@inproceedings{StauferNadimpalliHauseretal., author = {Staufer, Susanne and Nadimpalli, Vamsi Krishna and Hauser, Florian and Bittner, Dominik and Grabinger, Lisa and Bugert, Flemming and Ezer, Timur and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {Persistence of Questionnaire Data on Learning Styles, Learning Strategies AND Personality Traits}, series = {ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain}, booktitle = {ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain}, publisher = {IATED}, isbn = {978-84-09-63010-3}, issn = {2340-1095}, doi = {10.21125/iceri.2024.1524}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-78246}, pages = {6310 -- 6319}, abstract = {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.}, language = {en} } @article{RoehrlStauferBugertetal., author = {R{\"o}hrl, Simon and Staufer, Susanne and Bugert, Flemming and Nadimpalli, Vamsi Krishna and Hauser, Florian and Grabinger, Lisa and Bittner, Dominik and Ezer, Timur and Altweck, Ricardo and Graf, Sabine and Kriza, Thomas and Mottok, J{\"u}rgen}, title = {Ethical Considerations of AI in Education: A Case Study based on Pythia Learning Enhancement System}, series = {IEEE Access}, volume = {11}, journal = {IEEE Access}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {2169-3536}, doi = {10.1109/ACCESS.2025.3583975}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-83403}, pages = {1 -- 25}, abstract = {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.}, language = {en} } @techreport{TaiberChowduryHammondetal., author = {Taiber, Joachim and Chowdury, M. and Hammond, G. and Hipp, V. and Huttner, B. and Lonien, J. and Mottok, J{\"u}rgen and Neukart, Florian and Stengel, T.}, title = {Unsettled Topics Concerning the Impact of Quantum Technologies on Automotive Cybersecurity}, address = {Warrendale, Pennsylvania, USA}, issn = {2640-3536}, doi = {10.4271/EPR2020026}, abstract = {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?}, language = {en} } @incollection{HauserGrabingerEzeretal., author = {Hauser, Florian and Grabinger, Lisa and Ezer, Timur and Mottok, J{\"u}rgen and Gruber, Hans}, title = {Integrating deliberate practice in software engineering education}, series = {ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain}, booktitle = {ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2024.1331}, pages = {5457 -- 5466}, language = {en} } @misc{StarkSchreistetterReuteretal., author = {Stark, T. and Schreistetter, Stefan and Reuter, Rebecca and Hauser, Florian and Holmqvist, Kenneth and Mottok, J{\"u}rgen and Gruber, Hans}, title = {Learning from Gaze: Eye Movement Modeling Examples in Software Engineering Education}, series = {Earli Book of Abstracts}, journal = {Earli Book of Abstracts}, publisher = {Earli}, pages = {291}, language = {en} } @inproceedings{DeNiklasBradaetal., author = {De, Sangita and Niklas, Michael and Brada, Premek and Mottok, J{\"u}rgen}, title = {Model Transformation of Application Software Component from Classic to Adaptive AUTOSAR: An Approach to Migrate Software Components}, series = {SEAA 2018, the 44th EUROMICRO Conference on Software Engineering and Advanced Applications, August 29 - 31 2018, Prague, Czech Republic}, booktitle = {SEAA 2018, the 44th EUROMICRO Conference on Software Engineering and Advanced Applications, August 29 - 31 2018, Prague, Czech Republic}, isbn = {978-3-902457-50-9}, language = {en} } @inproceedings{BugertStauferBittneretal., author = {Bugert, Flemming and Staufer, Susanne and Bittner, Dominik and Nadimpalli, Vamsi Krishna and Ezer, Timur and Hauser, Florian and Grabinger, Lisa and Mottok, J{\"u}rgen}, title = {Ariadne's Thread for Unravelling Learning Paths: Identifying Learning Styles via Hidden Markov Models}, series = {2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece}, booktitle = {2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece}, publisher = {IEEE}, issn = {2165-9567}, doi = {10.1109/EDUCON60312.2024.10578825}, pages = {1 -- 7}, abstract = {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.}, language = {en} } @inproceedings{HauserGrabingerMottoketal., author = {Hauser, Florian and Grabinger, Lisa and Mottok, J{\"u}rgen and Jahn, Sabrina and Nadimpalli, Vamsi Krishna}, title = {The Expert's View: Eye Movement Modeling Examples in Software Engineering Education}, series = {Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, booktitle = {Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, publisher = {ACM}, isbn = {978-1-4503-9956-2}, doi = {10.1145/3593663.3593683}, pages = {148 -- 152}, abstract = {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.