TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Bittner, Dominik A1 - Staufer, Susanne A1 - Röhrl, Simon A1 - Hauser, Florian A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Maier, Robert A1 - Mottok, Jürgen T1 - Probabilistic Machine Learning for Simulating Complex Learner Profiles T2 - 2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), 06-08 November 2024, Paris, France N2 - Data is the foundation of all machine learning applications. In education science, especially for the learner characteristics that drive personalized learning, it is difficult to collect and often uncertain. It is challenging to model, train, evaluate, and analyze the underlying algorithms when developing AI-based systems and having small sample sizes. To address these problems, we present a synthetic data generator utilising probabilistic models. This generator can effectively model and simulate complex learner profiles. To achieve this, we collected extensive data on learning styles, learning strategies, personalities, and preferred learning paths from 593 students over several semesters at a higher education level. Then, Bayesian networks, Hidden Markov Models, and Markov Chains are used to model the relationships between learner profiles. Using the Bayesian information criterion, and cross-validation with log-likelihood scores, we compare various models to select the best fitting one for synthesizing the data. The synthetic data is then evaluated using statistical validation techniques. In addition, we developed a simulation module with the option to simulate learner profiles based on manual user-defined inputs. The data and code used in this work are available as open source1110.5281/zenodo.13768136 [Titel anhand dieser DOI in Citavi-Projekt übernehmen] contributing to open science and developers for customized simulated data. In the future, this data will refine the training, evaluation, analysis, and benchmarking of algorithms for personalized learning. KW - probabilistic models KW - synthetic data KW - learner characteristics KW - generative AI KW - higher education area Y1 - 2024 SN - 979-8-3315-1663-5 U6 - https://doi.org/10.1109/ITHET61869.2024.10837641 SN - 2473-2060 SP - 1 EP - 10 PB - IEEE ER - TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Staufer, Susanne A1 - Ezer, Timur A1 - Bugert, Flemming A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Röhrl, Simon A1 - Maier, Robert A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Predicting learner characteristics using machine learning T2 - ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain N2 - In education science research, data collection is challenging due to difficulty identifying students at the higher education level, privacy concerns, and varying levels of student engagement. Importantly, psychological questionnaires can be lengthy, leading to incomplete responses. We conducted repeated studies, and over time, the focus of the research adapted, introducing new materials and consequently leading to missing learner characteristics in some datasets. In this research, the issue of incomplete learner characteristics is addressed using data from three different studies: winter term 2022/2023 (n=297), summer term 2023 (n=274), and winter term 2023/2024 (n=25). These studies collected various learner characteristics, such as learning styles, personalities, learning strategies, and learning element preferences. However, learning element preferences and learning strategies were missing in the winter term of 2022, and the summer term of 2023 respectively. To analyze the data and predict these missing features, statistical analysis, and machine learning techniques were employed. Then, these models are rigorously evaluated using cross-validation and performance metrics like accuracy, precision, recall, and F1-score. Our findings provide insights into the relationships between learners' learning styles, personalities, learning strategies, and learning element preferences. This offers valuable implications for the design and implementation of educational interventions, like learning path recommendations. The results imply that machine learning models can predict missing learner characteristics, thus addressing the problem of incomplete data in educational research Y1 - 2024 U6 - https://doi.org/10.21125/iceri.2024.1454 SP - 6005 EP - 6014 PB - IATED ER - TY - CHAP A1 - Hammer, Pascal A1 - Mottok, Jürgen T1 - Prototyping for high-performance random number generators for critical systems T2 - 2025 International Conference on Applied Electronics (AE), 08-09. September 2025, Pilsen Czech Republic N2 - The security of modern cryptographic applications depends on the quality of their random number generators (RNGs). While physical true random number generators (PTRNGs) provide high-quality randomness, their performance is often insufficient for high-throughput applications. On the other hand, deterministic random number generators (DRNGs) offer better speed but lack true