TY - JOUR A1 - Maier, Robert A1 - Grabinger, Lisa A1 - Urlhart, David A1 - Mottok, Jürgen T1 - Causal Models to Support Scenario-Based Testing of ADAS JF - IEEE Transactions on Intelligent Transportation Systems N2 - 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. KW - automated driving systems KW - Automation KW - Bayesian networks KW - Causal inference KW - Data models KW - ISO Standards KW - model-based testing KW - Safety KW - Task analysis KW - Testing KW - Vehicles Y1 - 2023 U6 - https://doi.org/10.1109/TITS.2023.3317475 SN - 1524-9050 SP - 1 EP - 17 PB - IEEE ER - TY - CHAP A1 - Maier, Robert A1 - Mottok, Jürgen T1 - Causality and Functional Safety - How Causal Models Relate to the Automotive Standards ISO 26262, ISO/PAS 21448, and UL 4600 T2 - 27th International Conference on Applied Electronics (AE): 06-07 September 2022, Pilsen, CZ N2 - With autonomous driving, the system complexity of vehicles will increase drastically. This requires new approaches to ensure system safety. Looking at standards like ISO 26262 or ISO/PAS 21448 and their suggested methodologies, an increasing trend in the recent literature can be noticed to incorporate uncertainty. Often this is done by using Bayesian Networks as a framework to enable probabilistic reasoning. These models can also be used to represent causal relationships. Many publications claim to model cause-effect relations, yet rarely give a formal introduction of the implications and resulting possibilities such an approach may have. This paper aims to link the domains of causal reasoning and automotive system safety by investigating relations between causal models and approaches like FMEA, FTA, or GSN. First, the famous “Ladder of Causation” and its implications on causality are reviewed. Next, we give an informal overview of common hazard and reliability analysis techniques and associate them with probabilistic models. Finally, we analyse a mixed-model methodology called Hybrid Causal Logic, extend its idea, and build the concept of a causal shell model of automotive system safety. KW - Causality KW - Reliability KW - Functional Safety Y1 - 2022 U6 - https://doi.org/10.1109/AE54730.2022.9920053 PB - IEEE ER - TY - CHAP A1 - Kristen, Meret A1 - Engl, Fabian A1 - Mottok, Jürgen T1 - Enhancing Phishing Detection: An Eye-Tracking Study on User Interaction and Oversights in Phishing Emails T2 - SECURWARE 2024 : The Eighteenth International Conference on Emerging Security Information, Systems and Technologies, November 03-07, 2024, Nice, France N2 - Phishing remains a significant threat to organizational security, necessitating effective countermeasures. This paper presents findings from an in-depth eye-tracking study with 103 participants, evaluating the effectiveness of phishing awareness tools and trainings. The study examines how a phishing awareness system influences user behavior, efficiency, and the ability to identify phishing attempts. By analyzing eye movements, the study reveals real-time interactions and oversights, providing insights into the decision-making process. Results indicate that while the system improves the efficiency of users already proficient in phishing detection, it does not universally enhance recognition rates. Notably, participants using the tool spent significantly less time looking at attachment-related phishing markers, indicating partial efficiency improvements. Since phishing attempts containing suspicious attachments were successful in 19% of cases, as compared to an overall phishing success rate of 15%, the phishing awareness tool is particularly useful here. A usability evaluation revealed that users reporting a higher perceived usability score profited more from the help of the tool. Additionally, no improvement in phishing detection rates was observed in users who had completed prior IT-security training, highlighting the necessity for a paradigm shift in phishing training to adequately prepare users for phishing attempts. Y1 - 2024 UR - https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_80_30041.html SN - 978-1-68558-206-7 SP - 71 EP - 80 PB - IARIA ER - TY - JOUR A1 - Weber, Karsten A1 - Dendorfer, Sebastian