}, language = {en} } @inproceedings{BugertGrabingerBittneretal., author = {Bugert, Flemming and Grabinger, Lisa and Bittner, Dominik and Hauser, Florian and Nadimpalli, Vamsi Krishna and Staufer, Susanne and Mottok, J{\"u}rgen}, title = {Towards Learning Style Prediction based on Personality}, series = {Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, booktitle = {Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, publisher = {ACM}, isbn = {978-1-4503-9956-2}, doi = {10.1145/3593663.3593682}, pages = {48 -- 55}, abstract = {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.}, language = {en} } @inproceedings{BittnerHauserNadimpallietal., author = {Bittner, Dominik and Hauser, Florian and Nadimpalli, Vamsi Krishna and Grabinger, Lisa and Staufer, Susanne and Mottok, J{\"u}rgen}, title = {Towards Eye Tracking based Learning Style Identification}, series = {Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, booktitle = {Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, publisher = {ACM}, isbn = {978-1-4503-9956-2}, doi = {10.1145/3593663.3593680}, pages = {138 -- 147}, abstract = {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.}, language = {en} } @inproceedings{NadimpalliBugertBittneretal., author = {Nadimpalli, Vamsi Krishna and Bugert, Flemming and Bittner, Dominik and Hauser, Florian and Grabinger, Lisa and Staufer, Susanne and Mottok, J{\"u}rgen}, title = {Towards personalized learning paths in adaptive learning management systems: bayesian modelling of psychological theories}, series = {Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023}, booktitle = {Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2023.1144}, pages = {4593 -- 4603}, abstract = {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.}, language = {en} } @inproceedings{EzerPloesslGrabingeretal., author = {Ezer, Timur and Pl{\"o}ßl, Moritz and Grabinger, Lisa and Bittner, Dominik and Staufer, Susanne and Nadimpalli, Vamsi Krishna and Bugert, Flemming and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Deep learning for eye movement classification}, series = {ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain}, booktitle = {ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2024.1028}, pages = {4056 -- 4065}, language = {en} } @misc{BroserFalterŁawrowskietal., author = {Broser, Christian and Falter, Thomas and Ławrowski, Robert Damian and Altenbuchner, Amelie and V{\"o}gele, Daniel and Koss, Claus and Schlamp, Matthias and Dunnweber, Jan and Steffens, Oliver and Heckner, Markus and Jaritz, Sabine and Schiegl, Thomas and Corsten, Sabine and Lauer, Norina and Guertler, Katherine and Koenig, Eric and Haug, Sonja and Huber, Dominik and Birkenmaier, Clemens and Krenkel, Lars and Wagner, Thomas and Justus, Xenia and Saßmannshausen, Sean Patrick and Kleine, Nadine and Weber, Karsten and Braun, Carina N. and Giacoppo, Giuliano and Heinrich, Michael and Just, Tobias and Schreck, Thomas and Schnabl, Andreas and Gilmore, Amador T{\´e}ran and Roeslin, Samuel and Schmid, Sandra and Wellnitz, Felix and Malz, Sebastian and Maurial, Andreas and Hauser, Florian and Mottok, J{\"u}rgen and Klettke, Meike and Scherzinger, Stefanie and St{\"o}rl, Uta and Heckner, Markus and Bazo, Alexander and Wolff, Christian and Kopper, Andreas and Westner, Markus and Pongratz, Christian and Ehrlich, Ingo and Briem, Ulrich and Hederer, Sebastian and Wagner, Marcus and Schillinger, Moritz and G{\"o}rlach, Julien and Hierl, Stefan and Siegl, Marco and Langer, Christoph and Hausladen, Matthias and Schreiner, Rupert and Haslbeck, Matthias and Kreuzer, Reinhard and Br{\"u}ckl, Oliver and Dawoud, Belal and Rabl, Hans-Peter and Gamisch, Bernd and Schmidt, Ottfried and Heberl, Michael and G{\"a}nsbauer, Bianca and Bick, Werner and Ellermeier, Andreas and Monkman, Gareth J. and Prem, Nina and Sindersberger, Dirk and Tschurtschenthaler, Karl and Aurbach, Maximilian and Dendorfer, Sebastian and Betz, Michael A. and Szecsey, Tamara and Mauerer, Wolfgang and Murr, Florian}, title = {Forschung 2018}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-9818209-5-9}, doi = {10.35096/othr/pub-1382}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13826}, pages = {98}, subject = {Forschung}, language = {de} } @unpublished{GaubeJussupowKokjeetal., author = {Gaube, Susanne and Jussupow, Ekaterina and Kokje, Eesha and Khan, Jowaria and Bondi-Kelly, Elizabeth and Schicho, Andreas and Kitamura, Felipe Campos and Koch, Timo Kevin and Ezer, Timur and Mottok, J{\"u}rgen and Lermer, Eva and Ghassemi, Marzyeh and Colak, Errol}, title = {Underreliance Harms Human-AI Collaboration More Than Overreliance in Medical Imaging}, publisher = {OSFPreprints}, doi = {10.31219/osf.io/4wv8j}, abstract = {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.