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    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
    <pageNumber/>
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    <issue/>
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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>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>eye tracking</value>
    </subject>
    <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>
    </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="funding" number="">DEAL Wiley</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/6297/Grabinger_Journal_Software.pdf</file>
  </doc>
  <doc>
    <id>5580</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>14</pageFirst>
    <pageLast>28</pageLast>
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    <volume/>
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    <publisherName>Springer</publisherName>
    <publisherPlace>Cham</publisherPlace>
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    <completedDate>2022-11-14</completedDate>
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    <title language="eng">Evaluation of Semantic Interoperability of Automotive Service API Models Based on Metamodels Similarity Metrics Using a Semi-automated Approach</title>
    <abstract language="eng">In recent years, mapping of application software components’ ontologies semantically emerged as a big research challenge in automotive application domain that manipulates several cross-enterprise synergy knowledge application frameworks. The same knowledge formalized by different experts in different vehicle application frameworks leads to heterogeneous representations of components’ interface data. Consequently, this causes the most daunting impediment in semantic interoperability between the service components in cooperative automotive systems. From a modeling perspective, in the absence of standardized domain-based unified modeling techniques, the orchestration and resolution of semantic data interoperability between various vehicle application frameworks’ components’ interface models remain a challenge. However, this challenge could be addressed using ontological metamodeling by specifying semantic associations between components’ interface model concepts based on the domain knowledge. Apart from the semantic mapping of interface ontological metamodels, this work also defines quality metrics to determine the degree of semantic alignment achieved between the various interface ontologies. Additionally, to reduce development time and cost towards semantic interoperability, this work proposes a semi-automated plugin tool for the applicability of the evaluated quality metrics to semantic mapping of real-world components’ interface models.</abstract>
    <parentTitle language="eng">iCETiC 2022 : 5th International Conference on Emerging Technologies in Computing 2022: Aug 15, 2022 - Aug 16, 2022, Chester, UK</parentTitle>
    <identifier type="doi">10.1007/978-3-031-25161-0_2</identifier>
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    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="OtherSeries">Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ; vol 463</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Sangita De</author>
    <author>Jürgen Mottok</author>
    <author>Premek Brada</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Framework</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Interface</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metamodel</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metrics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ontology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Semantic</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>6085</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>120</pageFirst>
    <pageLast>129</pageLast>
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    <issue/>
    <volume/>
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    <publisherName>ACM</publisherName>
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    <title language="eng">Evaluating Graph-based Modeling Languages</title>
    <abstract language="eng">As humans, we tend to use models to describe reality. Modeling languages provide the formal frameworks for creating such models. Usually, the graphical design of individual model elements is based on subjective decisions; their suitability is determined at most by the prevalence of the modeling language. With other words: there is no objective way to compare different designs of model elements. The present paper addresses this issue: it introduces a systematic approach for evaluating the elements of graph-based modeling languages comprising 14 criteria – derived from standards, usability analyses, or the design theories ‘Physics of Notations’ and ‘Cognitive Dimensions of Notations’. The criteria come with measurement procedures and evaluation schemes based on reasoning, eye tracking, and questioning. The developed approach is demonstrated with a specific use case: three distinct sets of node elements for causal graphs are evaluated in an eye tracking study with 41 subjects.</abstract>
    <parentTitle language="eng">Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023</parentTitle>
    <identifier type="doi">10.1145/3593663.3593664</identifier>
    <identifier type="isbn">978-1-4503-9956-2</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <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>6088</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>65</pageFirst>
    <pageLast>72</pageLast>
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    <issue/>
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    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
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    <title language="eng">Something Short Gets Even Shorter: Adapting the LIST-K for the Use in an Online Learning Management System</title>
    <abstract language="eng">This study examines how Klingsieck’s LIST-K questionnaire [22] can be shortened and adapted to the requirements of an online learning management system. In a study with 213 participants, the questionnaire is subjected to an exploitative factor analysis. In a next step, the results are evaluated in terms of their reliability. This process creates a modified factor structure for the LIST-K, comprising a total of eight factors. The reliability of the modified questionnaire is at an α of .770. The shortened version of the LIST-K questionnaire is currently being used on an experimental basis in different courses.</abstract>
    <parentTitle language="eng">Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023</parentTitle>
    <identifier type="doi">10.1145/3593663.3593684</identifier>
    <identifier type="isbn">978-1-4503-9956-2</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>
    <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>6115</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>7</issue>
    <volume>47</volume>
    <type>article</type>
    <publisherName>Slovenian Society Informatika</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
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    <title language="eng">Derivation of Optimized Threshold of Semantic Alignment Metrics for Intepretation of Interoperability and Reusability of Cross-enterprise Vehicle Service Interface Models</title>