A1 - Süß, Franz A1 - Kubowitsch, Simone A1 - Schratzenstaller, Thomas A1 - Haug, Sonja A1 - Mohr, Christa A1 - Kiesl, Hans A1 - Drechsler, Jörg A1 - Westner, Markus A1 - Kobus, Jörn A1 - Schubert, Martin J. W. A1 - Zenger, Stefan A1 - Pietsch, Alexander A1 - Weiß, Josef A1 - Hinterseer, Sebastian A1 - Schieck, Roland A1 - Scherzinger, Stefanie A1 - Klettke, Meike A1 - Ringlstetter, Andreas A1 - Störl, Uta A1 - Bissyandé, Tegawendé F. A1 - Seeburger, Achim A1 - Schindler, Timo A1 - Ramsauer, Ralf A1 - Kiszka, Jan A1 - Kölbl, Andreas A1 - Lohmann, Daniel A1 - Mauerer, Wolfgang A1 - Maier, Johannes A1 - Scorna, Ulrike A1 - Palm, Christoph A1 - Soska, Alexander A1 - Mottok, Jürgen A1 - Ellermeier, Andreas A1 - Vögele, Daniel A1 - Hierl, Stefan A1 - Briem, Ulrich A1 - Buschmann, Knut A1 - Ehrlich, Ingo A1 - Pongratz, Christian A1 - Pielmeier, Benjamin A1 - Tyroller, Quirin A1 - Monkman, Gareth J. A1 - Gut, Franz A1 - Roth, Carina A1 - Hausler, Peter A1 - Bierl, Rudolf A1 - Prommesberger, Christian A1 - Ławrowski, Robert Damian A1 - Langer, Christoph A1 - Schreiner, Rupert A1 - Huang, Yifeng A1 - She, Juncong A1 - Ottl, Andreas A1 - Rieger, Walter A1 - Kraml, Agnes A1 - Poxleitner, Thomas A1 - Hofer, Simon A1 - Heisterkamp, Benjamin A1 - Lerch, Maximilian A1 - Sammer, Nike A1 - Golde, Olivia A1 - Wellnitz, Felix A1 - Schmid, Sandra A1 - Muntschick, Claudia A1 - Kusterle, Wolfgang A1 - Paric, Ivan A1 - Brückl, Oliver A1 - Haslbeck, Matthias A1 - Schmidt, Ottfried A1 - Schwanzer, Peter A1 - Rabl, Hans-Peter A1 - Sterner, Michael A1 - Bauer, Franz A1 - Steinmann, Sven A1 - Eckert, Fabian A1 - Hofrichter, Andreas ED - Baier, Wolfgang T1 - Forschungsbericht 2017 T3 - Forschungsberichte der OTH Regensburg - 2017 KW - Forschung KW - Forschungsbericht Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-13835 SN - 978-3-9818209-3-5 CY - Regensburg ER - TY - CHAP A1 - Staufer, Susanne A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Bittner, Dominik A1 - Nadimpalli, Vamsi Krishna A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Learning elements in online learning management systems T2 - Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-67725 SP - 3121 EP - 3130 PB - IATED ER - TY - CHAP A1 - Staufer, Susanne A1 - Nadimpalli, Vamsi Krishna A1 - Hauser, Florian A1 - Bittner, Dominik A1 - Grabinger, Lisa A1 - Bugert, Flemming A1 - Ezer, Timur A1 - Röhrl, Simon A1 - Mottok, Jürgen T1 - Persistence of Questionnaire Data on Learning Styles, Learning Strategies AND Personality Traits T2 - ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain N2 - In the context of learning, questionnaires are carried out to gain information about learners. This learner profile has various benefits, like adapting the learning content, promoting of self-reflection, or increasing motivation in learning and therefore better learning results. Some researchers take the results of such questionnaires as ground truth or trust them blindly. Examining how persistent questionnaires are, asking the same questions twice, and comparing the results is a way to prove or falsify this statement. Our research procedure compares the results of three different questionnaires. The first questionnaire is the ILS (index of learning styles) according to the Felder-Silverman Learning Style Model (FSLSM) with about 44 questions. Learning strategies are examined with the LIST-K questionnaire consisting of 39 questions after Klingsieck. The third and last questionnaire is about personality traits. With only ten questions, Rammstedt et al. describe the BFI-10 questionnaire for personality traits. To compare results, the same study with the three named questionnaires is carried out twice with three to four months in between. The sample includes 35 students in their bachelor studies taking the course Software Engineering in a blended learning format. After collecting the questionnaire data from the students, a comparison is made in two different ways. First, we compare the data points based on the given answers. Second, a comparison is made based on the calculated questionnaire results because each of the three questionnaires has its own analysis method. For