@misc{ReuterLangerHauseretal., author = {Reuter, Rebecca and Langer, Thomas and Hauser, Florian and Muckelbauer, Daniel and Gegenfurtner, Andreas}, title = {Automatic generation of areas of interest in eye tracking: The case of software engineering}, series = {Earli Book of Abstracts}, journal = {Earli Book of Abstracts}, publisher = {RWTH Aachen}, language = {en} } @inproceedings{StauferHauserEzeretal., author = {Staufer, Susanne and Hauser, Florian and Ezer, Timur and Grabinger, Lisa and Nadimpalli, Vamsi Krishna and R{\"o}hrl, Simon and Bugert, Flemming and Bittner, Dominik and Mottok, J{\"u}rgen}, title = {EVALUATION OF THE LEARNING MANAGEMENT SYSTEM PYTHIA}, series = {EDULEARN Proceedings: 16th International Conference on Education and New Learning Technologies, Palma, Spain. 1-3 July, 2024}, booktitle = {EDULEARN Proceedings: 16th International Conference on Education and New Learning Technologies, Palma, Spain. 1-3 July, 2024}, publisher = {IATED}, issn = {2340-1117}, doi = {10.21125/edulearn.2024.2189}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-73474}, pages = {9089 -- 9098}, abstract = {Learning management systems gain importance due to the Corona pandemic. To personalize such a learning management system, a Moodle adaption named Pythia was created. Pythia is implemented as a Moodle plugin solution. The possibility of generating learning paths with various algorithms and the change of the graphical user interface is significant. Learning paths are generated with two different algorithms. Nestor algorithm generates learning paths with a Bayesian network, while Tyche algorithm uses a Markov model. The graphical user interface is adapted to the generated learning paths where learning element symbols are customized and the individual learning paths are depicted. This paper presents the results of a qualitative survey among German students with the aim of evaluating the first version of the learning management system Pythia. 25 students took the subject "Software Engineering for Safe and Secure Systems" in the winter term of 2023/24. The 68 asked questions focus on usage behavior, graphical user interface, usability, preferences, volume, and quality of learning elements, and learning paths. Our Moodle course for the lecture contains seven sections, each section has minimum one subsection on which the learning path is calculated. Last three sections are designed by presenting minimum one learning element of each learning element category except the collaboration tool. In a previous work, ten learning element categories were designed and evaluated comprising manuscript, brief overview, learning goal, quiz, exercise, summary, three types of additional material, and collaboration tool. The first sections are left as they are initially designed by the lecturer (normal section). The survey considers the difference between normal sections and diverse sections. Furthermore, the generated learning paths are evaluated. 13 students undertook Nestor learning paths, whereas 12 undertook Tyche learning paths. The results suggest that for the majority of students, the learning element symbols are well-chosen. Moreover, all students wish the elements relevant to the exam to be labelled. 23 students are overwhelmed by the number of learning elements in the last sections of the Moodle course. In the future, some suggestions will be implemented such as the labelling of exam relevant learning elements. To overcome the overload of learning elements, a strategy could be developed like hiding learning elements not preferred by the learning path algorithm.}, language = {en} } @article{GrabingerHauserMottok, author = {Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {On the perception of graph layouts}, series = {Journal of Software: Evolution and Process}, journal = {Journal of Software: Evolution and Process}, publisher = {Wiley}, doi = {10.1002/smr.2599}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-62973}, abstract = {In the field of software engineering, graph-based models are used for a variety of applications. Usually, the layout of those graphs is determined at the discretion of the user. This article empirically investigates whether different layouts affect the comprehensibility or popularity of a graph and whether one can predict the perception of certain aspects in the graph using basic graphical laws from psychology (i.e., Gestalt principles). Data on three distinct layouts of one causal graph is collected from 29 subjects using eye tracking and a print questionnaire. The evaluation of the collected data suggests that the layout of a graph does matter and that the Gestalt principles are a valuable tool for assessing partial aspects of a layout.}, language = {en} } @misc{HauserReuterGegenfurtneretal., author = {Hauser, Florian and Reuter, Rebecca and Gegenfurtner, Andreas and Gruber, Hans and Mottok, J{\"u}rgen and Hutzler, Ivonne}, title = {Heuristics in Software Modelling: An Eyetracking Study}, series = {Earli Book of Abstracts}, journal = {Earli Book of Abstracts}, publisher = {RWTH Aachen}, abstract = {To investigate the role of heuristics in the domain of software engineering, an eye tracking study was conducted in which experts and novices were compared. The study focused on one of the most challenging parts in this domain: the generation of an object model for a software product based on a requirements specification. During their training, software engineers are taught different techniques to solve this task. One of these techniques is the noun/verb analysis. However, it is still unclear to what extent novice and expert programmers are making use of it. Ideally, the noun/verb analysis works as a heuristic and helps programmers to make fast and accurate decisions. Participants in the study were 40 software programmers at four levels of expertise (novices, intermediates, experienced rogrammers, experts). They were presented with ten decision tasks. In each task, participants read a requirement specification and then had to choose one out of three presented class diagrams that they considered the best solution. During the task, their eye movements were recorded. Results show that all participants used the noun/verb analysis as a heuristic. Programmers with higher levels of