978-3-9818209-7-3</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-7890</identifier>
    <identifier type="doi">10.35096/othr/pub-789</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Marie-Luise Appelhans</author>
    <author>Matthias Kampmann</author>
    <author>Jürgen Mottok</author>
    <author>Michael Riederer</author>
    <author>Klaus Nagl</author>
    <author>Oliver Steffens</author>
    <author>Jan Dünnweber</author>
    <author>Markus Wildgruber</author>
    <author>Julius Roth</author>
    <author>Timo Stadler</author>
    <author>Christoph Palm</author>
    <author>Martin Georg Weiß</author>
    <author>Sandra Rochholz</author>
    <author>Rudolf Bierl</author>
    <author>Andreas Gschossmann</author>
    <author>Sonja Haug</author>
    <author>Simon Schmidbauer</author>
    <author>Anna Koch</author>
    <author>Markus Westner</author>
    <author>Benedikt von Bary</author>
    <author>Andreas Ellermeier</author>
    <author>Daniel Vögele</author>
    <author>Frederik Maiwald</author>
    <author>Stefan Hierl</author>
    <author>Matthias Schlamp</author>
    <author>Ingo Ehrlich</author>
    <author>Marco Siegl</author>
    <author>Sven Hüntelmann</author>
    <author>Matthias Wildfeuer</author>
    <author>Oliver Brückl</author>
    <author>Michael Sterner</author>
    <author>Andreas Hofrichter</author>
    <author>Fabian Eckert</author>
    <author>Franz Bauer</author>
    <author>Belal Dawoud</author>
    <author>Hans-Peter Rabl</author>
    <author>Bernd Gamisch</author>
    <author>Ottfried Schmidt</author>
    <author>Michael Heberl</author>
    <author>Martin Thema</author>
    <author>Ulrike Mayer</author>
    <author>Johannes Eller</author>
    <author>Thomas Sippenauer</author>
    <author>Christian Adelt</author>
    <author>Matthias Haslbeck</author>
    <author>Bettina Vogl</author>
    <author>Wolfgang Mauerer</author>
    <author>Ralf Ramsauer</author>
    <author>Daniel Lohmann</author>
    <author>Irmengard Sax</author>
    <author>Thomas Gabor</author>
    <author>Sebastian Feld</author>
    <author>Claudia Linnhoff-Popien</author>
    <author>Robert Damian Ławrowski</author>
    <author>Christoph Langer</author>
    <author>Rupert Schreiner</author>
    <author>Josef Sellmair</author>
    <series>
      <title>Forschungsberichte der OTH Regensburg</title>
      <number>2019</number>
    </series>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Forschung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Forschungsbericht</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <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>
    <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>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/789/Forschungsbericht_2019.pdf</file>
  </doc>
  <doc>
    <id>1224</id>
    <completedYear/>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>166</pageFirst>
    <pageLast>169</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Shaker</publisherName>
    <publisherPlace>Aachen</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-02-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A gesture-based human-machine interface using stereoscopic depth images</title>
    <abstract language="eng">Reasonable integration of gesture-based automotive HMI-functionality offers potential safety benefits by reducing driver distractions and glance times to operate tertiary in-car devices. Stereo camera systems are a well investigated choice to perform the task of generating depth data for spatial gesture recognition. This paper describes the functionality of our stereo vision software, which is intended for application in a target system based on CMOS wafer-level cameras. The retrieved point cloud data was passed to a gesture-based sample application.</abstract>
    <parentTitle language="eng">2nd Applied Research Conference 2012 (ARC 2012), 25./26. June 2012, Nuremberg</parentTitle>
    <enrichment key="opus.source">publish</enrichment>
    <author>T. Bock</author>
    <author>Jürgen Mottok</author>
    <author>Thorsten Köhler</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>5577</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>17</pageFirst>
    <pageLast>30</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-11-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">BayesianSafety - an Open-Source Package for Causality-Guided, Multi-Model Safety Analysis</title>
    <abstract language="eng">Development and verification of modern, dependable automotive systems require appropriate modelling approaches. Classic automotive safety is described by the normative regulations ISO 26262, its relative ISO/PAS 21448, and their respective methodologies. In recent publications, an emerging demand to combine environmental influences, machine learning, or reasoning under uncertainty with standard-compliant analysis techniques can be noticed. Therefore, adapting established methods like FTA and proper tool support is necessary. We argue that Bayesian Networks (BNs) can be used as a central component to address and merge these demands. In this paper, we present our Open-Source Python package BayesianSafety. First, we review how BNs relate to data-driven methods, model-to-model transformations, and causal reasoning. Together with FTA and ETA, these models form the core functionality of our software. After describing currently implemented features and possibilities of combining individual modelling approaches, we provide an informal view of the tool’s architecture and of the resulting software ecosystem. By comparing selected publicly available safety and reliability analysis libraries, we outline that many relevant methodologies yield specialized implementations. Finally, we show that there is a demand for a flexible, unifying analysis tool that allows researching system safety by using multi-model and multi-domain approaches.</abstract>
    <parentTitle language="eng">Computer Safety, Reliability, and Security, 41st International Conference (SAFECOMP 2022): Munich, Germany, September 6–9, 2022, Proceedings</parentTitle>