example, the analysis method of the ILS delivers as a result four integers representing the four characteristics of a learning style, while LIST-K makes a factor analysis. The results of the paper show that both the given answers and the calculated questionnaire results differ. Not one student has given the same answer inside each questionnaire with causes, like a change of learning style, learning strategy, and personality traits, or not filling in conscientiously. KW - learning style KW - learning strategy KW - personality traits KW - questionnaire study Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-78246 SN - 978-84-09-63010-3 SN - 2340-1095 SP - 6310 EP - 6319 PB - IATED ER - TY - JOUR A1 - Röhrl, Simon A1 - Staufer, Susanne A1 - Bugert, Flemming A1 - Nadimpalli, Vamsi Krishna A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Bittner, Dominik A1 - Ezer, Timur A1 - Altweck, Ricardo A1 - Graf, Sabine A1 - Kriza, Thomas A1 - Mottok, Jürgen T1 - Ethical Considerations of AI in Education: A Case Study based on Pythia Learning Enhancement System JF - IEEE Access N2 - As the use of AI in education increases, addressing ethical concerns is essential for its responsible use and implementation. This study explores the ethical considerations of AI in education by analyzing a concrete application, the Pythia Learning Enhancement System, which enables personalized and adaptive educational experiences. In this case study, the concrete implementation of Pythia, contrary to high-level abstract analyses, provides practical insights into these ethical issues. To achieve this goal, we first define Pythia and outline its implementation. Using the various topics covered by Pythia, the ethical issues associated with each of these topics are outlined, resulting in a list of ten distinct ethical issues. Because there is overlap in issues covered by similar applications, they can benefit from this identification. An ethical (human rights) and didactic (constructivism) theoretical background is then provided as a basis for further discussion. This is used to build the arguments used in the main study. This consists of analyzing each of the issues and providing recommendations to mitigate potential harm arising from these issues. This helps to provide guidelines for future applications. In the analysis, the ethical discussion is mainly based on the human rights of the stakeholders involved, emphasizing human dignity. After the analysis, each of the issues is contextualized with the recent AI Act of the European Union, providing a novel mapping of the issues of AI in education to the AI Act. Finally, the limitations of the study are stated and a plan for future research is presented. KW - Ethics KW - Artificial intelligence KW - Education KW - Stakeholders KW - Psychology KW - Privacy KW - Search problems KW - Guidelines KW - Focusing KW - Europe Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-83403 SN - 2169-3536 N1 - Corresponding author der OTH Regensburg: Simon Röhrl VL - 11 SP - 1 EP - 25 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - RPRT A1 - Taiber, Joachim A1 - Chowdury, M. A1 - Hammond, G. A1 - Hipp, V. A1 - Huttner, B. A1 - Lonien, J. A1 - Mottok, Jürgen A1 - Neukart, Florian A1 - Stengel, T. T1 - Unsettled Topics Concerning the Impact of Quantum Technologies on Automotive Cybersecurity N2 - Quantum computing is considered the “next big thing” when it comes to solving computational problems impossible to tackle using conventional computers. However, a major concern is that quantum computers could be used to crack current cryptographic schemes designed to withstand traditional cyberattacks. This threat also impacts future automated vehicles as they become embedded in a vehicle-to-everything (V2X) ecosystem. In this scenario, encrypted data is transmitted between a complex network of cloud-based data servers, vehicle-based data servers, and vehicle sensors and controllers. While the vehicle hardware ages, the software enabling V2X interactions will be updated multiple times. It is essential to make the V2X ecosystem quantum-safe through use of “post-quantum cryptography” as well other applicable quantum