expertise, however, outperformed programmers with lower levels of expertise. Interestingly, the more experienced programmers were not following the noun/verb analysis in a blindfolded way. They realised that the noun/verb analysis would produce diagrams, but a skilled software architect would not model them in this way. Instead they created their models in a way that they perceived as more logical and realistic}, language = {en} } @inproceedings{GruberWolffMottoketal., author = {Gruber, Hans and Wolff, Christian and Mottok, J{\"u}rgen and Bazo, Alexander and Hauser, Florian and Schreistetter, Stefan}, title = {Tutorial on Software Engineering Education in Co-Located Multi-User Eye-Tracking-Environments}, series = {Proceedings of the 32nd IEEE Conference on Software Engineering Education and Training: Munich, November 2020}, booktitle = {Proceedings of the 32nd IEEE Conference on Software Engineering Education and Training: Munich, November 2020}, doi = {10.1109/CSEET49119.2020.9206170}, abstract = {We briefly describe a tutorial on the application of Eye-Tracking technology for Software Engineering Education. We will showcase our setup of a large-scale Eye-Tracking-Classroom and its usage for real-time improvement of traditional learning scenarios in Software Engineering Education. We will focus on the integration of gaze data into modern integrated development environments (IDEs) and demonstrate a complete workflow for its usage in co-located multi-user Eye-Tracking-Environments.}, language = {en} } @inproceedings{HauserSchreistetterReuteretal., author = {Hauser, Florian and Schreistetter, Stefan and Reuter, Rebecca and Mottok, J{\"u}rgen and Gruber, Hans and Holmqvist, Kenneth and Schorr, Nick}, title = {Code reviews in C++: Preliminary results from an eye tracking study}, series = {ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and ApplicationsJune 2020}, booktitle = {ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and ApplicationsJune 2020}, isbn = {ISBN 9781450371346}, doi = {10.1145/3379156.3391980}, pages = {1 -- 5}, abstract = {Code reviews are an essential part of quality assurance in modern software projects. But despite their great importance, they are still carried out in a way that relies on human skills and decisions. During the last decade, there have been several publications on code reviews using eye tracking as a method, but only a few studies have focused on the performance differences between experts and novices. To get a deeper understanding of these differences, the following experiment was developed: This study surveys expertise-related differences in experts', advanced programmers', and novices' eye movements during the review of eight short C++ code examples, including correct and erroneous codes. A sample of 35 participants (21 novices, 14 advanced and expert programmers) were recruited. A Tobii Spectrum 600 was used for the data collection. Measures included participants' eye movements during the code review, demographic background data, and cued retrospective verbal comments on replays of their own eye movement recordings. Preliminary results give proof for experience-related differences between participants. Advanced and expert programmers performed significantly better in case of error detection and the eye tracking data implies a more efficient reviewing strategy.}, language = {en} } @inproceedings{HauserStarkMottoketal., author = {Hauser, Florian and Stark, Theresa and Mottok, J{\"u}rgen and Gruber, Hans and Reuter, Rebecca}, title = {Deliberate Practice in Programming: How is it carried out by programmers?}, series = {ECSEE '20: Proceedings of the 4th European Conference on Software Engineering Education, Seeon, Germany, June 2020}, booktitle = {ECSEE '20: Proceedings of the 4th European Conference on Software Engineering Education, Seeon, Germany, June 2020}, isbn = {ISBN 9781450377522}, doi = {10.1145/3396802.3396815}, pages = {42 -- 46}, abstract = {This work in progress study examines through which activities programmers perform deliberate practice to improve their own skills in coding and programming. For this reason, a qualitative questionnaire was developed and conducted with a sample of 22 participants. The results indicate that programmers perform formal and informal forms of training and learning. Typically, a classical programming training in the context of a university course or for work-related reasons is a first step in the acquisition of expertise. Building on these basic skills, non-formal and informal learning activities are carried out by the learners. Especially the social interaction and the collaborative work with other programmers is of great importance in this context. The activities mentioned by the test persons fulfil the characteristics of deliberate practice and will be examined more closely in a further study.}, language = {en} } @inproceedings{ReuterHauserMuckelbaueretal., author = {Reuter, Rebecca and Hauser, Florian and Muckelbauer, Daniel and Stark, Theresa and Antoni, Erika and Mottok, J{\"u}rgen and Wolff, Christian}, title = {Using augmented reality in software engineering education? First insights to a comparative study of 2D and AR UML modeling}, series = {Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS) and 31st Conference on Software Engineering Education and Training (CSEE\&T), January 8 - 11, 2019 Grand Wailea, Hawaii}, booktitle = {Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS) and 31st Conference on Software Engineering Education and Training (CSEE\&T), January 8 - 11, 2019 Grand Wailea, Hawaii}, doi = {10.24251/HICSS.2019.938}, pages = {7798 -- 7807}, abstract = {Although there has been much speculation about the potential of Augmented Reality (AR) in teaching for learning material, there is a significant lack of empirical proof about its effectiveness and implementation in higher education. We describe a software to integrate AR using the Microsoft Hololens into UML (Unified Modeling Language) teaching. Its user interface is laid out to overcome problems of existing software. We discuss the design of the tool and report a first evaluation study. The study is based upon effectiveness as a metric for students performance and components of motivation. The study was designed as control group experiment with two groups. The experimental group had to solve tasks with the help of the AR modeling tool and the control group used a classic PC software. We identified tendencies that participants of the experimental group showed more motivation than the control group. Both groups performed equally well}, language = {en} } @inproceedings{ReuterKnietzschHauseretal., author = {Reuter, Rebecca and Knietzsch, Marco and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Supporting Abstraction Skills Using Augmented Reality?