randomness. This paper presents an approach to developing a high-performance PTRNG. The research focuses on optimizing the digitization of analog noise signals. For this purpose, an ADC was selected that can digitize analog signals at a high clock rate. The functionality of the ADC could be verified by digitizing reference signals. Preliminary results by digitizing the analog noise signal demonstrates the feasibility of the proposed approach, but highlight challenges in achieving good results for Min-Entropy. Y1 - 2025 U6 - https://doi.org/10.1109/AE66163.2025.11197780 PB - IEEE ER - TY - CHAP A1 - Staufer, Susanne A1 - Bugert, Flemming A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Ezer, Timur A1 - Nadimpalli, Vamsi Krishna A1 - Bittner, Dominik A1 - Röhrl, Simon A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Tyche algorithm: Markov models for generating learning paths in learning management systems T2 - INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024 N2 - 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. 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. 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. KW - Markov model KW - learning style KW - learning elements KW - learning management system KW - higher education area Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-71148 SN - 978-84-09-59215-9 SN - 2340-1079 SP - 4195 EP - 4205 PB - IATED Academy ER - TY - CHAP A1 - Staufer, Susanne A1 - Ezer, Timur A1 - Röhrl, Simon A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Nadimpalli, Vamsi Krishna A1 - Antoni, Erika A1 - Mottok, Jürgen A1 - Schaffer, Josefa T1 - TYCHE ALGORITHM 2.0: Learning Paths from Questionnaire Responses and Learning Analytics T2 - ICERI2025 Proceedings N2 - The digitalization of learning processes has increased the need for adaptive learning paths tailored individually to learners. A novel algorithm for learning path generation is presented in this paper, namely Tyche 2.0. It extends the original Tyche approach after Staufer et al. – a Markov model for generating learning paths – by integrating additional learner data beyond learning styles (Index of Learning Styles (ILS)), including learning strategies (LIST-K questionnaire), personality traits (BFI-10 questionnaire), and learning analytics captured through screen recordings. In order to be able to use the screen recordings, a heuristic evaluates them. Furthermore, this enhanced algorithm employs Markov models to dynamically generate personalized learning paths. These are based on both questionnaire responses and real-time engagement data, the weights of which undergo dynamic adjustment over time. We made a small evaluation of Tyche 2.0 without the learning analytics influence, which shows that there is room for further improvements. Future research will focus on evaluating whole Tyche 2.0 in another university setting to further improve personalization and user engagement. KW - Markov model KW - Learning paths KW - Learning style KW - Learning strategy KW - Personality traits KW - Learning analytics Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-87846 SN - 978-84-09-78706-7 SP - 2562 EP - 2572 PB - IATED ER - TY - CHAP A1 - Hauser, Florian A1 - Staufer, Susanne A1 - Röhrl, Simon A1 - Nadimpalli, Vamsi Krishna A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Mottok, Jürgen A1 - Falter, Thomas ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - LEVERAGING FIVE QUESTIONNAIRES TO ANALYZE STUDENT LEARNING STRATEGIES AND GENERATE AI-POWERED INDIVIDUALIZED LEARNING PATHS T2 - ICERI2025 Proceedings N2 - Background: The COVID-19 pandemic has significantly accelerated the shift toward online and blended learning in higher education, placing renewed emphasis on the individualization of learning content to meet diverse student needs. Even high-quality learning materials may fail to engage learners if they do not align with students’ personal preferences and learning styles. Identifying these learner preferences, therefore, emerges as a critical challenge. Objectives: This paper presents ongoing work within a larger research project aimed at employing artificial intelligence to recommend optimal learning path for students in specific courses. Beyond mere optimization, the goal is to ensure the best possible fit between learning materials and individual learners. Sample & Methods: A total of 27 students from technical degree programs took part in this survey. All participation was voluntary, and data were handled in full compliance with GDPR regulations. Although our broader project integrates fine-grained learning analytics from Moodle, the present abstract focuses exclusively on the self-report questionnaire results. Participants completed five instruments: 1. Index of Learning Styles (ILS) 2. LIST-K (Learning and Study Strategies Inventory – Short version) 3. BFI-10 (Big Five Inventory – 10 items) 4. Custom Preferences Instrument, capturing preferences for specific learning elements (e.g. instructional videos, lecture