technologies. This SAE EDGE™ Research Report considers the following three areas to be unsettled questions in the V2X ecosystem: How soon will quantum computing pose a threat to connected and automated vehicle technologies? What steps and measures are needed to make a V2X ecosystem “quantum-safe?” What standardization is needed to ensure that quantum technologies do not pose an unacceptable risk from an automotive cybersecurity perspective? Y1 - 2020 U6 - https://doi.org/10.4271/EPR2020026 SN - 2640-3536 CY - Warrendale, Pennsylvania, USA ER - TY - CHAP A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Ezer, Timur A1 - Mottok, Jürgen A1 - Gruber, Hans ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Integrating deliberate practice in software engineering education T2 - ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain Y1 - 2024 U6 - https://doi.org/10.21125/iceri.2024.1331 SP - 5457 EP - 5466 PB - IATED ER - TY - GEN A1 - Stark, T. A1 - Schreistetter, Stefan A1 - Reuter, Rebecca A1 - Hauser, Florian A1 - Holmqvist, Kenneth A1 - Mottok, Jürgen A1 - Gruber, Hans T1 - Learning from Gaze: Eye Movement Modeling Examples in Software Engineering Education T2 - Earli Book of Abstracts Y1 - 2021 UR - https://earli.org/assets/files/BOA-2021.pdf SP - 291 PB - Earli ER - TY - CHAP A1 - De, Sangita A1 - Niklas, Michael A1 - Brada, Premek A1 - Mottok, Jürgen T1 - Model Transformation of Application Software Component from Classic to Adaptive AUTOSAR: An Approach to Migrate Software Components T2 - SEAA 2018, the 44th EUROMICRO Conference on Software Engineering and Advanced Applications, August 29 - 31 2018, Prague, Czech Republic Y1 - 2018 SN - 978-3-902457-50-9 ER - TY - CHAP A1 - Bugert, Flemming A1 - Staufer, Susanne A1 - Bittner, Dominik A1 - Nadimpalli, Vamsi Krishna A1 - Ezer, Timur A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Mottok, Jürgen T1 - Ariadne's Thread for Unravelling Learning Paths: Identifying Learning Styles via Hidden Markov Models T2 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece N2 - Modern education through Learning Management Systems (LMSs) provides learners with personalized learning paths. This is achieved by first querying the learning style according to the theory of Felder and Silverman to recommend suitable learning content. However, a rigid learning style representation is lacking of adaptability to the learners' choices. Therefore, the present study evaluates the idea of providing adaption to the representation of learning styles by using Hidden Markov Models (HMMs). Thus, data is collected from participants out of the Higher Education Area. The Index of Learning Styles questionnaire is used to obtain the learning style based on the theory of Felder and Silverman. Also, a questionnaire that asks the respondents to create a preferred learning path with the sequence length of nine learning elements is provided. From the given data, we initially evaluate the probability relationships between learning styles and learning elements. Then, we use the Viterbi algorithm in HMMs to identify alterations in learning styles from the provided learning paths. The alignment is then quantified by introducing a metric called support value. The findings imply that our concept can be used to adapt the learning style based on the user's real choice of learning elements. Thus, the proposed model also offers a way to integrate a feedback loop within LMSs leading to an improvement of learning path recommendation algorithms. KW - Surveys KW - Knowledge engineering KW - Feedback loop KW - Learning management systems KW - Uncertainty KW - Viterbi algorithm Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578825 SN - 2165-9567 SP - 1 EP - 7 PB - IEEE ER - TY - CHAP A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Mottok, Jürgen A1 - Jahn, Sabrina A1 - Nadimpalli, Vamsi Krishna T1 - The Expert’s View: Eye Movement Modeling Examples in Software Engineering Education T2 - Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023 N2 - 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. Y1 - 2023 SN - 978-1-4503-9956-2 U6 - https://doi.org/10.1145/3593663.3593683 SP - 148 EP - 152 