}, series = {ITiCSE '19, Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education, July 2019, Aberdeen Scotland UK}, booktitle = {ITiCSE '19, Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education, July 2019, Aberdeen Scotland UK}, doi = {10.1145/3304221.3325562}, abstract = {We investigated the potential of augmented reality (AR) to enable visualization of abstract concepts and present the first iteration of a teaching experiment that evaluates the use of AR as support for abstraction skills. Students were confronted with the task to present and explain information to different groups of stakeholders at the example of a coffee machine. Results show that students find it helpful to have a visual app-prototype and especially one that can be disassembled in different levels. The main goal was to sensitize students for the need to think about and to abstract information for certain roles and perspectives.}, language = {en} } @inproceedings{SchreistetterHauserReuteretal., author = {Schreistetter, Stefan and Hauser, Florian and Reuter, Rebecca and Jahn, Sabrina and Mottok, J{\"u}rgen}, title = {Using Eye Tracking Data to analyze students' tasks solving behavior in classroom contexts}, series = {12th International Conference of Education, Research and Innovation (ICERI 2019), Conference Proceedings, 2019, Seville (Spain)}, booktitle = {12th International Conference of Education, Research and Innovation (ICERI 2019), Conference Proceedings, 2019, Seville (Spain)}, isbn = {978-84-09-14755-7}, issn = {2340-1095}, doi = {10.21125/iceri.2019.1473}, pages = {6087 -- 6096}, language = {en} } @inproceedings{HauserReuterGegenfurtneretal., author = {Hauser, Florian and Reuter, Rebecca and Gegenfurtner, Andreas and Gruber, Hans and Mottok, J{\"u}rgen}, title = {Eye movements in software modelling - what do they tell us about heuristics}, series = {12th International Conference of Education, Research and Innovation (ICERI 2019), Conference Proceedings, 2019, Seville (Spain)}, booktitle = {12th International Conference of Education, Research and Innovation (ICERI 2019), Conference Proceedings, 2019, Seville (Spain)}, isbn = {978-84-09-14755-7}, doi = {10.21125/iceri.2019.1469}, pages = {6064 -- 6070}, language = {en} } @inproceedings{NivalaHauserMottoketal., author = {Nivala, Markus and Hauser, Florian and Mottok, J{\"u}rgen and Gruber, Hans}, title = {Developing visual expertise in software engineering: An eye tracking study}, series = {IEEE Global Engineering Education Conference (EDUCON), 10-13 April 2016, Abu Dhabi, UAE}, booktitle = {IEEE Global Engineering Education Conference (EDUCON), 10-13 April 2016, Abu Dhabi, UAE}, doi = {10.1109/EDUCON.2016.7474614}, pages = {613 -- 620}, abstract = {Program comprehension and the ability to find program errors are key skills of software engineering. The aim of this pilot study was to examine the visual processes of novice and advanced programmers in authentic tasks. Fifteen novices and eight advanced programmers were given eight short pieces of code. Their task was to either identify an error or give the output of the code. Eye movements and keyboard activity were recorded. On average, the novices spent more time reading the code than composing the response, whereas the more advanced programmers started composing the response sooner and spent more time on it. In general, the advanced programmers had shorter fixations and saccades. The results suggest that the advanced programmers are quicker to grasp the essence of the code and able to see more details in it. The advanced programmers had shorter fixations and saccade lengths during the second phase which might indicate the process of chunking.}, language = {en} } @inproceedings{HauserReissNivalaetal., author = {Hauser, Florian and Reiß, Markus and Nivala, Markus and Mottok, J{\"u}rgen and Gruber, Hans}, title = {Eye tracking applied: Visual expertise in code reviews}, series = {9th International Conference on Education and New Learning Technologies, EDULEARN17, 3-5 July, 2017, Barcelona, Spain, Proceedings}, booktitle = {9th International Conference on Education and New Learning Technologies, EDULEARN17, 3-5 July, 2017, Barcelona, Spain, Proceedings}, doi = {10.21125/edulearn.2017.1084}, pages = {379 -- 389}, language = {en} } @inproceedings{ReuterHauserGoldVeerkampetal., author = {Reuter, Rebecca and Hauser, Florian and Gold-Veerkamp, Carolin and Mottok, J{\"u}rgen and Abke, J{\"o}rg}, title = {Towards a Definition and Identification of Learning Obstacles in Higher Software Engineering Education}, series = {EDULEARN17 Proceedings, 9th International Conference on Education and New Learning Technologies, 3-5 July, 2017, Barcelona, Spain}, booktitle = {EDULEARN17 Proceedings, 9th International Conference on Education and New Learning Technologies, 3-5 July, 2017, Barcelona, Spain}, isbn = {978-84-697-3777-4}, issn = {2340-1117}, doi = {10.21125/edulearn.2017.0943}, pages = {10259 -- 10267}, subject = {Hochschuldidaktik}, language = {en} } @inproceedings{NivalaHauserJossbergeretal., author = {Nivala, Markus and Hauser, Florian and Jossberger, Helen and Reiß, Markus and Mottok, J{\"u}rgen and Gruber, Hans}, title = {Detecting errors in program flow diagrams: Comparing novices and advanced programmers}, series = {EARLI Biennial Conference 2017, 17th European Association for Research on Learning and Instruction, 29 August - 2 September, 2017 - University of Tampere, Finland}, booktitle = {EARLI Biennial Conference 2017, 17th European Association for Research on Learning and Instruction, 29 August - 2 September, 2017 - University of Tampere, Finland}, language = {en} } @inproceedings{ReuterHauserGoldVeerkampetal., author = {Reuter, Rebecca and Hauser, Florian and Gold-Veerkamp, Carolin