    <parentTitle language="mul">Lecture notes in computer science ; 13414</parentTitle>
    <identifier type="doi">10.1007/978-3-031-14835-4_2</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Robert Maier</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Package BayesianSafety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Causality</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian Networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Event Tree Analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fault Tree Analysis</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>5578</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>116</pageFirst>
    <pageLast>129</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-11-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <enrichment key="opus.source">publish</enrichment>
    <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>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <enrichment key="opus.import.date">2023-10-16T20:23:06+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>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/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
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    <completedDate>2022-11-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
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    <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>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>1383</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <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>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
  </doc>
  <doc>
    <id>6772</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3121</pageFirst>
    <pageLast>3130</pageLast>
    <pageNumber/>
    <edition/>
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    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <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/>
    <edition/>
    <issue/>
    <volume/>
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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>
  </doc>
  <doc>
    <id>8340</id>
    <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/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
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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>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <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>3376</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>report</type>
    <publisherName/>
    <publisherPlace>Warrendale, Pennsylvania, USA</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation>Society of Automotive Engineers</contributingCorporation>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
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    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <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>
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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>
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  <doc>
    <id>3047</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>291</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <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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    <contributingCorporation/>
    <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/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <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>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>
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    <publisherName>ACM</publisherName>
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    <completedDate>--</completedDate>
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    <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>
    <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>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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    <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>
    <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>Vamsi Krishna Nadimpalli</author>
    <author>Flemming Bugert</author>
    <author>Dominik Bittner</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Susanne Staufer</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>7897</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4056</pageFirst>
    <pageLast>4065</pageLast>
    <pageNumber/>
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    <publisherName>IATED</publisherName>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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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>
    <enrichment key="opus.source">sword</enrichment>
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    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <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>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <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>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Forschung 2018</title>
    <identifier type="isbn">978-3-9818209-5-9</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-13826</identifier>
    <identifier type="doi">10.35096/othr/pub-1382</identifier>
    <author>Christian Broser</author>
    <author>Thomas Falter</author>
    <author>Robert Damian Ławrowski</author>
    <author>Amelie Altenbuchner</author>
    <author>Daniel Vögele</author>
    <author>Claus Koss</author>
    <author>Matthias Schlamp</author>
    <author>Jan Dunnweber</author>
    <author>Oliver Steffens</author>
    <author>Markus Heckner</author>
    <author>Sabine Jaritz</author>
    <author>Thomas Schiegl</author>
    <author>Sabine Corsten</author>
    <author>Norina Lauer</author>
    <author>Katherine Guertler</author>
    <author>Eric Koenig</author>
    <author>Sonja Haug</author>
    <author>Dominik Huber</author>