PB - ACM ER - TY - CHAP A1 - Bugert, Flemming A1 - Grabinger, Lisa A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Nadimpalli, Vamsi Krishna A1 - Staufer, Susanne A1 - Mottok, Jürgen T1 - Towards Learning Style Prediction based on Personality T2 - Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023 N2 - 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. Y1 - 2023 SN - 978-1-4503-9956-2 U6 - https://doi.org/10.1145/3593663.3593682 SP - 48 EP - 55 PB - ACM ER - TY - CHAP A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Nadimpalli, Vamsi Krishna A1 - Grabinger, Lisa A1 - Staufer, Susanne A1 - Mottok, Jürgen T1 - Towards Eye Tracking based Learning Style Identification T2 - Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023 N2 - 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. Y1 - 2023 SN - 978-1-4503-9956-2 U6 - https://doi.org/10.1145/3593663.3593680 SP - 138 EP - 147 PB - ACM ER - TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Staufer, Susanne A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Towards personalized learning paths in adaptive learning management systems: bayesian modelling of psychological theories T2 - Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023 N2 - 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. 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. 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. Y1 - 2023 U6 - https://doi.org/10.21125/iceri.2023.1144 SP - 4593 EP - 4603 PB - IATED ER - TY - CHAP A1 - Ezer, Timur A1 - Plößl, Moritz A1 - Grabinger, Lisa A1 - Bittner, Dominik A1 - Staufer, Susanne A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Hauser, Florian A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Deep learning for eye movement classification T2 - ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain Y1 - 2024 U6 - https://doi.org/10.21125/iceri.2024.1028 SP - 4056 EP - 4065 PB - IATED ER - TY - JOUR A1 - Broser, Christian A1 - Falter, Thomas A1 - Ławrowski, Robert Damian A1 - Altenbuchner, Amelie A1 - Vögele, Daniel A1 - Koss, Claus A1 - Schlamp, Matthias A1 - Dunnweber, Jan A1 - Steffens, Oliver A1 - Heckner, Markus A1 - Jaritz, Sabine A1 - Schiegl, Thomas A1 - Corsten, Sabine A1 - Lauer, Norina A1 - Guertler, Katherine A1 - Koenig, Eric A1 - Haug, Sonja A1 - Huber, Dominik A1 - Birkenmaier, Clemens A1 - Krenkel, Lars A1 - Wagner, Thomas A1 - Justus, Xenia A1 - Saßmannshausen, Sean Patrick A1 - Kleine, Nadine A1 - Weber, Karsten A1 - Braun, Carina N. A1 - Giacoppo, Giuliano A1 - Heinrich, Michael A1 - Just, Tobias A1 - Schreck, Thomas A1 - Schnabl, Andreas A1 - Gilmore, Amador Téran A1 - Roeslin, Samuel A1 - Schmid, Sandra A1 - Wellnitz, Felix A1 - Malz, Sebastian A1 - Maurial, Andreas A1 - Hauser, Florian A1 - Mottok, Jürgen A1 - Klettke, Meike A1 - Scherzinger, Stefanie A1 - Störl, Uta A1 - Heckner, Markus A1 - Bazo, Alexander A1 - Wolff, Christian A1 - Kopper, Andreas A1 - Westner, Markus A1 - Pongratz, Christian A1 - Ehrlich, Ingo A1 - Briem, Ulrich A1 - Hederer, Sebastian A1 - Wagner, Marcus A1 - Schillinger, Moritz A1 - Görlach, Julien A1 - Hierl, Stefan A1 - Siegl, Marco A1 - Langer, Christoph A1 - Hausladen, Matthias A1 - Schreiner, Rupert A1 - Haslbeck, Matthias A1 - Kreuzer, Reinhard A1 - Brückl, Oliver A1 - Dawoud, Belal A1 - Rabl, Hans-Peter A1 - Gamisch, Bernd A1 - Schmidt, Ottfried A1 - Heberl, Michael A1 - Gänsbauer, Bianca A1 - Bick, Werner A1 - Ellermeier, Andreas A1 - Monkman, Gareth J. A1 - Prem, Nina A1 - Sindersberger, Dirk A1 - Tschurtschenthaler, Karl A1 - Aurbach, Maximilian A1 - Dendorfer, Sebastian A1 - Betz, Michael A. A1 - Szecsey, Tamara A1 - Mauerer, Wolfgang A1 - Murr, Florian ED - Baier, Wolfgang T1 - Forschung 2018 T3 - Forschungsberichte der OTH Regensburg - 2018 KW - Forschung KW - Forschungsbericht Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-13826 SN - 978-3-9818209-5-9 CY - Regensburg 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 - Underreliance Harms Human-AI Collaboration More Than Overreliance in Medical Imaging N2 - Importance: The use of artificial intelligence (AI) to support clinicians in diagnostic decision-making holds significant