and Stark, Theresa and Kis, Juliane and Mottok, J{\"u}rgen and Abke, J{\"o}rg and Meyer, Dany}, title = {Towards the construction of a questionnaire for the identification of learning obstacles}, series = {2018 IEEE Global Engineering Education Conference (EDUCON 2018), 17-20 April, 2018, Santa Cruz de Tenerife, Spain}, booktitle = {2018 IEEE Global Engineering Education Conference (EDUCON 2018), 17-20 April, 2018, Santa Cruz de Tenerife, Spain}, doi = {10.1109/EDUCON.2018.8363266}, pages = {457 -- 466}, abstract = {This paper deals with the identification of learning ob-stacles using the questionnaire method. Therefore, two iterations were proceeded: The first one was part of a survey that was carried out at four lo-cations at universities of applied sciences. We asked students about obstructive facts in general providing items for five learning ob-stacle dimensions that were set up before; emotional/motivational, epistemological/cognitive, didactical, resource-related and meta-cognitive learning obstacle dimensions. After the general part, we asked them to answer the same question, but in relation to the - in their opinion - most difficult learning content. With this question, we aim to get indications regarding to epistemological obstacles. In a second step, we used the "Motivated Strategies for Learning Questionnaire", which was developed by Pintrich [1] as a basis to develop a questionnaire that extracts learning obstacles. In its original version, the "Motivated Strategies for Learning Question-naire" was intended to measure students' learning strategies, but, as the obstacle dimensions were partly derived from learning strategy classification, we chose this already validated question-naire [2]. Within this iteration, we could confirm a five-factor structure of the questionnaire that could be mapped to the five be-fore set learning obstacle dimensions.}, language = {en} } @inproceedings{HauserReuterGruberetal., author = {Hauser, Florian and Reuter, Rebecca and Gruber, Hans and Mottok, J{\"u}rgen}, title = {Research competence: Modification of a questionnaire to measure research competence at universities of applied sciences}, series = {IEEE Global Engineering Education Conference (EDUCON), 17-20 April 2018, Santa Cruz de Tenerife, Spain}, booktitle = {IEEE Global Engineering Education Conference (EDUCON), 17-20 April 2018, Santa Cruz de Tenerife, Spain}, doi = {10.1109/EDUCON.2018.8363216}, pages = {109 -- 117}, abstract = {This paper deals with the validation and modification of the German questionnaire "F-Komp". In its original version, it was intended to measure university students' research competences. In the beginning of this study, there were only a few tools available which were reliable. For the purposes of this study, they were not suitable. At the same time, there was no validated version of the F-Komp available, which made the whole validation process for further usage necessary. This questionnaire is based on a structure, which consist of different skills and knowledge and is focused on measuring research competence in general. The validation and modification of the F-Komp is therefore the aim of our contribution as well as a revised version of the questionnaire. We proceeded an explorative factor and a reliability analysis to do a general evaluation of the tool. Some modifications were done in the questionnaire to make it more suitable to the requirements of technical oriented universities of applied sciences [5]. Our revised version is slightly longer and contains several items to gather data about the participants demographics. The modified questionnaire is based on a more appropriate factor structure. This structure is more practically oriented and pays attention to ethical issues. In future cases, this questionnaire will be used in research oriented courses to measure students' progress in acquiring the knowledge and methods which are necessary to perform as a scientist in different research areas.}, language = {en} } @inproceedings{HauserGegenfurtnerHutzleretal., author = {Hauser, Florian and Gegenfurtner, Andreas and Hutzler, Ivonne and Reuter, Rebecca and Mottok, J{\"u}rgen}, title = {Eye movement patterns in source code review}, series = {EARLI SIG 17 and 25 Biennial Conference 2018, Cambridge, United Kingdom}, booktitle = {EARLI SIG 17 and 25 Biennial Conference 2018, Cambridge, United Kingdom}, abstract = {This study is based on the work of Uwano, Nakamura, Monden and Matsumoto (2006) who tried to identify programmers' eye movements in source code reviews by using eye tracking technology. The researchers were able to identify certain eye movement patterns but due to the technical limitations of earlier eye tracking systems and a small sample they could not find a valid proof for their existence. Now, twelve years later, the eye tracking technology has made significant improvements and is able to capture programmers' reading behavior in an unobtrusive and precise way. Now the goal is to verify the described patterns by using eye tracking data from expert and novice programmers. In the experiment they have to detect errors in six different codes and take part in a retrospective interview. At the moment, data collections are ongoing. At the time of the conference, we will present the results of our analyses.}, language = {en} } @inproceedings{HutzlerHauserReuteretal., author = {Hutzler, Ivonne and Hauser, Florian and Reuter, Rebecca and Mottok, J{\"u}rgen and Gruber, Hans}, title = {Will the noun/verb analysis be used to generate class diagrams? An eye tracking study}, series = {IATED, 11th International Conference of Education, Research and Innovation (ICERI 2018), 12-14 November 2018, Seville, Spain}, booktitle = {IATED, 11th International Conference of Education, Research and Innovation (ICERI 2018), 12-14 November 2018, Seville, Spain}, doi = {10.21125/iceri.2018.1103}, pages = {505 -- 514}, language = {en} } @inproceedings{HauserMottokReuteretal., author = {Hauser, Florian and Mottok, J{\"u}rgen and Reuter, Rebecca and Hutzler, Ivonne and Gruber, Hans}, title = {Eye movement patterns in software engineering - What differs the expert from the novice?