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    <publishedYear>2024</publishedYear>
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    <title language="eng">Problems and New Approaches for Crypto-Agility in Operational Technology</title>
    <abstract language="eng">In recent years, cybersecurity has also become relevant for Operational Technology (OT). Critical systems like industrial automation systems or transportation systems are faced with new threats, and therefore require the implementation of thorough security measures. Regulations further mandate the deployment and regular verification of these security measures. However, OT systems differ from well-known systems of classic Information Technology (IT), such as mission times spanning decades, infrequent updates only during on-site maintenance, or diverse devices with varying support for security measures. The growing field of crypto-agility examines approaches to integrate security measures in an agile and flexible way, making updates easier and, therefore, encouraging a more frequent deployment of them. This paper contributes to this research field in the context of secure communication in two ways. We first examine the current state of crypto-agility by providing an overview of existing measures for OT systems. Then, we propose a new architecture concept with different deployment approaches to integrate security measures in a crypto-agile way. Based on a security library with a generic interface and a flexible proxy application, our architecture is capable of securing both new OT systems and existing ones via retrofit.</abstract>
    <parentTitle language="eng">12th European Congress Embedded Real Time Systems (ERTS 2024), 11-12 June 2024, Toulouse, France</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/383709526_Problems_and_New_Approaches_for_Crypto-Agility_in_Operational_Technology</identifier>
    <enrichment key="opus.import.date">2024-09-08T09:13:13+00:00</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <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>
  </doc>
  <doc>
    <id>7345</id>
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    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
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    <publisherName>IEEE</publisherName>
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    <title language="eng">Ariadne's Thread for Unravelling Learning Paths: Identifying Learning Styles via Hidden Markov Models</title>
    <abstract language="eng">Modern education through Learning Management Systems (LMSs) provides learners with personalized learning paths. This is achieved by first querying the learning style according to the theory of Felder and Silverman to recommend suitable learning content. However, a rigid learning style representation is lacking of adaptability to the learners' choices. Therefore, the present study evaluates the idea of providing adaption to the representation of learning styles by using Hidden Markov Models (HMMs). Thus, data is collected from participants out of the Higher Education Area. The Index of Learning Styles questionnaire is used to obtain the learning style based on the theory of Felder and Silverman. Also, a questionnaire that asks the respondents to create a preferred learning path with the sequence length of nine learning elements is provided. From the given data, we initially evaluate the probability relationships between learning styles and learning elements. Then, we use the Viterbi algorithm in HMMs to identify alterations in learning styles from the provided learning paths. The alignment is then quantified by introducing a metric called support value. The findings imply that our concept can be used to adapt the learning style based on the user's real choice of learning elements. Thus, the proposed model also offers a way to integrate a feedback loop within LMSs leading to an improvement of learning path recommendation algorithms.</abstract>
    <parentTitle language="eng">2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece</parentTitle>
    <identifier type="issn">2165-9567</identifier>
    <identifier type="doi">10.1109/EDUCON60312.2024.10578825</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Flemming Bugert</author>
    <author>Susanne Staufer</author>
    <author>Dominik Bittner</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Timur Ezer</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Surveys</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Knowledge engineering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Feedback loop</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning management systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Uncertainty</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Viterbi algorithm</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>7347</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>9089</pageFirst>
    <pageLast>9098</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IATED</publisherName>
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    <title language="eng">EVALUATION OF THE LEARNING MANAGEMENT SYSTEM PYTHIA</title>
    <abstract language="eng">Learning management systems gain importance due to the Corona pandemic. To personalize such a learning management system, a Moodle adaption named Pythia was created. Pythia is implemented as a Moodle plugin solution. The possibility of generating learning paths with various algorithms and the change of the graphical user interface is significant. Learning paths are generated with two different algorithms. Nestor algorithm generates learning paths with a Bayesian network, while Tyche algorithm uses a Markov model. The graphical user interface is adapted to the generated learning paths where learning element symbols are customized and the individual learning paths are depicted.&#13;
&#13;