notes, summaries) and basic demographic data 5. Motivational Value Systems Questionnaire (MVSQ), piloted last semester to assess value orientations and motivational drivers Results: Preliminary analyses of the questionnaire data reveal: - Learning Styles (ILS): The majority lean toward the visual learning type (M = 5.740, SD = 3.430). - Learning Strategies (LIST-K): High scores on metacognitive strategies (M = 3.000; SD = 0.520) and collaboration with peers (M = 3.190; SD = 0.540). - Preferred Learning Elements: Summaries, overviews, and self-checks are most favored. - Value Orientations (MVSQ): Students are primarily driven by the pursuit of personal achievement (M = 4.400; SD = 11.140). Conclusion & Significance: By integrating these five standardized questionnaires, we gain valuable insights into student learning preferences—insights that complement our Moodle analytics in the broader project. Observed trends suggest that learning materials should be concise and designed to facilitate peer interaction and knowledge deepening. These findings will guide the refinement of our AI-driven recommendation engine, enhancing its ability to deliver personalized learning paths that boost both engagement and effectiveness. KW - AI in higher education KW - learning management system KW - adaptive learning KW - personalized learning paths KW - online and blended learning Y1 - 2025 U6 - https://doi.org/10.21125/iceri.2025.0658 SP - 1775 EP - 1784 PB - IATED ER - TY - CHAP A1 - Schaffer, Josefa A1 - Ezer, Timur A1 - Röhrl, Simon A1 - Hauser, Florian A1 - Staufer, Susanne A1 - Nadimpalli, Vamsi Krishna A1 - Grabinger, Lisa A1 - Antoni, Erika A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - EYE TRACKING GLASSES IN EDUCATIONAL SETTINGS: GUIDELINES ON DATA QUALITY T2 - ICERI2025 Proceedings N2 - Eye movement modeling examples, so-called EMME videos, are a valuable tool in education, helping learners better understand instructional content. Like conventional educational videos, EMME videos combine text, images, and voiceovers. However, they also display the instructor’s or teacher’s gaze, guiding learners attention to key elements. Although various approaches exist for creating EMME videos, there is currently no standardised guideline for ensuring gaze data quality. Eye tracking technology is essential to capture gaze behaviour, and in educational settings without a fixed computer monitor – such as when using blackboards or conducting live experiments – the usage of mobile eye tracking glasses is beneficial. An accuracy study is conducted using mobile eye tracking glasses to provide empirical guidance for the development of high-quality educational EMME videos and ensure that the instructor’s or teacher’s gaze is captured with high precision. The study uses the Tobii Pro Glasses 3 and involves a static and a dynamic setup with 34 participants. To gain insight into the effects of visual impairments on accuracy, we also include participants who wear contact lenses. In the static setup, participants are seated at a desk with a headrest and focus on a poster with nine fixation points. In the dynamic setup, participants are walking in a controlled half-circle around the poster while maintaining focus on its centre. Each setup is performed multiple times under varying lighting levels (300 lux, 700 lux) and distances between participant and poster (80 cm, 120 cm, 180 cm). This enables the simulation of diverse educational environments, including the possibility of a teacher’s or instructor’s movement. The study results will be evaluated regarding lighting conditions, the distance between the person wearing eye tracking glasses and the object, and possible influences of contact lenses. Based on these findings, favourable conditions for creating EMME videos in educational settings are collected, especially when working without a fixed computer monitor. The results address the outlined research gap by providing instructors and teachers with guidelines enabling them to produce high-quality educational EMME videos. Y1 - 2025 U6 - https://doi.org/10.21125/iceri.2025.1419 SP - 5027 EP - 5038 PB - IATED ER - TY - CHAP A1 - Ezer, Timur A1 - Röhrl, Simon A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Towards accurate eye tracking: quantifying error in linear pixel-to-degree conversion T2 - ICERI2025 Proceedings N2 - Eye tracking has become a powerful tool for analyzing cognitive processes in educational research. Educators and researchers can utilize eye tracking to model learners by determining their gaze patterns. In addition, eye tracking can be used directly for teaching by extracting experts' gaze behavior in certain areas and learning from it. However, eye tracking data is captured in a variety of coordinate systems, which can differ across individual studies. Consequently, algorithms, such as eye movement classifiers, must frequently convert gaze data between different coordinate systems. In particular, the conversion from screen pixels into