}, series = {IATED, 11th International Conference of Education, Research and Innovation (ICERI 2018), 12-14 November 2018, Seville, Spain}, booktitle = {IATED, 11th International Conference of Education, Research and Innovation (ICERI 2018), 12-14 November 2018, Seville, Spain}, doi = {10.21125/iceri.2018.1129}, language = {en} } @article{GegenfurtnerBoucheixGruberetal., author = {Gegenfurtner, Andreas and Boucheix, Jean-Michel and Gruber, Hans and Hauser, Florian and Lehtinen, Erno and Lowe, Richard K.}, title = {The gaze relational index as a measure of visual expertise}, series = {Journal of Expertise}, volume = {3}, journal = {Journal of Expertise}, number = {March}, publisher = {Michigan State University}, issn = {2573-2773}, pages = {32 -- 40}, abstract = {Eye tracking is a powerful technique that helps reveal how people process visual information. This paper discusses a novel metric for indicating expertise in visual information processing. Named the Gaze Relational Index (GRI), this metric is defined as the ratio of mean fixation duration to fixation count. Data from two eye-tracking studies of professional vision and visual expertise in using 3D dynamic medical visualizations are presented as cases to illustrate the suitability and additional benefits of the GRI. Calculated values of the GRI were higher for novices than for experts, and higher in non-representative, semi-familiar / unfamiliar task conditions than in domain-representative familiar tasks. These differences in GRI suggest that, compared to novices, experts engaged in more knowledge-driven, top-down processing that was characterized by quick, exploratory visual search. We discuss future research aiming to replicate the GRI in professional domains with complex visual stimuli and to identify the moderating role of cognitive ability on GRI estimates.}, language = {en} } @inproceedings{HauserMottokGruber, author = {Hauser, Florian and Mottok, J{\"u}rgen and Gruber, Hans}, title = {Eye Tracking Metrics in Software Engineering}, series = {Proceedings of the 3rd European Conference of Software Engineering Education (ECSEE'18); Seeon/ Bavaria Germany, 14.06.2018 -15.06.2018}, booktitle = {Proceedings of the 3rd European Conference of Software Engineering Education (ECSEE'18); Seeon/ Bavaria Germany, 14.06.2018 -15.06.2018}, editor = {Mottok, J{\"u}rgen}, publisher = {ACM}, address = {New York, NY}, isbn = {9781450363839}, doi = {10.1145/3209087.3209092}, pages = {39 -- 44}, abstract = {This paper aims to provide an overview of the interdisciplinary combination of educational science, psychology, software engineering and the eye tracking methodology. The domain of software engineering is offering great potential for applied eye tracking research and in turn it can benefit from the possibilities of this upcoming technology as well. Nevertheless, software engineering has to struggle with some obstacles. These are namely the different terms, missing guidelines for experimental setups and a lack of common and standardized metrics. If eye tracking should be used in a broader way these problems must be solved. The main purpose of this paper is to list all eye tracking metrics which are relevant for software engineering and to give guidelines to help beginners by avoiding possible pitfalls.}, language = {en} } @inproceedings{GrabingerHauserMottok, author = {Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Accessing the Presentation of Causal Graphs and an Application of Gestalt Principles with Eye Tracking}, series = {2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), 1st Workshop on Advances in Human-Centric Experiments in Software Engineering (HUMAN 2022): 15-18 March 2022, Honolulu, HI, USA}, booktitle = {2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), 1st Workshop on Advances in Human-Centric Experiments in Software Engineering (HUMAN 2022): 15-18 March 2022, Honolulu, HI, USA}, publisher = {IEEE}, doi = {10.1109/SANER53432.2022.00153}, pages = {1278 -- 1285}, abstract = {The discipline of causal inference uses so-called causal graphs to model cause and effect relations of random variables. As those graphs only encode a relation structure there is no hard rule concerning their alignment. The present paper presents a study with the aim of working out the optimal alignment of causal graphs with respect to comprehensibility and interestingness. In addition, the study examines whether the central gestalt principles of psychology apply for causal graphs. Data from 29 participants is acquired by triangulating eye tracking with a questionnaire. The results of the study suggest that causal graphs should be aligned downwards. Moreover, the gestalt principles proximity, similarity and closure are shown to hold true for causal graphs.}, language = {en} } @inproceedings{GrabingerHauserMottok, author = {Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Evaluating Graph-based Modeling Languages}, 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 = {10.1145/3593663.3593664}, pages = {120 -- 129}, abstract = {As humans, we tend to use models to describe reality. Modeling languages provide the formal frameworks for creating such models. Usually, the graphical design of individual model elements is based on subjective decisions; their suitability is determined at most by the prevalence of the modeling language. With other words: there is no objective way to compare different designs of model elements. The present paper addresses this issue: it introduces a systematic approach for evaluating the elements of graph-based modeling languages comprising 14 criteria - derived from standards, usability analyses, or the design theories 'Physics of Notations' and 'Cognitive Dimensions of Notations'. The criteria come with measurement procedures and evaluation schemes based on reasoning, eye tracking, and questioning. The developed approach is demonstrated with a specific use case: three distinct sets of node elements for causal graphs are evaluated in an eye tracking study with 41 subjects.