@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{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} } @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{HauserStauferRoehrletal., author = {Hauser, Florian and Staufer, Susanne and R{\"o}hrl, Simon and Nadimpalli, Vamsi Krishna and Ezer, Timur and Grabinger, Lisa and Mottok, J{\"u}rgen and Falter, Thomas}, title = {LEVERAGING FIVE QUESTIONNAIRES TO ANALYZE STUDENT LEARNING STRATEGIES AND GENERATE AI-POWERED INDIVIDUALIZED LEARNING PATHS}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2025.0658}, pages = {1775 -- 1784}, abstract = {Background: The COVID-19 pandemic has significantly accelerated the shift toward online and blended learning in higher education, placing renewed emphasis on the individualization of learning content to meet diverse student needs. Even high-quality learning materials may fail to engage learners if they do not align with students' personal preferences and learning styles. Identifying these learner preferences, therefore, emerges as a critical challenge. Objectives: This paper presents ongoing work within a larger research project aimed at employing artificial intelligence to recommend optimal learning path for students in specific courses. Beyond mere optimization, the goal is to ensure the best possible fit between learning materials and individual learners. Sample \& Methods: A total of 27 students from technical degree programs took part in this survey. All participation was voluntary, and data were handled in full compliance with GDPR regulations. Although our broader project integrates fine-grained learning analytics from Moodle, the present abstract focuses exclusively on the self-report questionnaire results. Participants completed five instruments: 1. Index of Learning Styles (ILS) 2. LIST-K (Learning and Study Strategies Inventory - Short version) 3. BFI-10 (Big Five Inventory - 10 items) 4. Custom Preferences Instrument, capturing preferences for specific learning elements (e.g. instructional videos, lecture notes, summaries) and basic demographic data 5. Motivational Value Systems Questionnaire (MVSQ), piloted last semester to assess value orientations and motivational drivers Results: Preliminary analyses of the questionnaire data reveal: - Learning Styles (ILS): The majority lean toward the visual learning type (M = 5.740, SD = 3.430). - Learning Strategies (LIST-K): High scores on metacognitive strategies (M = 3.000; SD = 0.520) and collaboration with peers (M = 3.190; SD = 0.540). - Preferred Learning Elements: Summaries, overviews, and self-checks are most favored. - Value Orientations (MVSQ): Students are primarily driven by the pursuit of personal achievement (M = 4.400; SD = 11.140). Conclusion \& Significance: By integrating these five standardized questionnaires, we gain valuable insights into student learning preferences—insights that complement our Moodle analytics in the broader project. Observed trends suggest that learning materials should be concise and designed to facilitate peer interaction and knowledge deepening. These findings will guide the refinement of our AI-driven recommendation engine, enhancing its ability to deliver personalized learning paths that boost both engagement and effectiveness.}, language = {en} } @inproceedings{SchafferEzerRoehrletal., author = {Schaffer, Josefa and Ezer, Timur and R{\"o}hrl, Simon and Hauser, Florian and Staufer, Susanne and Nadimpalli, Vamsi Krishna and Grabinger, Lisa and Antoni, Erika and Mottok, J{\"u}rgen}, title = {EYE TRACKING GLASSES IN EDUCATIONAL SETTINGS: GUIDELINES ON DATA QUALITY}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2025.1419}, pages = {5027 -- 5038}, abstract = {Eye movement modeling examples, so-called EMME videos, are a valuable tool in education, helping learners better understand instructional content. Like conventional educational videos, EMME videos combine text, images, and voiceovers. However, they also display the instructor's or teacher's gaze, guiding learners attention to key elements. Although various approaches exist for creating EMME videos, there is currently no standardised guideline for ensuring gaze data quality. Eye tracking technology is essential to capture gaze behaviour, and in educational settings without a fixed computer monitor - such as when using blackboards or conducting live experiments - the usage of mobile eye tracking glasses is beneficial. An accuracy study is conducted using mobile eye tracking glasses to provide empirical guidance for the development of high-quality educational EMME videos and ensure that the instructor's or teacher's gaze is captured with high precision. The study uses the Tobii Pro Glasses 3 and involves a static and a dynamic setup with 34 participants. To gain insight into the effects of visual impairments on accuracy, we also include participants who wear contact lenses. In the static setup, participants are seated at a desk with a