visual degrees is typically approximated by a linear conversion in current literature and standard practice. However, this approximation introduces inaccuracies, thus potentially obscuring eye movements relevant to educational research. This paper provides a detailed geometric and analytical examination of the commonly applied linear approximation, quantifying its error in comparison to the exact coordinate conversion from screen pixels to angular degrees. For this purpose, the exact conversion formulas are mathematically derived from geometric optics, enabling researchers and educators to use them in their work. Utilizing these derived transformations can improve the robustness of analyses, for example, when detecting subtle eye movements. The present work supports educational research using eye tracking to achieve more insightful findings that may have previously been obscured by measurement inaccuracies. Thereby, we provide an important contribution toward more reliable and valid research and educational practices in eye tracking. KW - Eye tracking KW - coordinate system KW - conversion KW - angular degree KW - pixel Y1 - 2025 U6 - https://doi.org/10.21125/iceri.2025.1841 SP - 6709 EP - 6717 PB - IATED ER - TY - CHAP A1 - Alhulaibi, Ayham A1 - Frauenschläger, Tobias A1 - Mottok, Jürgen T1 - Towards Post-Quantum-Ready Automated Certificate Lifecycle Management in Operational Technology T2 - SECURWARE 2025, The Nineteenth International Conference on Emerging Security Information, Systems and Technologies, 26.-30.10.2025, Barcelona N2 - Operational Technology (OT) systems increasingly depend on robust and automated certificate lifecycle management to maintain secure operations across long device lifespans and constrained environments. As quantum-capable adversaries emerge, these systems must also support cryptographic agility and prepare for a seamless transition to Post-Quantum Cryptography (PQC). This work presents a crypto-agile, post-quantum-ready testbed architecture that extends existing standards, such as Enrollment over Secure Transport (EST) and Bootstrapping Remote Secure Key Infrastructure (BRSKI), to support hybrid certificates, hardware-based key storage, and protocol flexibility for device bootstrapping and certificate management. A work-in-progress prototype implementation demonstrates support for both traditional and PQC algorithms across device types. Planned evaluations target performance on constrained devices, PQC readiness, and compatibility with alternative protocols. The system lays a foundation for secure and standards-compliant certificate management in future-proof OT deployments. KW - Automated Device Onboarding KW - BRSKI KW - Operational Technology Security. KW - Post-Quantum Cryptography KW - Public Key Infrastructure KW - Security Token Y1 - 2025 UR - https://www.thinkmind.org/library/SECURWARE/SECURWARE_2025 SN - 978-1-68558-306-4 SP - 112 EP - 116 PB - IARIA ER - TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Maier, Robert A1 - Staufer, Susanne A1 - Röhrl, Simon A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - EXPERT SURVEYS TO REAL TIME ADAPTATION OF LEARNING PATHS T2 - ICERI2025 Proceedings N2 - Learning management systems rely on adaptive algorithms that use learner preferences to personalize the instructional content in form of learning paths. However, these preferences are uncertain in nature, and change over time. The present solutions are either static or purely data-driven missing the dynamic adaption to changes in the preferences and infusion of pedagogical nuances respectively. This paper introduces an extended variant of Nestor, our Bayesian network engine that models personality traits, learning styles, and learning strategies. This extension overlays a lightweight rule-based mechanism whose “secret recipe’’ lies in the infusion of expert-derived weights adapting learning paths dynamically whenever a learner selects new material in Moodle. To parameterise these rules, we conducted a structured survey with 12 hand-picked professors and researchers in educational science. Each expert responded to 4 demographic items and 12 item that are distributed across algorithm-overview, scenario-based, and example-based categories, thereby supplying the nuanced weightings that result the personalised recommendations. This hybrid system (Nestor plus the expert-infused rule layer) operated during the winter term of 2025. 18 students completed an end-of-term questionnaire. Although their learning gains were not recorded, the majority of respondents reported positive or neutral experiences with the dynamically adapted learning paths. The {Future work} will compare three engines: (i) the present dynamic, expert-infused rule layer on top of the static Bayesian network, (ii) purely data-driven machine-learning models that neglect expert weighting, and (iii) the original static-adaptation Bayesian network without rules. Analyses of log files, intermediate satisfaction surveys, and pre/post term surveys will clarify whether this on-the-fly adaptation and pedagogical nuance lead to measurable learning benefits. Y1 - 2025 U6 - https://doi.org/10.21125/iceri.2025.1571 SP - 5677 EP - 5687 PB - IATED ER - TY - CHAP A1 - Schönhärl, Stefan A1 - Fuxen, Philipp A1 - Graf, Julian A1 - Schmidt, Jonas A1 - Hackenberg, Rudolf A1 - Mottok, Jürgen T1 - An Automotive Penetration Testing Framework for IT-Security Education T2 - Cloud Computing 2022: The Thirteenth International Conference on Cloud Computing, GRIDs, and Virtualization, Special Track FAST-CSP, Barcelona, Spain, 24.