}, language = {en} } @inproceedings{HauserGrabingerMottok, author = {Hauser, Florian and Grabinger, Lisa and Mottok, J{\"u}rgen}, title = {Something Short Gets Even Shorter: Adapting the LIST-K for the Use in an Online Learning Management System}, 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 = {10.1145/3593663.3593684}, pages = {65 -- 72}, abstract = {This study examines how Klingsieck's LIST-K questionnaire [22] can be shortened and adapted to the requirements of an online learning management system. In a study with 213 participants, the questionnaire is subjected to an exploitative factor analysis. In a next step, the results are evaluated in terms of their reliability. This process creates a modified factor structure for the LIST-K, comprising a total of eight factors. The reliability of the modified questionnaire is at an α of .770. The shortened version of the LIST-K questionnaire is currently being used on an experimental basis in different courses.}, language = {en} } @inproceedings{HauserGrabingerMottoketal., author = {Hauser, Florian and Grabinger, Lisa and Mottok, J{\"u}rgen and Gruber, Hans}, title = {Visual Expertise in Code Reviews: Using Holistic Models of Image Perception to Analyze and Interpret Eye Movements}, series = {ETRA '23: 2023 Symposium on Eye Tracking Research and Applications, Tubingen Germany 30 May 2023- 2 June 2023}, booktitle = {ETRA '23: 2023 Symposium on Eye Tracking Research and Applications, Tubingen Germany 30 May 2023- 2 June 2023}, publisher = {ACM}, doi = {10.1145/3588015.3589189}, pages = {1 -- 7}, abstract = {This study uses holistic models of image perception to analyze and interpret eye movements during a code review. 23 participants (15 novices and 8 experts) take part in the experiment. The subjects' task is to review six short code examples in C programming language and identify possible errors. During the experiment, their eye movements are recorded by an SMI 250 REDmobile. Additional data is collected through questionnaires and retrospective interviews. The results implicate that holistic models of image perception provide a suitable theoretical background for the analysis and interpretation of eye movements during code reviews. The assumptions of these models are particularly evident for expert programmers. Their approach can be divided into different phases with characteristic eye movement patterns. It is best described as switching between scans of the code example (global viewing) and the detailed examination of errors (focal viewing).}, language = {en} } @inproceedings{RoehrlStauferNadimpallietal., author = {R{\"o}hrl, Simon and Staufer, Susanne and Nadimpalli, Vamsi Krishna and Bugert, Flemming and Bugert, Flemming and Hauser, Florian and Grabinger, Lisa and Bittner, Dominik and Ezer, Timur and Mottok, J{\"u}rgen}, title = {PYTHIA - AI SUGGESTED INDIVIDUAL LEARNING PATHS FOR EVERY STUDENT}, 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}, isbn = {978-84-09-59215-9}, issn = {2340-1079}, doi = {10.21125/inted.2024.0783}, pages = {2871 -- 2880}, abstract = {During the COVID-19 pandemic, the importance of digital course rooms, where teachers provide their learning materials, increased dramatically. While these platforms are crucial for providing teaching materials, they often fall short in addressing individual student needs. A system within an academic setting, capable of creating and presenting individual learning paths for each student, can solve these issues. These paths are composed of various learning elements - defined in our previous work as units of educational content with which a learner works. Currently, there is no suitable system that enables the integration of learning path generating algorithms into a digital course room. Therefore we present an application that enables this integration into the Moodle Learning Management System (LMS). More precisely, this paper presents a Moodle plugin together with its framework. It describes the mechanism for effectively collecting data from Moodle, which AI algorithms then use to generate personalized learning paths. Subsequently these paths are visualized with the help of the Moodle plugin. We started with a set of requirements and use cases for the interface connecting Moodle to the AI system, which were established with a group of experts. Based on the requirements, various relevant technologies were assessed, and the best ones were chosen for implementation. Following that, the paper develops a strategy for software structuring as well as an architecture, focusing on performance, modularity, and ease of deployment for widespread use. Furthermore, the architecture ensures a simple method for integrating the algorithms. Afterwards, the framework's concrete implementation is described. A technique for enriching learning elements with metadata is presented, and additionally a concept for presenting these learning elements within a hierarchy. Moreover, it is shown how questionnaire responses and learning analytics are utilized for data collection. We cover in detail techniques for extracting and storing data from the Moodle database, as well as methods for customizing Moodle course rooms and a standard API for incorporating AI algorithms. Finally, the paper discusses the application of the proposed framework in an actual course and how student feedback is collected, which could enhance the framework. It concludes with an assessment of the outcomes obtained and prospects for the framework's future advancements.}, language = {en} } @inproceedings{StauferHauserGrabingeretal., author = {Staufer, Susanne and Hauser, Florian and Grabinger, Lisa and Bittner, Dominik and Nadimpalli, Vamsi Krishna and Bugert, Flemming and Ezer, Timur and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {Learning elements in LMS - a survey among students}, 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}, publisher = {IATED}, isbn = {978-84-09-59215-9}, issn = {2340-1079}, doi = {10.21125/inted.2024.1087}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-71135}, pages = {4224 -- 4231}, abstract = {In higher education, improving learning and learning success are goals of general improvement. Lecturers teaches content and students acquire that content in an efficient way. To structure content, learning element categories are evaluated from the student's point of view in higher education area. The aim is to validate given definitions of ten learning element categories within a Learning Management System (LMS). This paper evaluates a categorization of learning elements for organizing learning content in online education within LMSs. Therefore, ten categories of learning elements and corresponding definitions were defined in a previous work as base for this paper. The learning elements to examine are manuscript, exercise, quiz, brief overview, learning goal, summary, collaboration tool, auditory additional material, textual additional material, and visual additional material. To validate the definitions and to get improvements to each learning element a survey is processed. Beside the demographic data questions, the survey consists of two questions to the acceptance of the definitions and asks for improvements. 