headrest and focus on a poster with nine fixation points. In the dynamic setup, participants are walking in a controlled half-circle around the poster while maintaining focus on its centre. Each setup is performed multiple times under varying lighting levels (300 lux, 700 lux) and distances between participant and poster (80 cm, 120 cm, 180 cm). This enables the simulation of diverse educational environments, including the possibility of a teacher's or instructor's movement. The study results will be evaluated regarding lighting conditions, the distance between the person wearing eye tracking glasses and the object, and possible influences of contact lenses. Based on these findings, favourable conditions for creating EMME videos in educational settings are collected, especially when working without a fixed computer monitor. The results address the outlined research gap by providing instructors and teachers with guidelines enabling them to produce high-quality educational EMME videos.}, language = {en} } @inproceedings{NadimpalliMaierStauferetal., author = {Nadimpalli, Vamsi Krishna and Maier, Robert and Staufer, Susanne and R{\"o}hrl, Simon and Ezer, Timur and Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {EXPERT SURVEYS TO REAL TIME ADAPTATION OF 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.2025.1571}, pages = {5677 -- 5687}, abstract = {Learning management systems rely on adaptive algorithms that use learner preferences to personalize the instructional content in form of learning paths. However, these preferences are uncertain in nature, and change over time. The present solutions are either static or purely data-driven missing the dynamic adaption to changes in the preferences and infusion of pedagogical nuances respectively. This paper introduces an extended variant of Nestor, our Bayesian network engine that models personality traits, learning styles, and learning strategies. This extension overlays a lightweight rule-based mechanism whose "secret recipe'' lies in the infusion of expert-derived weights adapting learning paths dynamically whenever a learner selects new material in Moodle. To parameterise these rules, we conducted a structured survey with 12 hand-picked professors and researchers in educational science. Each expert responded to 4 demographic items and 12 item that are distributed across algorithm-overview, scenario-based, and example-based categories, thereby supplying the nuanced weightings that result the personalised recommendations. This hybrid system (Nestor plus the expert-infused rule layer) operated during the winter term of 2025. 18 students completed an end-of-term questionnaire. Although their learning gains were not recorded, the majority of respondents reported positive or neutral experiences with the dynamically adapted learning paths. The {Future work} will compare three engines: (i) the present dynamic, expert-infused rule layer on top of the static Bayesian network, (ii) purely data-driven machine-learning models that neglect expert weighting, and (iii) the original static-adaptation Bayesian network without rules. Analyses of log files, intermediate satisfaction surveys, and pre/post term surveys will clarify whether this on-the-fly adaptation and pedagogical nuance lead to measurable learning benefits.}, language = {en} } @inproceedings{NadimpalliHauserBittneretal., author = {Nadimpalli, Vamsi Krishna and Hauser, Florian and Bittner, Dominik and Grabinger, Lisa and Staufer, Susanne and Mottok, J{\"u}rgen}, title = {Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning Management Systems}, 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}, editor = {Mottok, J{\"u}rgen}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {978-1-4503-9956-2}, doi = {10.1145/3593663.3593681}, pages = {83 -- 92}, abstract = {Nowadays, learning management systems are widely employed in all educational institutions to instruct students as a result of the increasing in online usage. Today's learning management systems provide learning paths without personalizing them to the characteristics of the learner. Therefore, research these days is concentrated on employing AI-based strategies to personalize the systems. However, there are many different AI algorithms, making it challenging to determine which ones are most suited for taking into account the many different features of learner data and learning contents. This paper conducts a systematic literature review in order to discuss the AI-based methods that are frequently used to identify learner characteristics, organize the learning contents, recommend learning paths, and highlight their advantages and disadvantages.