}, language = {en} } @inproceedings{BittnerHauserEngletal., author = {Bittner, Dominik and Hauser, Florian and Engl, Fabian and Mottok, J{\"u}rgen}, title = {Eye Movement Modelling Examples on Usability Heuristics}, series = {Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025}, booktitle = {Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025}, editor = {Mottok, J{\"u}rgen and Hagel, Georg}, publisher = {ACM}, isbn = {9798400712821}, doi = {10.1145/3723010.3723035}, pages = {106 -- 114}, abstract = {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.}, language = {en} } @misc{RueckBierlLechneretal., author = {R{\"u}ck, Thomas and Bierl, Rudolf and Lechner, Alfred and Graf, Antonia and Dams, Florian and Schreiner, Rupert and Auchter, Eberhard and Kriz, Willy and Deubzer, MIchael and Schiller, Frank and Mottok, J{\"u}rgen and Niemetz, Michael and Margull, Ulrich and Hagel, Georg and Utesch, Matthias and Waldherr, Franz and B{\"o}hm, Matthias and Fraunhoffer, Judith and Gardeia, Armin and Schneider, Ralph and Streubel, Janet and Landes, Dieter and Studt, Reimer and Peuker, Dominik and Scharfenberg, Georg and Hook, Christian and Schuster, Dietwald and Ehrlich, Ingo and Dinnebier, Heinrich and Briem, Ulrich and L{\"a}mmlein, Stephan and Koder, Alexander and Bialek, Adam and Genewsky, Axel and Neumeier, Michael and Schlosser, Philipp and Rabl, Hans-Peter and Paule, Matthias and Galster, Christoph and Schiedermeier, Michael and Zwickel, Andreas and Hobmeier, Christoph and Bischoff, Tobias and Rill, Georg and Schaeffer, Thomas and Arbesmeier, Martin and Groß, Andreas and Schlegl, Thomas and Becker, Mark and Senn, Konrad and Schliekmann, Claus and Scholz, Peter and Sippl, Christian and Grill, Martin}, title = {Forschungsbericht 2011 / Hochschule f{\"u}r Angewandte Wissenschaften - Fachhochschule Regensburg}, editor = {Eckstein, Josef}, address = {Regensburg}, organization = {Hochschule f{\"u}r Angewandte Wissenschaften Regensburg}, issn = {1868-3533}, doi = {10.35096/othr/pub-732}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-7321}, language = {de} } @inproceedings{HauserStauferGrabingeretal., author = {Hauser, Florian and Staufer, Susanne and Grabinger, Lisa and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {On the analysis of student learning strategies: using the LIST-K questionnaire ro generate ai-based individualized learning paths}, series = {Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023}, booktitle = {Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2023.1147}, pages = {4611 -- 4620}, abstract = {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.}, language = {en} } @inproceedings{BugertNadimpalliBittneretal., author = {Bugert, Flemming and Nadimpalli, Vamsi Krishna and Bittner, Dominik and Ezer, Timur and Grabinger, Lisa and Maier, Robert and R{\"o}hrl, Simon and Staufer, Susanne and Hauser, Florian and Mottok, J{\"u}rgen}, title = {ML based Evaluation Methodology for Learning Path Recommender Systems}, series = {Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025}, booktitle = {Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025}, editor = {Mottok, J{\"u}rgen and Hagel, Georg}, publisher = {ACM}, isbn = {9798400712821}, doi = {10.1145/3723010.3723022}, pages = {40 -- 48}, abstract = {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.