148 students between the ages 19 and 35 participate in the survey in summer term 2023. The education level of the participants ranges from undergraduates to Ph.D. students. The results of this paper are that more than 80\% accept the given definitions. Some definitions of the learning elements are changed, but the changes are restricted to additions of maximal four words. This categorization of learning elements could lead to improvements in learning by giving the content more structure. With the structure students get the possibility to learn with preferred learning elements which could lead to more success in learning and to a decreasing dropout rate in universities. In the future, the learning elements allow to classify content within LMSs with the goal of generating individual learning paths. Furthermore, our project will integrate these learning elements, use them to generate learning paths, and could set a new standard in the way of personalized learning.}, language = {en} } @inproceedings{GrabingerEzerHauseretal., author = {Grabinger, Lisa and Ezer, Timur and Hauser, Florian and Mottok, J{\"u}rgen}, title = {The impact of eyenalyzer}, 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.2024.0271}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-78215}, pages = {695 -- 701}, abstract = {Empirical research poses numerous challenges for beginners. This is especially true for data analysis - a task that usually requires knowledge from two distinct areas: statistics and programming. To support prospective researchers with that task, we developed a web-based tool called eyenalyzer. It supports common activities in the data analysis phase of empirical studies in a way that is suitable for novices in both, statistics and programming. The present article describes a controlled experiment investigating the impact of this tool with a total of 20 participants. All of them are given a set of common data analysis tasks. Half of the participants complete the tasks using eyenalyzer, the other half can use anything except for eyenalyzer. For each task and participant, we record the time in minutes, the task score, and the perceived difficulty. The results confirm that our tool is a valuable support for novice researchers: With eyenalyzer, the participants are significantly faster, achieve higher scores, and perceive the tasks to be less difficult.}, language = {en} } @inproceedings{EzerGrabingerHauseretal., author = {Ezer, Timur and Grabinger, Lisa and Hauser, Florian and Staufer, Susanne and Mottok, J{\"u}rgen}, title = {EYE TRACKING AS TECHNOLOGY IN EDUCATION: FURTHER INVESTIGATION OF DATA QUALITY AND IMPROVEMENTS}, 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}, publisher = {IATED Academy}, isbn = {978-84-09-59215-9}, issn = {2340-1079}, doi = {10.21125/inted.2024.0802}, pages = {2955 -- 2961}, abstract = {Eye tracking serves as a powerful tool across a variety of empirical research areas: From usability research over cognitive research to educational research and applications in classrooms. However, data noise in eye tracking data poses a challenge to researchers and educators, as it leads to gaze positions being measured imprecisely under unfavorable conditions. In our previous study, we systematically investigated factors that influence data quality and are easily controllable in a classroom or laboratory environment, such as illumination, sampling frequency, and head orientation. However, no recommendations regarding the light source and light orientation could be provided, as these influences could not be analyzed in sufficient detail. Yet, a further examination of these factors, eliminating human influences by using an artificial head, revealed significant differences between individual settings. Hence, in this empirical study of eye tracking as an educational technology, we delve deeper into examining the impact of both light source and light orientation on data quality. This is investigated with an artificial head together with the Tobii Pro Spectrum eye tracking device. To measure data quality, we use the metrics precision and standard deviation as indicators of data noise. The obtained results derive practical advice for educators and researchers, such as not to illuminate the subject from the rear, in order to gather useful data for research and future classroom applications. Thereby, this study serves as a complement to our previous research, answering open questions regarding best practices for researchers and educators when using eye trackers. It aims to provide valuable insights into producing data of the highest quality possible when using eye trackers, both in laboratory settings and in future classrooms applications.}, language = {en} } @inproceedings{EzerGrabingerHauseretal., author = {Ezer, Timur and Grabinger, Lisa and Hauser, Florian and Staufer, Susanne and Mottok, J{\"u}rgen}, title = {EYE TRACKING METRICS FOR DISTINGUISHING GLOBAL AND FOCAL GAZE PATTERNS: A SYSTEMATIC LITERATURE REVIEW}, 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}, publisher = {IATED Academy}, isbn = {978-84-09-59215-9}, issn = {2340-1079}, doi = {10.21125/inted.2024.0814}, pages = {3005 -- 3014}, abstract = {Global and focal eye tracking gaze patterns are distinguished in a variety of domains, such as radiology research, empirical software engineering, behavioral psychology, and cartography research. A global gaze pattern is present if a participant in an eye tracking study gains an overview of the stimulus, whereas a focal pattern emerges during a more detailed evaluation of a specific part of the stimulus. With this higher-level measure of global and focal gaze patterns, cognitive effort, expertise levels, and