    <author>Clemens Birkenmaier</author>
    <author>Lars Krenkel</author>
    <author>Thomas Wagner</author>
    <author>Xenia Justus</author>
    <author>Sean Patrick Saßmannshausen</author>
    <author>Nadine Kleine</author>
    <author>Karsten Weber</author>
    <author>Carina N. Braun</author>
    <author>Giuliano Giacoppo</author>
    <author>Michael Heinrich</author>
    <author>Tobias Just</author>
    <author>Thomas Schreck</author>
    <author>Andreas Schnabl</author>
    <author>Amador Téran Gilmore</author>
    <author>Samuel Roeslin</author>
    <author>Sandra Schmid</author>
    <author>Felix Wellnitz</author>
    <author>Sebastian Malz</author>
    <author>Andreas Maurial</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <author>Meike Klettke</author>
    <author>Stefanie Scherzinger</author>
    <author>Uta Störl</author>
    <author>Markus Heckner</author>
    <author>Alexander Bazo</author>
    <author>Christian Wolff</author>
    <author>Andreas Kopper</author>
    <author>Markus Westner</author>
    <author>Christian Pongratz</author>
    <author>Ingo Ehrlich</author>
    <author>Ulrich Briem</author>
    <author>Sebastian Hederer</author>
    <author>Marcus Wagner</author>
    <author>Moritz Schillinger</author>
    <author>Julien Görlach</author>
    <author>Stefan Hierl</author>
    <author>Marco Siegl</author>
    <author>Christoph Langer</author>
    <author>Matthias Hausladen</author>
    <author>Rupert Schreiner</author>
    <author>Matthias Haslbeck</author>
    <author>Reinhard Kreuzer</author>
    <author>Oliver Brückl</author>
    <author>Belal Dawoud</author>
    <author>Hans-Peter Rabl</author>
    <author>Bernd Gamisch</author>
    <author>Ottfried Schmidt</author>
    <author>Michael Heberl</author>
    <author>Bianca Gänsbauer</author>
    <author>Werner Bick</author>
    <author>Andreas Ellermeier</author>
    <author>Gareth J. Monkman</author>
    <author>Nina Prem</author>
    <author>Dirk Sindersberger</author>
    <author>Karl Tschurtschenthaler</author>
    <author>Maximilian Aurbach</author>
    <author>Sebastian Dendorfer</author>
    <author>Michael A. Betz</author>
    <author>Tamara Szecsey</author>
    <author>Wolfgang Mauerer</author>
    <author>Florian Murr</author>
    <series>
      <title>Forschungsberichte der OTH Regensburg</title>
      <number>2018</number>
    </series>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Forschung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Forschungsbericht</value>
    </subject>
    <collection role="institutes" number="HL">Hochschulleitung/Hochschulverwaltung</collection>
    <collection role="othpublikationsherkunft" number="">Von der OTH Regensburg herausgegeben</collection>
    <collection role="persons" number="weberlate">Weber, Karsten (Prof. Dr.) - Labor für Technikfolgenabschätzung und Angewandte Ethik</collection>
    <collection role="institutes" number="IAFW">Zentrum für Forschung und Transfer (ZFT ab 2024; vorher: IAFW)</collection>
    <collection role="persons" number="bruecklfenes">Brückl, Oliver (Prof. Dr.) - FENES / Forschungsgruppe Energienetze</collection>
    <collection role="persons" number="hauglasofo">Haug, Sonja (Prof. Dr.) - Labor Empirische Sozialforschung</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/1382/Forschung-OTHR_2018.pdf</file>
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  <doc>
    <id>7909</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
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    <type>preprint</type>
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    <title language="eng">Underreliance Harms Human-AI Collaboration More Than Overreliance in Medical Imaging</title>
    <abstract language="deu">Importance: The use of artificial intelligence (AI) to support clinicians in diagnostic decision-making holds significant potential; however, evidence regarding its clinical utility remains mixed. In many cases, the interaction between healthcare professionals and AI systems does not improve collaborative performance compared to the standalone performance of humans or AI. Currently, the underlying mechanisms that limit human-AI collaboration are poorly understood. Objective: To examine the impact of AI advice on diagnostic decision-making among experts and novices, focusing on understanding the role of explainability (XAI) on users’ reliance on advice. Design, Setting, and Participants: A mixed-methods design combining a crossover experimental design with a think-aloud and an eye-tracking study arm was conducted in 2023. Participants were task experts (radiologists) and novices (non-radiologist physicians and medical trainees) from 10 countries, with the think-aloud and eye-tracking conducted in Germany. Intervention: Participants reviewed 50 patient cases containing head CT scans and patient information. Every case was reviewed in three time-separate sessions in randomized order. In each session, participants were exposed to a different experimental condition: (a) control, i.e., no AI prediction presented; (b) basic advice, i.e., AI prediction without annotations; and (c) XAI advice, i.e., AI prediction with annotations. For each case, participants had to determine if the patients had an intracranial hemorrhage (ICH), rate their confidence, and, if applicable, the usefulness of the AI advice. Main Outcome(s) and Measure(s): Diagnostic performance, confidence in the diagnosis, case reading time, and AI advice usefulness ratings. Results: The data analysis included 125 participants. The mean age was 28.5 years (SD = 6.72), and 55.2% identified as female. Underreliance on correct AI advice was associated with high uncertainty and had a more detrimental impact on diagnostic performance than overreliance on incorrect advice. XAI advice reduced underreliance and improved performance and confidence, particularly when reviewing more difficult cases with ICH. AI advice, particularly XAI, did not reduce reading time. XAI was perceived as more useful than basic AI advice, especially among novices. Conclusions and Relevance: Our findings indicate that underreliance on AI might be more harmful than overreliance, highlighting the need to develop efficient counterstrategies beyond current XAI methods.</abstract>