potential; however, evidence regarding its clinical utility remains mixed. In many cases, the interaction between healthcare professionals and AI systems does not improve collaborative performance compared to the standalone performance of humans or AI. Currently, the underlying mechanisms that limit human-AI collaboration are poorly understood. Objective: To examine the impact of AI advice on diagnostic decision-making among experts and novices, focusing on understanding the role of explainability (XAI) on users’ reliance on advice. Design, Setting, and Participants: A mixed-methods design combining a crossover experimental design with a think-aloud and an eye-tracking study arm was conducted in 2023. Participants were task experts (radiologists) and novices (non-radiologist physicians and medical trainees) from 10 countries, with the think-aloud and eye-tracking conducted in Germany. Intervention: Participants reviewed 50 patient cases containing head CT scans and patient information. Every case was reviewed in three time-separate sessions in randomized order. In each session, participants were exposed to a different experimental condition: (a) control, i.e., no AI prediction presented; (b) basic advice, i.e., AI prediction without annotations; and (c) XAI advice, i.e., AI prediction with annotations. For each case, participants had to determine if the patients had an intracranial hemorrhage (ICH), rate their confidence, and, if applicable, the usefulness of the AI advice. Main Outcome(s) and Measure(s): Diagnostic performance, confidence in the diagnosis, case reading time, and AI advice usefulness ratings. Results: The data analysis included 125 participants. The mean age was 28.5 years (SD = 6.72), and 55.2% identified as female. Underreliance on correct AI advice was associated with high uncertainty and had a more detrimental impact on diagnostic performance than overreliance on incorrect advice. XAI advice reduced underreliance and improved performance and confidence, particularly when reviewing more difficult cases with ICH. AI advice, particularly XAI, did not reduce reading time. XAI was perceived as more useful than basic AI advice, especially among novices. Conclusions and Relevance: Our findings indicate that underreliance on AI might be more harmful than overreliance, highlighting the need to develop efficient counterstrategies beyond current XAI methods. Y1 - 2024 U6 - https://doi.org/10.31219/osf.io/4wv8j PB - OSFPreprints ER - TY - CHAP A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Engl, Fabian A1 - Mottok, Jürgen ED - Mottok, Jürgen ED - Hagel, Georg T1 - Eye Movement Modelling Examples on Usability Heuristics T2 - Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025 N2 - The user interface (UI) and user experience (UX) design is of crucial importance for human-computer interaction (HCI), particularly in the context of web applications. In light of the high expectations of users and the competitive nature of the market, it is imperative to employ usability measurement techniques to avoid losing users. Heuristic evaluation (HE) is a cost- and resource-efficient method for evaluating the usability of websites in which evaluators are guided by heuristics. However, the level of expertise of the evaluators has a significant impact on the results, with experts identifying up to 50% more usability issues than novices. To address this gap, this paper proposes Eye Movement Modeling Examples (EMMEs) to demonstrate Jakob Nielsen’s ten usability heuristics in an easy-to-understand format for all levels of experience while also incorporating expert knowledge. In particular, the eye movements and verbal feedback of a usability expert are recorded as the expert analyses the usability of a simple website application in terms of Jakob Nielsen’s ten usability heuristics. This reveals the strategies and cognitive processes of the expert when assessing the usability of a website and makes them more tangible for non-experts or novices. The findings of a questionnaire-based assessment indicate that EMMEs are perceived as beneficial and supportive during the learning process. Ultimately, this comprehensive analysis