even neurological characteristics - such as autism - can be quantified. Furthermore, with this measure, cognitive efforts of students in certain tasks can be investigated and clues for targeted support can be provided. These properties in particular make this eye tracking measure valuable for the education domain. In the field of eye tracking research, many researchers have established distinct criteria for differentiating global and focal gaze patterns. Moreover, it is rarely quantitatively measured which of these patterns is present, but rather determined based on qualitative considerations. However, some studies explicitly use quantitative metrics like saccade-length or fixation-duration to distinguish between global and focal gaze patterns. This is the focus of the present systematic literature review: It searches for and compiles the eye tracking metrics used in the literature to distinguish global from focal gaze patterns in a quantitative way. In doing so, this study fills the yawning gap in quantitative metrics for determining global and focal gaze patterns. In the long run, this can be used to answer questions about cognitive load and problems in processing tasks during eye tracking studies and to provide further insights into the cognitive processes of students. The present paper first discusses definitions of global and focal gaze patterns and presents examples in which this metric has already been applied. Then, the research questions together with search strings and search engines used for the systematic literature review are described. Finally, the results are summarized, presenting a compilation of and connections between quantitative metrics utilized in the literature to distinguish between global and focal viewing patterns.}, language = {en} } @inproceedings{EzerGreinerGrabingeretal., author = {Ezer, Timur and Greiner, Matthias and Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Eye tracking al technology in education; data quality analysis and improvements}, 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.2023.1127}, pages = {4500 -- 4509}, abstract = {Eye tracking has proven to be a powerful tool in a variety of empirical research areas; hence, it is steadily gaining attention. Driven by the expanding frontiers of Artificial Intelligence and its potential for data analysis, eye tracking technology offers promising applications in diverse fields, from usability research to cognitive research. The education sector in particular can benefit from the increased use of eye tracking technology - both indirectly, for example by studying the differences in gaze patterns between experts and novices to identify promising strategies, and directly by using the technology itself to teach in future classrooms. As with any empirical method, the results depend directly on the quality of the data collected. That raises the question of which parameters educators or researchers can influence to maximize the data quality of an eye tracker. This is the starting point of the present work: In an empirical study of eye tracking as an (educational) technology, we systematically examine factors that influence the data quality, such as illumination, sampling frequency, and head orientation - parameters that can be varied without much additional effort in everyday classroom or research use - using two human subjects, an artificial face, and the Tobii Pro Spectrum. We rely on metrics derived from the raw gaze data, such as accuracy or precision, to measure data quality. The obtained results derive practical advice for educators and researchers, such as using the lowest sampling frequency appropriate for a certain purpose. Thereby, this research fills a gap in the current understanding of eye tracker performance and, by offering best practices, enables researchers or teachers to produce data of the highest quality possible and therefore best results when using eye trackers in laboratories or future classrooms.}, language = {en} } @inproceedings{HomannGrabingerHauseretal., author = {Homann, Alexander and Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {An Eye Tracking Study on MISRA C Coding Guidelines}, series = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, booktitle = {ECSEE '23: 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 = {10.1145/3593663.3593671}, pages = {130 -- 137}, abstract = {C is one of the most widely used programming languages - MISRA C is one of the most known sets of coding guidelines for C. This paper examines the usefulness and comprehensibility of the MISRA C:2012 guidelines in an eye tracking study. There, subjects encounter non-compliant code in four different code review settings: with no additional reference, with an actual MISRA C guideline, with a case-specific interpretation of a MISRA C guideline, and with a compliant version of the code. The data collected was analyzed not only in terms of the four presentation styles, but also by dividing the subjects into experience levels based on their semesters of study or years of work experience. Regarding the difference between actual and interpreted guidelines, we found that for interpreted guidelines the error detection rate is higher whereas the duration and frequency of visits to the guideline itself are mainly lower. This suggest that the actual guidelines are less useful and more difficult to understand. The former is contradicted by the subjects' opinions: when surveyed, they rated the usefulness of the actual guidelines higher.}, language = {en} } @inproceedings{StauferHauserGrabingeretal., author = {Staufer, Susanne and Hauser, Florian and Grabinger, Lisa and Bittner, Dominik and Nadimpalli, Vamsi Krishna and Mottok, J{\"u}rgen}, title = {Learning elements in online learning management systems}, 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.2023.0815}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-67725}, pages = {3121 -- 3130}, language = {en} } @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} } @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{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} } @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} } @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{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} }