    <identifier type="doi">10.31219/osf.io/4wv8j</identifier>
    <enrichment key="opus.import.date">2025-01-20T09:16:32+00:00</enrichment>
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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>
  <doc>
    <id>8304</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>106</pageFirst>
    <pageLast>114</pageLast>
    <pageNumber/>
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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>
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    <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>
    <pageFirst/>
    <pageLast/>
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    <type>periodicalpart</type>
    <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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    <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>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Florian Hauser</author>
    <author>Susanne Staufer</author>
    <author>Lisa Grabinger</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
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    <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>
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    <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>
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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>
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    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>10</pageLast>
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    <title language="eng">Probabilistic Machine Learning for Simulating Complex Learner Profiles</title>
    <abstract language="eng">Data is the foundation of all machine learning applications. In education science, especially for the learner characteristics that drive personalized learning, it is difficult to collect and often uncertain. It is challenging to model, train, evaluate, and analyze the underlying algorithms when developing AI-based systems and having small sample sizes. To address these problems, we present a synthetic data generator utilising probabilistic models. This generator can effectively model and simulate complex learner profiles. To achieve this, we collected extensive data on learning styles, learning strategies, personalities, and preferred learning paths from 593 students over several semesters at a higher education level. Then, Bayesian networks, Hidden Markov Models, and Markov Chains are used to model the relationships between learner profiles. Using the Bayesian information criterion, and cross-validation with log-likelihood scores, we compare various models to select the best fitting one for synthesizing the data. The synthetic data is then evaluated using statistical validation techniques. In addition, we developed a simulation module with the option to simulate learner profiles based on manual user-defined inputs. The data and code used in this work are available as open source1110.5281/zenodo.13768136 [Titel anhand dieser DOI in Citavi-Projekt übernehmen] contributing to open science and developers for customized simulated data. In the future, this data will refine the training, evaluation, analysis, and benchmarking of algorithms for personalized learning.</abstract>
    <parentTitle language="eng">2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), 06-08 November 2024, Paris, France</parentTitle>
    <identifier type="doi">10.1109/ITHET61869.2024.10837641</identifier>
    <identifier type="isbn">979-8-3315-1663-5</identifier>
    <identifier type="issn">2473-2060</identifier>
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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>Susanne Staufer</author>
    <author>Simon Röhrl</author>
    <author>Florian Hauser</author>
    <author>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Robert Maier</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>probabilistic models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>synthetic data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learner characteristics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>generative AI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>higher education area</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
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  </doc>
  <doc>
    <id>7901</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>6005</pageFirst>
    <pageLast>6014</pageLast>
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    <title language="eng">Predicting learner characteristics using machine learning</title>
    <abstract language="eng">In education science research, data collection is challenging due to difficulty identifying students at the higher education level, privacy concerns, and varying levels of student engagement. Importantly, psychological questionnaires can be lengthy, leading to incomplete responses. We conducted repeated studies, and over time, the focus of the research adapted, introducing new materials and consequently leading to missing learner characteristics in some datasets.&#13;
&#13;
In this research, the issue of incomplete learner characteristics is addressed using data from three different studies: winter term 2022/2023 (n=297), summer term 2023 (n=274), and winter term 2023/2024 (n=25). These studies collected various learner characteristics, such as learning styles, personalities, learning strategies, and learning element preferences. However, learning element preferences and learning strategies were missing in the winter term of 2022, and the summer term of 2023 respectively. To analyze the data and predict these missing features, statistical analysis, and machine learning techniques were employed. Then, these models are rigorously evaluated using cross-validation and performance metrics like accuracy, precision, recall, and F1-score. Our findings provide insights into the relationships between learners' learning styles, personalities, learning strategies, and learning element preferences. This offers valuable implications for the design and implementation of educational interventions, like learning path recommendations. The results imply that machine learning models can predict missing learner characteristics, thus addressing the problem of incomplete data in educational research</abstract>