}, language = {en} } @inproceedings{NadimpalliBugertBittneretal., author = {Nadimpalli, Vamsi Krishna and Bugert, Flemming and Bittner, Dominik and Staufer, Susanne and R{\"o}hrl, Simon and Hauser, Florian and Ezer, Timur and Grabinger, Lisa and Maier, Robert and Mottok, J{\"u}rgen}, title = {Probabilistic Machine Learning for Simulating Complex Learner Profiles}, series = {2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), 06-08 November 2024, Paris, France}, booktitle = {2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), 06-08 November 2024, Paris, France}, publisher = {IEEE}, isbn = {979-8-3315-1663-5}, issn = {2473-2060}, doi = {10.1109/ITHET61869.2024.10837641}, pages = {1 -- 10}, abstract = {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 {\"u}bernehmen] 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.}, language = {en} } @inproceedings{NadimpalliStauferEzeretal., author = {Nadimpalli, Vamsi Krishna and Staufer, Susanne and Ezer, Timur and Bugert, Flemming and Bittner, Dominik and Hauser, Florian and Grabinger, Lisa and R{\"o}hrl, Simon and Maier, Robert and Mottok, J{\"u}rgen}, title = {Predicting learner characteristics using machine learning}, series = {ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain}, booktitle = {ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2024.1454}, pages = {6005 -- 6014}, abstract = {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}, language = {en} } @inproceedings{HammerMottok, author = {Hammer, Pascal and Mottok, J{\"u}rgen}, title = {Prototyping for high-performance random number generators for critical systems}, series = {2025 International Conference on Applied Electronics (AE), 08-09. September 2025, Pilsen Czech Republic}, booktitle = {2025 International Conference on Applied Electronics (AE), 08-09. September 2025, Pilsen Czech Republic}, publisher = {IEEE}, doi = {10.1109/AE66163.2025.11197780}, pages = {7}, abstract = {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.}, language = {en} } @inproceedings{StauferBugertHauseretal., author = {Staufer, Susanne and Bugert, Flemming and Hauser, Florian and Grabinger, Lisa and Ezer, Timur and Nadimpalli, Vamsi Krishna and Bittner, Dominik and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {Tyche algorithm: Markov models for generating learning paths in learning management systems}, series = {INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024}, booktitle = {INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED Academy}, isbn = {978-84-09-59215-9}, issn = {2340-1079}, doi = {10.21125/inted.2024.1080}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-71148}, pages = {4195 -- 4205}, abstract = {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.}, language = {en} } @inproceedings{StauferEzerRoehrletal., author = {Staufer, Susanne and Ezer, Timur and R{\"o}hrl, Simon and Grabinger, Lisa and Hauser, Florian and Nadimpalli, Vamsi Krishna and Antoni, Erika and Mottok, J{\"u}rgen and Schaffer, Josefa}, title = {TYCHE ALGORITHM 2.0: Learning Paths from Questionnaire Responses and Learning Analytics}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, publisher = {IATED}, isbn = {978-84-09-78706-7}, doi = {10.21125/iceri.2025.0831}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-87846}, pages = {2562 -- 2572}, abstract = {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.}, language = {en} } @inproceedings{HauserStauferRoehrletal., author = {Hauser, Florian and Staufer, Susanne and R{\"o}hrl, Simon and Nadimpalli, Vamsi Krishna and Ezer, Timur and Grabinger, Lisa and Mottok, J{\"u}rgen and Falter, Thomas}, title = {LEVERAGING FIVE QUESTIONNAIRES TO ANALYZE STUDENT LEARNING STRATEGIES AND GENERATE AI-POWERED INDIVIDUALIZED LEARNING PATHS}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2025.0658}, pages = {1775 -- 1784}, abstract = {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.}, language = {en} } @inproceedings{SchafferEzerRoehrletal., author = {Schaffer, Josefa and Ezer, Timur and R{\"o}hrl, Simon and Hauser, Florian and Staufer, Susanne and Nadimpalli, Vamsi Krishna and Grabinger, Lisa and Antoni, Erika and Mottok, J{\"u}rgen}, title = {EYE TRACKING GLASSES IN EDUCATIONAL SETTINGS: GUIDELINES ON DATA QUALITY}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2025.1419}, pages = {5027 -- 5038}, abstract = {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.}, language = {en} } @inproceedings{EzerRoehrlMottok, author = {Ezer, Timur and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {Towards accurate eye tracking: quantifying error in linear pixel-to-degree conversion}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2025.1841}, pages = {6709 -- 6717}, abstract = {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.}, language = {en} }