This paper presents the results of a qualitative survey among German students with the aim of evaluating the first version of the learning management system Pythia. 25 students took the subject “Software Engineering for Safe and Secure Systems” in the winter term of 2023/24. The 68 asked questions focus on usage behavior, graphical user interface, usability, preferences, volume, and quality of learning elements, and learning paths. Our Moodle course for the lecture contains seven sections, each section has minimum one subsection on which the learning path is calculated. Last three sections are designed by presenting minimum one learning element of each learning element category except the collaboration tool. In a previous work, ten learning element categories were designed and evaluated comprising manuscript, brief overview, learning goal, quiz, exercise, summary, three types of additional material, and collaboration tool. The first sections are left as they are initially designed by the lecturer (normal section). The survey considers the difference between normal sections and diverse sections. Furthermore, the generated learning paths are evaluated. 13 students undertook Nestor learning paths, whereas 12 undertook Tyche learning paths.&#13;
&#13;
The results suggest that for the majority of students, the learning element symbols are well-chosen. Moreover, all students wish the elements relevant to the exam to be labelled. 23 students are overwhelmed by the number of learning elements in the last sections of the Moodle course. In the future, some suggestions will be implemented such as the labelling of exam relevant learning elements. To overcome the overload of learning elements, a strategy could be developed like hiding learning elements not preferred by the learning path algorithm.</abstract>
    <parentTitle language="eng">EDULEARN Proceedings: 16th International Conference on Education and New Learning Technologies, Palma, Spain. 1-3 July, 2024</parentTitle>
    <identifier type="issn">2340-1117</identifier>
    <identifier type="doi">10.21125/edulearn.2024.2189</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-73474</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Susanne Staufer</author>
    <author>Florian Hauser</author>
    <author>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Simon Röhrl</author>
    <author>Flemming Bugert</author>
    <author>Dominik Bittner</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning management system (LMS)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>questionnaire (study)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning elements</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning paths</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>usability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>graphical user interface (GUI)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Moodle</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>higher education area (HEA)</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/7347/STAUFER2024EVA.pdf</file>
  </doc>
  <doc>
    <id>7133</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2871</pageFirst>
    <pageLast>2880</pageLast>
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    <issue/>
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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>7132</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2955</pageFirst>
    <pageLast>2961</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IATED Academy</publisherName>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IATED Academy</publisherName>
    <publisherPlace/>
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    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
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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>7114</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4195</pageFirst>
    <pageLast>4205</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IATED Academy</publisherName>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">Tyche algorithm: Markov models for generating learning paths in learning management systems</title>
    <abstract language="eng">In the intricate tapestry of the cosmos, where celestial threads weave stories of destiny, an enigmatic deity stands at the intersection of chance and fate — Tyche, the goddess of fortune. In science and probability, coincidence plays a distinctive role in Bayesian Networks (BNs) and Markov Models (MMs). This paper introduces the Tyche algorithm named after the goddess of fortune. The Tyche algorithm is a Markov model designed to generate learning paths in Learning Management Systems (LMSs). A learning path is a type of individualization that personalises the order of learning elements within an LMS course. Learning elements are fundamental components within an LMS course, depicting the learning content in diverse ways. In our case, the learning path tailoring is based on the learner’s learning style according to Felder-Silverman Learning Style Model (FSLSM) – an indicator for the ideal pathway and learning element for the learner’s optimum learning.&#13;
&#13;
The Tyche algorithm offers the advantage to provide students the most suitable learning path. Tyche is a MM structure with various matrices containing transition probabilities depending on the learning style. Nine categories of learning elements defined in a previous survey form the basis of the node structure of the MM. For a generic approach, a survey was designed to obtain the transition probabilities depending on the individual learning style. The survey with more than 100 German students participated is processed with the tool LimeSurvey. Students are asked about their learning style using the Index of Learning Styles (ILS) questionnaire according to Felder-Silverman and about the percentage probabilities of learning elements to get their individual sequence as learning path. The percentages are queried in two different ways. Firstly, the students were asked to provide a sequence of learning elements within each position of the sequence filled with probabilities for all learning elements. Secondly, a learning element is given and the students are asked to indicate which learning element they would work on next. The first way of asking for the probabilities is used to find the start node in the MM, whereas the second approach forms the probability matrices between the nodes within the MM.&#13;
&#13;
As result of the survey, the Tyche algorithm presents generic transition probabilities. It improves the learning process of individuals only by asking for their learning style: it generates individual learning paths through the learning elements within an LMS based on the MM explained above by solely getting the answers of the ILS questionnaire as input. In the future, other questionnaires such as BFI-10 for personality traits or LIST-K for learning strategies may offer a more comprehensive input. However, the next step is to evaluate Tyche with about 25 students in a software engineering lecture. This is planned for the year 2024.</abstract>