    <parentTitle language="eng">ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain</parentTitle>
    <identifier type="doi">10.21125/iceri.2024.1454</identifier>
    <enrichment key="opus.import.date">2025-01-20T09:16:32+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Susanne Staufer</author>
    <author>Timur Ezer</author>
    <author>Flemming Bugert</author>
    <author>Dominik Bittner</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Simon Röhrl</author>
    <author>Robert Maier</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>8655</id>
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    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <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>
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    <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>
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    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
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  <doc>
    <id>7114</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4195</pageFirst>
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    <title language="eng">Tyche algorithm: Markov models for generating learning paths in learning management systems</title>
    <abstract language="eng">In the intricate tapestry of the cosmos, where celestial threads weave stories of destiny, an enigmatic deity stands at the intersection of chance and fate — Tyche, the goddess of fortune. In science and probability, coincidence plays a distinctive role in Bayesian Networks (BNs) and Markov Models (MMs). This paper introduces the Tyche algorithm named after the goddess of fortune. The Tyche algorithm is a Markov model designed to generate learning paths in Learning Management Systems (LMSs). A learning path is a type of individualization that personalises the order of learning elements within an LMS course. Learning elements are fundamental components within an LMS course, depicting the learning content in diverse ways. In our case, the learning path tailoring is based on the learner’s learning style according to Felder-Silverman Learning Style Model (FSLSM) – an indicator for the ideal pathway and learning element for the learner’s optimum learning.&#13;
&#13;
The Tyche algorithm offers the advantage to provide students the most suitable learning path. Tyche is a MM structure with various matrices containing transition probabilities depending on the learning style. Nine categories of learning elements defined in a previous survey form the basis of the node structure of the MM. For a generic approach, a survey was designed to obtain the transition probabilities depending on the individual learning style. The survey with more than 100 German students participated is processed with the tool LimeSurvey. Students are asked about their learning style using the Index of Learning Styles (ILS) questionnaire according to Felder-Silverman and about the percentage probabilities of learning elements to get their individual sequence as learning path. The percentages are queried in two different ways. Firstly, the students were asked to provide a sequence of learning elements within each position of the sequence filled with probabilities for all learning elements. Secondly, a learning element is given and the students are asked to indicate which learning element they would work on next. The first way of asking for the probabilities is used to find the start node in the MM, whereas the second approach forms the probability matrices between the nodes within the MM.&#13;
&#13;
As result of the survey, the Tyche algorithm presents generic transition probabilities. It improves the learning process of individuals only by asking for their learning style: it generates individual learning paths through the learning elements within an LMS based on the MM explained above by solely getting the answers of the ILS questionnaire as input. In the future, other questionnaires such as BFI-10 for personality traits or LIST-K for learning strategies may offer a more comprehensive input. However, the next step is to evaluate Tyche with about 25 students in a software engineering lecture. This is planned for the year 2024.</abstract>
    <parentTitle language="eng">INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024</parentTitle>
    <identifier type="doi">10.21125/inted.2024.1080</identifier>
    <identifier type="isbn">978-84-09-59215-9</identifier>
    <identifier type="issn">2340-1079</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-71148</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Susanne Staufer</author>
    <author>Flemming Bugert</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Timur Ezer</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Dominik Bittner</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Markov model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning style</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning elements</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning management system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>higher education area</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7114/STAUFER2024TYC.pdf</file>
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  <doc>
    <id>8784</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2562</pageFirst>
    <pageLast>2572</pageLast>
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    <edition/>
    <issue/>
