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    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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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>
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    <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>
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  <doc>
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    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
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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>
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    <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>
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  <doc>
    <id>6771</id>
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    <publishedYear>2023</publishedYear>
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    <language>eng</language>
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    <title language="eng">Towards personalized learning paths in adaptive learning management systems: bayesian modelling of psychological theories</title>
    <abstract language="eng">In educational research, non-personalized learning content increases learners' cognitive load, causing them to lower their performance and sometimes drop out of the course. Personalizing learning content with learners’ unique characteristics, like learning styles, personality traits, and learning strategies, is being suggested to improve learners’ success. Several theories exist for assessing learners’ unique characteristics. By the end of 2020, 71 learning style theories have been formulated, and research has shown that combining multiple learning style theories to recommend learning paths yields better results. As of the end of 2022, there is no single research that demonstrates a relationship between the Index of Learning Styles (ILS) based Felder-Silverman learning style model (FSLSM) dimensions, Big Five (BFI-10) based personality traits, and the Learning strategies in studying (LIST-K) based learning strategies factors for personalizing learning content.&#13;
&#13;
In this paper, an innovative approach is proposed to estimate the relationship between these theories and map the corresponding learning elements to create personalized learning paths. Respective questionnaires were distributed to 297 higher education students for data collection. A three-step approach was formulated to estimate the relationship between the models. First, a literature search was conducted to find existing studies. Then, an expert interview was carried out with a group of one software engineering education research professor, three doctoral students, and two master’s students. Finally, the correlations between the students' questionnaire responses were calculated. To achieve this, a Bayesian Network was built with expert knowledge from the three-step approach, and the weights were learned from collected data. The probability of individual FSLSM learning style dimensions was estimated for a new test sample. Based on the literature, the learning elements were mapped to the respective FSLSM learning style dimensions and were initiated as learning paths to the learners.&#13;
&#13;
The next steps are proposed to extend this framework and dynamically recommend learning paths in real time. In addition, the individual levels of learning style dimensions, personality traits, and learning strategies can be considered to improve the recommendations. Further, using probabilities for mapping learning elements to learning styles can increase the chance of initiating multiple learning paths for an individual learner.</abstract>
    <parentTitle language="eng">Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023</parentTitle>
    <identifier type="doi">10.21125/iceri.2023.1144</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Flemming Bugert</author>
    <author>Dominik Bittner</author>
    <author>Florian Hauser</author>
    <author>Lisa Grabinger</author>
    <author>Susanne Staufer</author>
    <author>Jürgen Mottok</author>
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    <title language="eng">On the analysis of student learning strategies: using the LIST-K questionnaire ro generate ai-based individualized learning paths</title>
    <abstract language="deu">This paper presents the results of a data collection with the LIST-K questionnaire. This questionnaire measures students’ learning strategies and shows which strategies are particularly dominant or rather weak.&#13;
&#13;
Learning strategies have long been a major area of research in educational science and psychology. In these disciplines, learning strategies are understood as intentional behaviors and cognitive skills that learners employ to effectively complete learning tasks, by selecting, acquiring, organizing, and integrating information into their existing knowledge for long-term retention.&#13;
&#13;
The LIST-K, developed by Klingsieck in 2018, was chosen for accessing learning strategies due to its thematic suitability, widespread use, and test economy. It covers a total of four main categories (i.e., cognitive strategies, metacognitive strategies, management of internal resources, and management of external resources), each of which are subdivided into further subscales. With a total of 39 items answered via a 5-step Likert scale, the LIST-K can cover the topic relatively comprehensively and at the same time be completed in a reasonable amount of time of approximately 10 minutes.&#13;
&#13;
The LIST-K was used as part of a combined data collection along with other questionnaires on their personal data, their preferences regarding certain learning elements, their learning style (i.e. the ILS), and personality (i.e. the BFI-10). A total of 207 students from different study programs participated via an online survey created using the survey tool "LimeSurvey". Participation in the study was voluntary, anonymously, and in compliance with the GDPR.&#13;
&#13;
Overall, the results of the LIST-K show that students are willing to work intensively on relevant topics intensively and to perform beyond the requirements of the course seeking additional learning material. At the same time, however, it is apparent that the organization of their own learning process could still be improved. For example, students start repeating content too late (mean=2.70; SD=0.92) and do not set goals for themselves and do not create a learning plan (mean=3.19; SD=0.90). They also learn without a schedule (mean=2.23; SD=0.97) and miss opportunities to learn together with other students (mean=3.17; SD=0.94).&#13;
&#13;
The findings of the data collection will be used to create an AI-based adaptive learning management system that will create individualized learning paths for students in their respective courses. From the results of the LIST-K, it appears that the adaptive learning management system should primarily support organizational aspects of student learning. Even small impulses (an individual schedule of when to learn what or a hierarchical structuring of the learning material) could help students to complete their courses more successfully and improve their learning.</abstract>
    <parentTitle language="eng">Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023</parentTitle>
    <identifier type="doi">10.21125/iceri.2023.1147</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Florian Hauser</author>
    <author>Susanne Staufer</author>
    <author>Lisa Grabinger</author>
    <author>Simon Röhrl</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
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  <doc>
    <id>6105</id>
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    <language>eng</language>
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    <publisherName>Association for Computing Machinery</publisherName>
    <publisherPlace>New York</publisherPlace>
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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>
    <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>Vamsi Krishna Nadimpalli</author>
    <author>Florian Hauser</author>
    <author>Dominik Bittner</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>
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    <publishedYear>2023</publishedYear>
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    <language>eng</language>
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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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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Dominik Bittner</author>
    <author>Timur Ezer</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>
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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>
    <identifier type="doi">10.5281/zenodo.8349468</identifier>
    <note>Related works: &#13;
Is new version of publication: 10.1145/3593663.3593680 (DOI) &#13;
Is published in publication: 10.21125/iceri.2023.1291 (DOI)</note>
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    <author>Dominik Bittner</author>
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    <author>Lisa Grabinger</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
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    <title language="eng">Study: MISRA C coding guidelines [Data set]</title>
    <abstract language="eng">This repository contains the material and obtained data of an eye tracking study on the topic "MISRA C coding guidelines".</abstract>
    <identifier type="doi">10.5281/zenodo.7898606</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Lisa Grabinger</author>
    <author>Alexander Homann</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
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    <title language="eng">Fuzzy fault injection attacks against secure automotive bootloaders</title>
    <abstract language="eng">Secure embedded bootloaders are the trust anchors for modern vehicles’ software. The secure software update process of ECUs is well-defined across the entire automotive industry. Every OEM has his own implementation, but follows the general software update process. This paper demonstrates code execution attacks by combining software and hardware weaknesses in secure automotive bootloaders. The attack can be performed entirely automated, no static code analysisis required. Random fault injection parameters were sufficient to obtain code execution in a reasonable time. All experiments were conducted with electromagnetic fault injection and without any hardware modifications of the targets. We successfully performed our attack on two entirely different gateway Electronic Control Units (ECUs) used in current vehicles (at the time of this research) from Volkswagen and BMW. As a result of this attack, consisting of a combination of a hardware and asoftware attack, the general secure software update process used in the automotive industry needs to be revised.</abstract>
    <parentTitle language="eng">21th escar Europe : The World's Leading Automotive Cyber Security Conference (Hamburg, 15. - 16.11.2023)</parentTitle>
    <identifier type="doi">10.13154/294-10381</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Enrico Pozzobon</author>
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    <author>Václav Matoušek</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
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