    <parentTitle language="eng">INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024</parentTitle>
    <identifier type="doi">10.21125/inted.2024.1080</identifier>
    <identifier type="isbn">978-84-09-59215-9</identifier>
    <identifier type="issn">2340-1079</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-71148</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Susanne Staufer</author>
    <author>Flemming Bugert</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Timur Ezer</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Dominik Bittner</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Markov model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning style</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning elements</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning management system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>higher education area</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7114/STAUFER2024TYC.pdf</file>
  </doc>
  <doc>
    <id>7398</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>5</volume>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Singapore</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">On Eye Tracking in Software Engineering</title>
    <abstract language="deu">Eye tracking is becoming more and more important as a research method within the field of software engineering (SE). Existing meta-analyses focus on the design or conduct of SE eye tracking studies rather than the analysis phase. This article attempts to fill this gap; it presents a systematic literature review of eye tracking studies in the field of SE—focusing mainly on the data analysis methods used. From the IEEE Xplore and ACM digital libraries we gather 125 papers up to the first quarter of 2024. Detailed evaluation provides information on the number of papers that use specific methods of analysis (i.e., descriptive or inferential statistics, and gaze visualization) or settings (e.g., sample size, technical setup, and selected aspects of research design). With the data obtained we can infer the popularity of specific analysis methods in the field. Those results enable efficient work on data analysis tools or education of aspiring researchers and can serve as basis for standardization or guidelines within the community—providing for methods to include as well as current inconsistencies.</abstract>
    <parentTitle language="eng">SN Computer Science</parentTitle>
    <identifier type="doi">10.1007/s42979-024-03045-3</identifier>
    <identifier type="issn">2661-8907</identifier>
    <note>Zugehöriges Datenset: doi:10.5281/zenodo.11279180</note>
    <enrichment key="opus.import.date">2024-08-24T03:59:05+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Christian Wolff</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>Software engineering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Empirical</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Systematic literature review</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="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</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>
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    <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>7335</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
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    <type>article</type>
    <publisherName>Elsevier</publisherName>
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    <title language="eng">CausalOps - Towards an industrial lifecycle for causal probabilistic graphical models</title>
    <abstract language="eng">Context: &#13;
Causal probabilistic graph-based models have gained widespread utility, enabling the modeling of cause-and-effect relationships across diverse domains. With their rising adoption in new areas, such as safety analysis of complex systems, software engineering, and machine learning, the need for an integrated lifecycle framework akin to DevOps and MLOps has emerged. Currently, such a reference for organizations interested in employing causal engineering is missing. This lack of guidance hinders the incorporation and maturation of causal methods in the context of real-life applications.&#13;
&#13;
Objective: &#13;
This work contextualizes causal model usage across different stages and stakeholders and outlines a holistic view of creating and maintaining them within the process landscape of an organization.&#13;
&#13;
Method: &#13;
A novel lifecycle framework for causal model development and application called CausalOps is proposed. By defining key entities, dependencies, and intermediate artifacts generated during causal engineering, a consistent vocabulary and workflow model to guide organizations in adopting causal methods are established.&#13;
&#13;
Results: &#13;
Based on the early adoption of the discussed methodology to a real-life problem within the automotive domain, an experience report underlining the practicability and challenges of the proposed approach is discussed.&#13;
&#13;
Conclusion: &#13;
It is concluded that besides current technical advancements in various aspects of causal engineering, an overarching lifecycle framework that integrates these methods into organizational practices is missing. Although diverse skills from adjacent disciplines are widely available, guidance on how to transfer these assets into causality-driven practices still need to be addressed in the published literature. CausalOps’ aim is to set a baseline for the adoption of causal methods in practical applications within interested organizations and the causality community.</abstract>
    <parentTitle language="eng">Information and Software Technology</parentTitle>
    <identifier type="issn">0950-5849</identifier>
    <identifier type="doi">10.1016/j.infsof.2024.107520</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-73350</identifier>
    <note>Corresponding author der OTH Regensburg: Robert Maier</note>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="CorrespondingAuthor">Robert Maier</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Robert Maier</author>
    <author>Andreas Schlattl</author>