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    <title language="eng">TYCHE ALGORITHM 2.0: Learning Paths from Questionnaire Responses and Learning Analytics</title>
    <abstract language="eng">The digitalization of learning processes has increased the need for adaptive learning paths tailored individually to learners. A novel algorithm for learning path generation is presented in this paper, namely Tyche 2.0. It extends the original Tyche approach after Staufer et al. – a Markov model for generating learning paths – by integrating additional learner data beyond learning styles (Index of Learning Styles (ILS)), including learning strategies (LIST-K questionnaire), personality traits (BFI-10 questionnaire), and learning analytics captured through screen recordings. In order to be able to use the screen recordings, a heuristic evaluates them. Furthermore, this enhanced algorithm employs Markov models to dynamically generate personalized learning paths. These are based on both questionnaire responses and real-time&#13;
engagement data, the weights of which undergo dynamic adjustment over time. We made a small evaluation of Tyche 2.0 without the learning analytics influence, which shows that there is room for further improvements. Future research will focus on evaluating whole Tyche 2.0 in another university setting to further improve personalization and user engagement.</abstract>
    <parentTitle language="eng">ICERI2025 Proceedings</parentTitle>
    <identifier type="doi">10.21125/iceri.2025.0831</identifier>
    <identifier type="isbn">978-84-09-78706-7</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-87846</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="ConferenceStatement">18th annual International Conference of Education, Research and Innovation, 10.11.2025 - 11.11.2025, Seville, Spain</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Susanne Staufer</author>
    <author>Timur Ezer</author>
    <author>Simon Röhrl</author>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Erika Antoni</author>
    <author>Jürgen Mottok</author>
    <author>Josefa Schaffer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Markov model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning paths</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning style</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning strategy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Personality traits</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning analytics</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/8784/STAUFER2025TYC.pdf</file>
  </doc>
  <doc>
    <id>8804</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1775</pageFirst>
    <pageLast>1784</pageLast>
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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>
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    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
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    <publishedYear>2025</publishedYear>
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    <language>eng</language>
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    <title language="eng">EYE TRACKING GLASSES IN EDUCATIONAL SETTINGS: GUIDELINES ON DATA QUALITY</title>
    <abstract language="eng">Eye movement modeling examples, so-called EMME videos, are a valuable tool in education, helping learners better understand instructional content. Like conventional educational videos, EMME videos combine text, images, and voiceovers. However, they also display the instructor’s or teacher’s gaze, guiding learners attention to key elements. Although various approaches exist for creating EMME videos, there is currently no standardised guideline for ensuring gaze data quality. Eye tracking technology is essential to capture gaze behaviour, and in educational settings without a fixed computer monitor – such as when using blackboards or conducting live experiments – the usage of mobile eye tracking glasses is beneficial.&#13;
&#13;
An accuracy study is conducted using mobile eye tracking glasses to provide empirical guidance for the development of high-quality educational EMME videos and ensure that the instructor’s or teacher’s gaze is captured with high precision. The study uses the Tobii Pro Glasses 3 and involves a static and a dynamic setup with 34 participants. To gain insight into the effects of visual impairments on accuracy, we also include participants who wear contact lenses. In the static setup, participants are seated at a desk with a headrest and focus on a poster with nine fixation points. In the dynamic setup, participants are walking in a controlled half-circle around the poster while maintaining focus on its centre. Each setup is performed multiple times under varying lighting levels (300 lux, 700 lux) and distances between participant and poster (80 cm, 120 cm, 180 cm). This enables the simulation of diverse educational environments, including the possibility of a teacher’s or instructor’s movement.&#13;
&#13;
The study results will be evaluated regarding lighting conditions, the distance between the person wearing eye tracking glasses and the object, and possible influences of contact lenses. Based on these findings, favourable conditions for creating EMME videos in educational settings are collected, especially when working without a fixed computer monitor. The results address the outlined research gap by providing instructors and teachers with guidelines enabling them to produce high-quality educational EMME videos.</abstract>
    <parentTitle language="eng">ICERI2025 Proceedings</parentTitle>
    <identifier type="doi">10.21125/iceri.2025.1419</identifier>
    <enrichment key="opus.import.date">2026-01-12T13:41:50+00:00</enrichment>
    <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>
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    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <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>
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