not only enables a deeper understanding of heuristics for usability novices, but could also lead to EMMEs being applied more efficiently in diverse domains. Y1 - 2025 SN - 9798400712821 U6 - https://doi.org/10.1145/3723010.3723035 SP - 106 EP - 114 PB - ACM ER - TY - JOUR A1 - Rück, Thomas A1 - Bierl, Rudolf A1 - Lechner, Alfred A1 - Graf, Antonia A1 - Dams, Florian A1 - Schreiner, Rupert A1 - Auchter, Eberhard A1 - Kriz, Willy A1 - Deubzer, MIchael A1 - Schiller, Frank A1 - Mottok, Jürgen A1 - Niemetz, Michael A1 - Margull, Ulrich A1 - Hagel, Georg A1 - Utesch, Matthias A1 - Waldherr, Franz A1 - Böhm, Matthias A1 - Fraunhoffer, Judith A1 - Gardeia, Armin A1 - Schneider, Ralph A1 - Streubel, Janet A1 - Landes, Dieter A1 - Studt, Reimer A1 - Peuker, Dominik A1 - Scharfenberg, Georg A1 - Hook, Christian A1 - Schuster, Dietwald A1 - Ehrlich, Ingo A1 - Dinnebier, Heinrich A1 - Briem, Ulrich A1 - Lämmlein, Stephan A1 - Koder, Alexander A1 - Bialek, Adam A1 - Genewsky, Axel A1 - Neumeier, Michael A1 - Schlosser, Philipp A1 - Rabl, Hans-Peter A1 - Paule, Matthias A1 - Galster, Christoph A1 - Schiedermeier, Michael A1 - Zwickel, Andreas A1 - Hobmeier, Christoph A1 - Bischoff, Tobias A1 - Rill, Georg A1 - Schaeffer, Thomas A1 - Arbesmeier, Martin A1 - Groß, Andreas A1 - Schlegl, Thomas A1 - Becker, Mark A1 - Senn, Konrad A1 - Schliekmann, Claus A1 - Scholz, Peter A1 - Sippl, Christian A1 - Grill, Martin ED - Eckstein, Josef T1 - Forschungsbericht 2011 / Hochschule für Angewandte Wissenschaften - Fachhochschule Regensburg T3 - Forschungsberichte der OTH Regensburg - 2011 Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-7321 SN - 1868-3533 CY - Regensburg ER - TY - CHAP A1 - Hauser, Florian A1 - Staufer, Susanne A1 - Grabinger, Lisa A1 - Röhrl, Simon A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - On the analysis of student learning strategies: using the LIST-K questionnaire ro generate ai-based individualized learning paths T2 - Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023 N2 - 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. 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. 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. 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. 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). 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. Y1 - 2023 U6 - https://doi.org/10.21125/iceri.2023.1147 SP - 4611 EP - 4620 PB - IATED ER - TY - CHAP A1 - Bugert, Flemming A1 - Nadimpalli, Vamsi Krishna A1 - Bittner, Dominik A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Maier, Robert A1 - Röhrl, Simon A1 - Staufer, Susanne A1 - Hauser, Florian A1 - Mottok, Jürgen ED - Mottok, Jürgen ED - Hagel, Georg T1 - ML based Evaluation Methodology for Learning Path Recommender Systems T2 - Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025 N2 - In education, recommender systems can provide students with personalized learning materials based on their preferences. When comparing various recommendation algorithms, the main question is, which algorithm provides the most suitable recommendations for each student. Answering this question requires a quantitative evaluation methodology (i.e. a concrete metric) for ranking the results of (even non-deterministic) recommender systems. While there is already literature on this topic, the uniqueness of our approach lies in the application of machine learning: we deploy a likelihood based analysis via Hidden Markov Models named Aiakos. With this strategy, we aim to provide data-driven insights about accuracy and stability of recommendations towards a more reasonable selection of the appropriate recommender system. The training data for the Hidden Markov Models is collected from 80 students. Data from another 26 students is then used to discuss the behavior of our evaluation procedure considering a single recommendation as well as the results from 100 recommendations. Furthermore, the proposed concept allows to be applied to other domains as well. Y1 - 2025 SN - 9798400712821 U6 - https://doi.org/10.1145/3723010.3723022 SP - 40 EP - 48 PB - ACM ER - 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 -