    <author>Thomas Guess</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Causal engineering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Model lifecycle</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MLOps</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Causal graphical models</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="oaweg" number="">Corresponding author der OTH Regensburg</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="funding" number="">DEAL Elsevier</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7335/Maier_IST_2024.pdf</file>
  </doc>
  <doc>
    <id>6772</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3121</pageFirst>
    <pageLast>3130</pageLast>
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    <title language="eng">Learning elements in online learning management systems</title>
    <parentTitle language="eng">Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023</parentTitle>
    <identifier type="doi">10.21125/iceri.2023.0815</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-67725</identifier>
    <enrichment key="opus.import.date">2023-12-14T12:10:39+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Susanne Staufer</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Dominik Bittner</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/6772/STAUFER_2023_LEA_PDFA.pdf</file>
  </doc>
  <doc>
    <id>6771</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4593</pageFirst>
    <pageLast>4603</pageLast>
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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>6769</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4611</pageFirst>
    <pageLast>4620</pageLast>
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    <title language="eng">On the analysis of student learning strategies: using the LIST-K questionnaire ro generate ai-based individualized learning paths</title>
    <abstract language="deu">This paper presents the results of a data collection with the LIST-K questionnaire. This questionnaire measures students’ learning strategies and shows which strategies are particularly dominant or rather weak.&#13;
&#13;
Learning strategies have long been a major area of research in educational science and psychology. In these disciplines, learning strategies are understood as intentional behaviors and cognitive skills that learners employ to effectively complete learning tasks, by selecting, acquiring, organizing, and integrating information into their existing knowledge for long-term retention.&#13;
&#13;
The LIST-K, developed by Klingsieck in 2018, was chosen for accessing learning strategies due to its thematic suitability, widespread use, and test economy. It covers a total of four main categories (i.e., cognitive strategies, metacognitive strategies, management of internal resources, and management of external resources), each of which are subdivided into further subscales. With a total of 39 items answered via a 5-step Likert scale, the LIST-K can cover the topic relatively comprehensively and at the same time be completed in a reasonable amount of time of approximately 10 minutes.&#13;
&#13;
The LIST-K was used as part of a combined data collection along with other questionnaires on their personal data, their preferences regarding certain learning elements, their learning style (i.e. the ILS), and personality (i.e. the BFI-10). A total of 207 students from different study programs participated via an online survey created using the survey tool "LimeSurvey". Participation in the study was voluntary, anonymously, and in compliance with the GDPR.&#13;
&#13;
Overall, the results of the LIST-K show that students are willing to work intensively on relevant topics intensively and to perform beyond the requirements of the course seeking additional learning material. At the same time, however, it is apparent that the organization of their own learning process could still be improved. For example, students start repeating content too late (mean=2.70; SD=0.92) and do not set goals for themselves and do not create a learning plan (mean=3.19; SD=0.90). They also learn without a schedule (mean=2.23; SD=0.97) and miss opportunities to learn together with other students (mean=3.17; SD=0.94).&#13;
&#13;
The findings of the data collection will be used to create an AI-based adaptive learning management system that will create individualized learning paths for students in their respective courses. From the results of the LIST-K, it appears that the adaptive learning management system should primarily support organizational aspects of student learning. Even small impulses (an individual schedule of when to learn what or a hierarchical structuring of the learning material) could help students to complete their courses more successfully and improve their learning.</abstract>
    <parentTitle language="eng">Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023</parentTitle>
    <identifier type="doi">10.21125/iceri.2023.1147</identifier>
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    <enrichment key="opus.source">sword</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Florian Hauser</author>
    <author>Susanne Staufer</author>
    <author>Lisa Grabinger</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>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>
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    <author>Timur Ezer</author>
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    <author>Jürgen Mottok</author>
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    <title language="eng">Unveiling the secrets of learning styles: decoding eye movements via machine learning</title>
    <abstract language="eng">Universities are faced with a rising number of dropouts in recent years. This is largely due to students' limited capability of finding individual learning paths through various course materials. However, a possible solution to this problem is the introduction of adaptive learning management systems, which recommend tailored learning paths to students – based on their individual learning styles. For the classification of learning styles, the most commonly used methods are questionnaires and learning analytics. Nevertheless, both methods are prone to errors: questionnaires may give superficial answers due to lack of time or motivation, while learning analytics do not reflect offline learning behavior. This paper proposes an alternative approach to classify students' learning styles by integrating eye tracking in combination with Machine Learning (ML) algorithms.&#13;