-28.04.2022 N2 - Automotive Original Equipment Manufacturer (OEM) and suppliers started shifting their focus towards the security of their connected electronic programmable products recently since cars used to be mainly mechanical products. However, this has changed due to the rising digitalization of vehicles. Security and functional safety have grown together and need to be addressed as a single issue, referred to as automotive security, in the following article. One way to accomplish security is automotive security education. The scientific contribution of this paper is to establish an Automotive Penetration Testing Education Platform (APTEP). It consists of three layers representing different attack points of a vehicle. The layers are the outer, inner, and core layers. Each of those contains multiple interfaces, such as Wireless Local Area Network (WLAN) or electric vehicle charging interfaces in the outer layer, message bus systems in the inner layer, and debug or diagnostic interfaces in the core layer. One implementation of APTEP is in a hardware case and as a virtual platform, referred to as the Automotive Network Security Case (ANSKo). The hardware case contains emulated control units and different communication protocols. The virtual platform uses Docker containers to provide a similar experience over the internet. Both offer two kinds of challenges. The first introduces users to a specific interface, while the second combines multiple interfaces, to a complex and realistic challenge. This concept is based on modern didactic theory, such as constructivism and problem-based learning. Computer Science students from the Ostbayerische Technische Hochschule (OTH)Regensburg experienced the challenges as part of a special topic course and provided positive feedback. KW - IT-Security KW - Education KW - Automotive KW - Penetration testing KW - Education framework Y1 - 2022 UR - https://www.thinkmind.org/index.php?view=article&articleid=cloud_computing_2022_1_10_28001 SN - 978-1-61208-948-5 SP - 1 EP - 6 PB - IARIA CY - [Wilmington, DE, USA] ER - TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Hauser, Florian A1 - Bittner, Dominik A1 - Grabinger, Lisa A1 - Staufer, Susanne A1 - Mottok, Jürgen ED - Mottok, Jürgen T1 - Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning Management Systems T2 - Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023 N2 - 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. Y1 - 2023 SN - 978-1-4503-9956-2 U6 - https://doi.org/10.1145/3593663.3593681 SP - 83 EP - 92 PB - Association for Computing Machinery CY - New York ER - TY - CHAP A1 - Bittner, Dominik A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Unveiling the secrets of learning styles: decoding eye movements via machine learning T2 - Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023 N2 - 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. 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. 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. 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. Y1 - 2023 SN - 978-84-09-55942-8 U6 - https://doi.org/10.21125/iceri.2023.1291 N1 - Zugehöriges Datenset: https://doi.org/10.5281/zenodo.8349468 SP - 5153 EP - 5162 PB - IATED ER - TY - GEN A1 - Bittner, Dominik A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Mottok, Jürgen T1 - Eye Tracking based Learning Style Identification for Learning Management Systems [Data set] N2 - 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. Y1 - 2023 U6 - https://doi.org/10.5281/zenodo.8349468 N1 - Related works: Is new version of publication: 10.1145/3593663.3593680 (DOI) Is published in publication: 10.21125/iceri.2023.1291 (DOI) ER - TY - GEN A1 - Grabinger, Lisa A1 - Homann, Alexander A1 - Hauser, Florian A1 - Mottok, Jürgen T1 - Study: MISRA C coding guidelines [Data set] N2 - This repository contains the material and obtained data of an eye tracking study on the topic "MISRA C coding guidelines". Y1 - 2023 U6 - https://doi.org/10.5281/zenodo.7898606 ER - TY - GEN A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Mottok, Jürgen T1 - Study: Notation of Causal Graphs [Data set] N2 - This repository contains the material and obtained data of an eye tracking study on the topic "Notation of Causal Graphs". Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7241158 ER - TY - GEN A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Mottok, Jürgen T1 - Study: Layout of Causal Graphs [Data set] N2 - This repository contains the material and obtained data of an eye tracking study on the topic "Layout of Causal Graphs". Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7241097 ER - TY - JOUR A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Wolff, Christian A1 - Mottok, Jürgen T1 - On Eye Tracking in Software Engineering JF - SN Computer Science N2 - 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. KW - Eye tracking KW - Software engineering KW - Empirical KW - Systematic literature review Y1 - 2024 U6 - https://doi.org/10.1007/s42979-024-03045-3 SN - 2661-8907 N1 - Zugehöriges Datenset: doi:10.5281/zenodo.11279180 VL - 5 IS - 6 PB - Springer CY - Singapore ER - TY - CHAP A1 - Hammer, Pascal A1 - Mottok, Jürgen A1 - Krause, Veronika A1 - Probst, Tobias T1 - Approach for High-Performance Random Number Generators for Critical Systems T2 - Proceeding of the 12th European Congress on Embedded Real Time Software and Systems (ERTS2024) , Toulouse, 11-12 June 2024 N2 - In times of digitalization, the encryption and signing of sensitive data is becoming increasingly important. These cryptographic processes require large quantities of high-quality random numbers. Which is why a high-performance random number generator (RNG) is to be developed. For this purpose, existing concepts of RNGs and application standards are first analyzed. The proposed approach is to design a physical true random number generator (PTRNG) with a high output of random numbers. Based on this, the development begins with the analog part of the RNG, the noise signal source and a suitable amplifier for the analog noise signal. Therefore, a special noise diode from Noisecom and an amplifier from NXP were chosen and analyzed in different measurements. From the results of the measurements, it can be concluded that both components are suitable for use in the RNG. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.14848832 N1 - Identifier: hal-04678880 , version 1 ER - TY - CHAP A1 - Pozzobon, Enrico A1 - Weiß, Nils A1 - Mottok, Jürgen A1 - Matoušek, Václav T1 - Fuzzy fault injection attacks against secure automotive bootloaders T2 - 21th escar Europe : The World's Leading Automotive Cyber Security Conference (Hamburg, 15. - 16.11.2023) N2 - 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. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:294-103817 PB - Ruhr-Universität Bochum ER - TY - INPR A1 - Gaube, Susanne A1 - Jussupow, Ekaterina A1 - Kokje, Eesha A1 - Khan, Jowaria A1 - Bondi-Kelly, Elizabeth A1 - Schicho, Andreas A1 - Kitamura, Felipe Campos A1 - Koch, Timo Kevin A1 - Ezer, Timur A1 - Mottok, Jürgen A1 - Lermer, Eva A1 - Ghassemi, Marzyeh A1 - Colak, Errol T1 - Examining Reliance Patterns on AI Advice in Medical Imaging: a Mixed-Methods Randomized Crossover Experiment N2 - Background: Artificial intelligence (AI) holds significant potential to support diagnostic decision-making; however, evidence regarding its clinical utility remains mixed. Often, the collaboration between clinicians and AI systems does not surpass the individual performance of unaided humans or standalone AI. Yet, currently, the mechanisms that limit human-AI synergy are poorly understood. This study examined the impact of AI advice on diagnostic decision-making among experts and novices, focusing on reliance patterns. Methods: We used a mixed-methods crossover experimental design with a think-aloud and an eye-tracking study arm. Participants were 50 task experts (radiologists) and 75 novices (non-radiologist physicians and medical trainees) from 10 countries. They reviewed 50 head CT scans and every case was examined in three time-separate sessions in randomized order. In each session, participants were exposed to different experimental conditions: (a) control, no AI prediction; (b) basic advice, AI prediction without annotations; and (c) XAI advice, AI prediction with scan annotations. For each case, participants had to determine if the patients had an intracranial hemorrhage (ICH). The main outcomes were diagnostic performance, confidence in the diagnosis, case reading time, and AI advice usefulness ratings. Findings: Both overreliance on incorrect advice and underreliance on correct advice occurred. Underreliance was associated with high uncertainty and, in absolute terms, had a more detrimental impact on diagnostic performance than overreliance. Correct XAI advice reduced underreliance, improved performance (OR=1·84, p<0·0001), and confidence (b=0·15, p<0·0001), particularly when reviewing more difficult cases with ICH. Surprisingly, correct XAI did not reduce reading time (b=1·81, p=0·0713). XAI was perceived as more useful than basic AI advice (b=0·12, p=0·0029), especially among novices. Interpretation: The occurrence of both under- and overreliance highlights the need for efficient counterstrategies beyond classic XAI methods to foster appropriate reliance and synergy. Y1 - 2026 U6 - https://doi.org/10.31219/osf.io/4wv8j_v3 ER -