}, language = {en} } @inproceedings{BittnerEzerGrabingeretal., author = {Bittner, Dominik and Ezer, Timur and Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Unveiling the secrets of learning styles: decoding eye movements via machine learning}, 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}, isbn = {978-84-09-55942-8}, doi = {10.21125/iceri.2023.1291}, pages = {5153 -- 5162}, abstract = {Universities are faced with a rising number of dropouts in recent years. This is largely due to students' limited capability of finding individual learning paths through various course materials. However, a possible solution to this problem is the introduction of adaptive learning management systems, which recommend tailored learning paths to students - based on their individual learning styles. For the classification of learning styles, the most commonly used methods are questionnaires and learning analytics. Nevertheless, both methods are prone to errors: questionnaires may give superficial answers due to lack of time or motivation, while learning analytics do not reflect offline learning behavior. This paper proposes an alternative approach to classify students' learning styles by integrating eye tracking in combination with Machine Learning (ML) algorithms. Incorporating eye tracking technology into the classification process eliminates the potential problems arising from questionnaires or learning analytics by providing a more objective and detailed analysis of the subject's behavior. Moreover, this approach allows for a deeper understanding of subconscious processes and provides valuable insights into the individualized learning preferences of students. In order to demonstrate this approach, an eye tracking study is conducted with 117 participants using the Tobii Pro Fusion. Using qualitative and quantitative analyses, certain patterns in the subjects' gaze behavior are assigned to their learning styles given by the validated Index of Learning Styles (ILS) questionnaire. In short, this paper presents an innovative solution to the challenges associated with classifying students' learning styles. By combining eye tracking data with ML algorithms, an accurate and insightful understanding of students' individual learning paths can be achieved, ultimately leading to improved educational outcomes and reduced dropout rates.}, language = {en} } @misc{BittnerEzerGrabingeretal., author = {Bittner, Dominik and Ezer, Timur and Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Eye Tracking based Learning Style Identification for Learning Management Systems [Data set]}, doi = {10.5281/zenodo.8349468}, abstract = {In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.}, language = {en} } @misc{GrabingerHomannHauseretal., author = {Grabinger, Lisa and Homann, Alexander and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Study: MISRA C coding guidelines [Data set]}, doi = {10.5281/zenodo.7898606}, abstract = {This repository contains the material and obtained data of an eye tracking study on the topic "MISRA C coding guidelines".}, language = {en} } @misc{GrabingerHauserMottok, author = {Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Study: Notation of Causal Graphs [Data set]}, doi = {10.5281/zenodo.7241158}, abstract = {This repository contains the material and obtained data of an eye tracking study on the topic "Notation of Causal Graphs".}, language = {en} } @misc{GrabingerHauserMottok, author = {Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Study: Layout of Causal Graphs [Data set]}, doi = {10.5281/zenodo.7241097}, abstract = {This repository contains the material and obtained data of an eye tracking study on the topic "Layout of Causal Graphs".}, language = {en} } @article{GrabingerHauserWolffetal., author = {Grabinger, Lisa and Hauser, Florian and Wolff, Christian and Mottok, J{\"u}rgen}, title = {On Eye Tracking in Software Engineering}, series = {SN Computer Science}, volume = {5}, journal = {SN Computer Science}, number = {6}, publisher = {Springer}, address = {Singapore}, issn = {2661-8907}, doi = {10.1007/s42979-024-03045-3}, abstract = {Eye tracking is becoming more and more important as a research method within the field of software engineering (SE). Existing meta-analyses focus on the design or conduct of SE eye tracking studies rather than the analysis phase. This article attempts to fill this gap; it presents a systematic literature review of eye tracking studies in the field of SE—focusing mainly on the data analysis methods used. From the IEEE Xplore and ACM digital libraries we gather 125 papers up to the first quarter of 2024. Detailed evaluation provides information on the number of papers that use specific methods of analysis (i.e., descriptive or inferential statistics, and gaze visualization) or settings (e.g., sample size, technical setup, and selected aspects of research design). With the data obtained we can infer the popularity of specific analysis methods in the field. Those results enable efficient work on data analysis tools or education of aspiring researchers and can serve as basis for standardization or guidelines within the community—providing for methods to include as well as current inconsistencies.}, language = {en} }