&#13;
Incorporating eye tracking technology into the classification process eliminates the potential problems arising from questionnaires or learning analytics by providing a more objective and detailed analysis of the subject's behavior. Moreover, this approach allows for a deeper understanding of subconscious processes and provides valuable insights into the individualized learning preferences of students.&#13;
&#13;
In order to demonstrate this approach, an eye tracking study is conducted with 117 participants using the Tobii Pro Fusion. Using qualitative and quantitative analyses, certain patterns in the subjects' gaze behavior are assigned to their learning styles given by the validated Index of Learning Styles (ILS) questionnaire.&#13;
&#13;
In short, this paper presents an innovative solution to the challenges associated with classifying students' learning styles. By combining eye tracking data with ML algorithms, an accurate and insightful understanding of students' individual learning paths can be achieved, ultimately leading to improved educational outcomes and reduced dropout rates.</abstract>
    <parentTitle language="eng">Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023</parentTitle>
    <identifier type="doi">10.21125/iceri.2023.1291</identifier>
    <identifier type="isbn">978-84-09-55942-8</identifier>
    <note>Zugehöriges Datenset: https://doi.org/10.5281/zenodo.8349468</note>
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    <author>Dominik Bittner</author>
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    <issue>5</issue>
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    <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>
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    <author>Enrico Pozzobon</author>
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    <title language="eng">Causal Models to Support Scenario-Based Testing of ADAS</title>
    <abstract language="eng">In modern vehicles, system complexity and technical capabilities are constantly growing. As a result, manufacturers and regulators are both increasingly challenged to ensure the reliability, safety, and intended behavior of these systems. With current methodologies, it is difficult to address the various interactions between vehicle components and environmental factors. However, model-based engineering offers a solution by allowing to abstract reality and enhancing communication among engineers and stakeholders. Applying this method requires a model format that is machine-processable, human-understandable, and mathematically sound. In addition, the model format needs to support probabilistic reasoning to account for incomplete data and knowledge about a problem domain. We propose structural causal models as a suitable framework for addressing these demands. In this article, we show how to combine data from different sources into an inferable causal model for an advanced driver-assistance system. We then consider the developed causal model for scenario-based testing to illustrate how a model-based approach can improve industrial system development processes. We conclude this paper by discussing the ongoing challenges to our approach and provide pointers for future work.</abstract>
    <parentTitle language="eng">IEEE Transactions on Intelligent Transportation Systems</parentTitle>
    <identifier type="doi">10.1109/TITS.2023.3317475</identifier>
    <identifier type="issn">1524-9050</identifier>
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    <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>
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    <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>
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    <publisherName>Wiley</publisherName>
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    <title language="eng">On the perception of graph layouts</title>
    <abstract language="eng">In the field of software engineering, graph-based models are used for a variety of applications. Usually, the layout of those graphs is determined at the discretion of the user. This article empirically investigates whether different layouts affect the comprehensibility or popularity of a graph and whether one can predict the perception of certain aspects in the graph using basic graphical laws from psychology (i.e., Gestalt principles). Data on three distinct layouts of one causal graph is collected from 29 subjects using eye tracking and a print questionnaire. The evaluation of the collected data suggests that the layout of a graph does matter and that the Gestalt principles are a valuable tool for assessing partial aspects of a layout.</abstract>
    <parentTitle language="eng">Journal of Software: Evolution and Process</parentTitle>
    <identifier type="doi">10.1002/smr.2599</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-62973</identifier>
    <note>Corresponding author: Lisa Grabinger</note>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>causal graphs</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>eye tracking</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>gestalt principles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>graph layouts</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>modeling languages</value>
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    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
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    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/6297/Grabinger_Journal_Software.pdf</file>
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    <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>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Costs</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transfer learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Benchmark testing</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cameras</value>
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    <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>
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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>
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