    <abstract language="eng">Over the past decade, cars have turned gradually into real cyber physical systems. The collaboration of services between the service-oriented, cross-enterprise vehicle application frameworks has increased to generate novel, smart and complicated vehicle services. Consequently, from an interoperability perspective, semantically mapping of vehicle service component’s interface ontological models emerged as a big research interest in automotive application domain that manipulates several cross-enterprise synergy knowledge applications frameworks. The ontological metamodeling lays the foundation for building semantic bridge and exploring semantic associations between service components’ interface models based on the domain knowledge for semantic interoperability. Also, several semantic quality metrics has been defined over time for the vehicle service interface ontological metamodels. The empirically evaluated values of these metrics can be used to assess progress in cross-enterprise interoperability between the service and the clients’ APIs ontological models in vehicle domain. Despite potential benefits of semantic alignment quality metrics, the effective use of these metrics for vehicle service interface ontologies have proven elusive. Yes, such metrics can be used successfully for quantification, but then they mostly fail to provide adequate annotations in subsequent decision-making in the direction of semantic interoperability and reusability. The effective use of ontology semantic alignment quality metrics is basically hindered in the absence of the meaningful thresholds. In fact, the absence of an effective and meaningful threshold for the semantic similarity measure between various vehicle service interface ontological metamodels, motivates this research work which not only proposes a design approach to an optimized threshold for the semantic similarity metrics but also applies this threshold on few defined semantic alignment quality metrics. This paper also uses a real-world vehicle domain industrial case study to illustrate the design approach.</abstract>
    <parentTitle language="eng">Informatica</parentTitle>
    <identifier type="issn">0350-5596</identifier>
    <identifier type="doi">10.31449/inf.v47i7.4739</identifier>
    <enrichment key="opus.import.data">@article2254812609448121fc5c97bbfa4d469b, author = Sangita De, Premek Brada, Juergen Mottok, title = Derivation of Optimized Threshold of Semantic Alignment Metrics for Intepretation of Interoperability and Reusability of Cross-enterprise Vehicle Service Interface Models, journal = Informatica, volume = 47, number = 7, year = 2023, issn = 0350-5596, doi = 10.31449/inf.v47i7.4739,</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Sangita De</author>
    <author>Brada Premek</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>interface</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automotive services</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>semantic</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>metrics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>optimized threshold</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>framework</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>metamodels</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ontologies</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16312">Energie und Mobilität</collection>
    <collection role="oaweg" number="">Bronze Open Access - frei zugänglich aber ohne Lizenzhinweis</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>6256</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>7</pageLast>
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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>
  </doc>
  <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>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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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>
    <enrichment key="opus.source">sword</enrichment>
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    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <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>
  </doc>
  <doc>
    <id>6294</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
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    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
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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>
    </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>6768</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4500</pageFirst>
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    <publisherName>IATED</publisherName>
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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>
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  <doc>
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    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>130</pageFirst>
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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>
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    <type>article</type>
    <publisherName>IEEE</publisherName>
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    <title language="eng">Causal Models to Support Scenario-Based Testing of ADAS</title>
    <abstract language="eng">In modern vehicles, system complexity and technical capabilities are constantly growing. As a result, manufacturers and regulators are both increasingly challenged to ensure the reliability, safety, and intended behavior of these systems. With current methodologies, it is difficult to address the various interactions between vehicle components and environmental factors. However, model-based engineering offers a solution by allowing to abstract reality and enhancing communication among engineers and stakeholders. Applying this method requires a model format that is machine-processable, human-understandable, and mathematically sound. In addition, the model format needs to support probabilistic reasoning to account for incomplete data and knowledge about a problem domain. We propose structural causal models as a suitable framework for addressing these demands. In this article, we show how to combine data from different sources into an inferable causal model for an advanced driver-assistance system. We then consider the developed causal model for scenario-based testing to illustrate how a model-based approach can improve industrial system development processes. We conclude this paper by discussing the ongoing challenges to our approach and provide pointers for future work.</abstract>
    <parentTitle language="eng">IEEE Transactions on Intelligent Transportation Systems</parentTitle>
    <identifier type="doi">10.1109/TITS.2023.3317475</identifier>
    <identifier type="issn">1524-9050</identifier>
    <enrichment key="opus.import.date">2023-10-16T20:23:06+00:00</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Robert Maier</author>
    <author>Lisa Grabinger</author>
    <author>David Urlhart</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated driving systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Causal inference</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ISO Standards</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>model-based testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Task analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Vehicles</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>6772</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3121</pageFirst>
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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>6090</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>148</pageFirst>
    <pageLast>152</pageLast>
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    <publisherName>ACM</publisherName>
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    <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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    <title language="eng">Towards Learning Style Prediction based on Personality</title>
    <abstract language="eng">This paper assesses the relation between personality, demographics, and learning style. Hence, data is collected from 200 participants using 1) the BFI-10 to obtain the participant’s expression of personality traits according to the five-factor model, 2) the ILS to determine the participant’s learning style according to Felder and Silverman, and 3) a demographic questionnaire. From the obtained data, we train and evaluate a Bayesian network. Using Bayesian statistics, we show that age and gender slightly influence personality and that demographics as well as personality have at least a minor effect on learning styles. We also discuss the limitations and future work of the presented approach.</abstract>
    <parentTitle language="eng">Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023</parentTitle>
    <identifier type="doi">10.1145/3593663.3593682</identifier>
    <identifier type="isbn">978-1-4503-9956-2</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
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    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Flemming Bugert</author>
    <author>Lisa Grabinger</author>
    <author>Dominik Bittner</author>
    <author>Florian Hauser</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Susanne Staufer</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>6086</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>138</pageFirst>
    <pageLast>147</pageLast>
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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>
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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>
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    <publishedYear>2023</publishedYear>
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    <language>eng</language>
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    <title language="eng">On the analysis of student learning strategies: using the LIST-K questionnaire ro generate ai-based individualized learning paths</title>
    <abstract language="deu">This paper presents the results of a data collection with the LIST-K questionnaire. This questionnaire measures students’ learning strategies and shows which strategies are particularly dominant or rather weak.&#13;
&#13;
Learning strategies have long been a major area of research in educational science and psychology. In these disciplines, learning strategies are understood as intentional behaviors and cognitive skills that learners employ to effectively complete learning tasks, by selecting, acquiring, organizing, and integrating information into their existing knowledge for long-term retention.&#13;
&#13;
The LIST-K, developed by Klingsieck in 2018, was chosen for accessing learning strategies due to its thematic suitability, widespread use, and test economy. It covers a total of four main categories (i.e., cognitive strategies, metacognitive strategies, management of internal resources, and management of external resources), each of which are subdivided into further subscales. With a total of 39 items answered via a 5-step Likert scale, the LIST-K can cover the topic relatively comprehensively and at the same time be completed in a reasonable amount of time of approximately 10 minutes.&#13;
&#13;
The LIST-K was used as part of a combined data collection along with other questionnaires on their personal data, their preferences regarding certain learning elements, their learning style (i.e. the ILS), and personality (i.e. the BFI-10). A total of 207 students from different study programs participated via an online survey created using the survey tool "LimeSurvey". Participation in the study was voluntary, anonymously, and in compliance with the GDPR.&#13;
&#13;
Overall, the results of the LIST-K show that students are willing to work intensively on relevant topics intensively and to perform beyond the requirements of the course seeking additional learning material. At the same time, however, it is apparent that the organization of their own learning process could still be improved. For example, students start repeating content too late (mean=2.70; SD=0.92) and do not set goals for themselves and do not create a learning plan (mean=3.19; SD=0.90). They also learn without a schedule (mean=2.23; SD=0.97) and miss opportunities to learn together with other students (mean=3.17; SD=0.94).&#13;
&#13;
The findings of the data collection will be used to create an AI-based adaptive learning management system that will create individualized learning paths for students in their respective courses. From the results of the LIST-K, it appears that the adaptive learning management system should primarily support organizational aspects of student learning. Even small impulses (an individual schedule of when to learn what or a hierarchical structuring of the learning material) could help students to complete their courses more successfully and improve their learning.</abstract>
    <parentTitle language="eng">Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023</parentTitle>
    <identifier type="doi">10.21125/iceri.2023.1147</identifier>
    <enrichment key="opus.import.date">2023-12-14T12:10:39+00:00</enrichment>
    <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>Florian Hauser</author>
    <author>Susanne Staufer</author>
    <author>Lisa Grabinger</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
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  <doc>
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    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>83</pageFirst>
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    <title language="eng">Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning Management Systems</title>
    <abstract language="eng">Nowadays, learning management systems are widely employed in all educational institutions to instruct students as a result of the increasing in online usage. Today’s learning management systems provide learning paths without personalizing them to the characteristics of the learner. Therefore, research these days is concentrated on employing AI-based strategies to personalize the systems. However, there are many different AI algorithms, making it challenging to determine which ones are most suited for taking into account the many different features of learner data and learning contents. This paper conducts a systematic literature review in order to discuss the AI-based methods that are frequently used to identify learner characteristics, organize the learning contents, recommend learning paths, and highlight their advantages and disadvantages.</abstract>
    <parentTitle language="eng">Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023</parentTitle>
    <identifier type="doi">10.1145/3593663.3593681</identifier>
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    <author>Vamsi Krishna Nadimpalli</author>
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    <author>Dominik Bittner</author>
    <author>Lisa Grabinger</author>
    <author>Susanne Staufer</author>
    <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;
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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>
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    <title language="eng">Eye Tracking based Learning Style Identification for Learning Management Systems [Data set]</title>
    <abstract language="eng">In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.</abstract>
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Is new version of publication: 10.1145/3593663.3593680 (DOI) &#13;
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    <abstract language="eng">This repository contains the material and obtained data of an eye tracking study on the topic "MISRA C coding guidelines".</abstract>
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