@inproceedings{StauferHauserGrabingeretal., author = {Staufer, Susanne and Hauser, Florian and Grabinger, Lisa and Bittner, Dominik and Nadimpalli, Vamsi Krishna and Bugert, Flemming and Ezer, Timurt and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {Learning elements in LMS - a survey among students}, series = {18th International Technology, Education and Development Conference, 4-6 March 2024, Valencia, Spain}, booktitle = {18th International Technology, Education and Development Conference, 4-6 March 2024, Valencia, Spain}, publisher = {IATED}, isbn = {978-84-09-59215-9}, issn = {2340-1079}, doi = {10.21125/inted.2024.1087}, 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} } @article{MaierSchlattlGuessetal., author = {Maier, Robert and Schlattl, Andreas and Guess, Thomas and Mottok, J{\"u}rgen}, title = {CausalOps - Towards an industrial lifecycle for causal probabilistic graphical models}, series = {Information and Software Technology}, journal = {Information and Software Technology}, publisher = {Elsevier}, issn = {0950-5849}, doi = {10.1016/j.infsof.2024.107520}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-73350}, abstract = {Context: Causal probabilistic graph-based models have gained widespread utility, enabling the modeling of cause-and-effect relationships across diverse domains. With their rising adoption in new areas, such as safety analysis of complex systems, software engineering, and machine learning, the need for an integrated lifecycle framework akin to DevOps and MLOps has emerged. Currently, such a reference for organizations interested in employing causal engineering is missing. This lack of guidance hinders the incorporation and maturation of causal methods in the context of real-life applications. Objective: This work contextualizes causal model usage across different stages and stakeholders and outlines a holistic view of creating and maintaining them within the process landscape of an organization. Method: A novel lifecycle framework for causal model development and application called CausalOps is proposed. By defining key entities, dependencies, and intermediate artifacts generated during causal engineering, a consistent vocabulary and workflow model to guide organizations in adopting causal methods are established. Results: Based on the early adoption of the discussed methodology to a real-life problem within the automotive domain, an experience report underlining the practicability and challenges of the proposed approach is discussed. Conclusion: It is concluded that besides current technical advancements in various aspects of causal engineering, an overarching lifecycle framework that integrates these methods into organizational practices is missing. Although diverse skills from adjacent disciplines are widely available, guidance on how to transfer these assets into causality-driven practices still need to be addressed in the published literature. CausalOps' aim is to set a baseline for the adoption of causal methods in practical applications within interested organizations and the causality community.}, 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} } @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{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{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{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} } @article{DePremekMottok, author = {De, Sangita and Premek, Brada and Mottok, J{\"u}rgen}, title = {Derivation of Optimized Threshold of Semantic Alignment Metrics for Intepretation of Interoperability and Reusability of Cross-enterprise Vehicle Service Interface Models}, series = {Informatica}, volume = {47}, journal = {Informatica}, number = {7}, publisher = {Slovenian Society Informatika}, issn = {0350-5596}, doi = {10.31449/inf.v47i7.4739}, abstract = {Over the past decade, cars have turned gradually into real cyber physical systems. The collaboration of services between the service-oriented, cross-enterprise vehicle application frameworks has increased to generate novel, smart and complicated vehicle services. Consequently, from an interoperability perspective, semantically mapping of vehicle service component's interface ontological models emerged as a big research interest in automotive application domain that manipulates several cross-enterprise synergy knowledge applications frameworks. The ontological metamodeling lays the foundation for building semantic bridge and exploring semantic associations between service components' interface models based on the domain knowledge for semantic interoperability. Also, several semantic quality metrics has been defined over time for the vehicle service interface ontological metamodels. The empirically evaluated values of these metrics can be used to assess progress in cross-enterprise interoperability between the service and the clients' APIs ontological models in vehicle domain. Despite potential benefits of semantic alignment quality metrics, the effective use of these metrics for vehicle service interface ontologies have proven elusive. Yes, such metrics can be used successfully for quantification, but then they mostly fail to provide adequate annotations in subsequent decision-making in the direction of semantic interoperability and reusability. The effective use of ontology semantic alignment quality metrics is basically hindered in the absence of the meaningful thresholds. In fact, the absence of an effective and meaningful threshold for the semantic similarity measure between various vehicle service interface ontological metamodels, motivates this research work which not only proposes a design approach to an optimized threshold for the semantic similarity metrics but also applies this threshold on few defined semantic alignment quality metrics. This paper also uses a real-world vehicle domain industrial case study to illustrate the design approach.}, 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{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{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{BittnerHendricksHornetal., author = {Bittner, Dominik and Hendricks, Ricky-Ricardo and Horn, Luca and Mottok, J{\"u}rgen}, title = {In-depth Benchmarking of Transfer Learning Techniques for Improved Bottle Recognition}, series = {2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS), Guayaquil, Ecuador, 04-07 July 2023}, booktitle = {2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS), Guayaquil, Ecuador, 04-07 July 2023}, publisher = {IEEE}, isbn = {979-8-3503-3337-4}, doi = {10.1109/ICPRS58416.2023.10178995}, pages = {1 -- 6}, abstract = {An immense diversity in bottle types requires high accuracy during sorting for recycling purposes by breweries. This extremely complex and time-consuming procedure can result in enormous additional costs for them. This paper presents transfer learning-based algorithms for classifying beer bottle brands using camera images, applicable in individual sorting solutions for different use cases. The problem is tackled using customised EfficientNet, InceptionResNet and VGG models along with an augmented dataset. In addition, a detailed analysis of different model and parameter combinations is performed, enabling tailor-made technologies for specific conditions and resource limitations. In accompanying validations and subsequent tests, a test accuracy of 100\% in the recognition of beer brands could be achieved, proving the proposed method fully contributes to the solution of the problem.}, language = {en} } @article{MaierGrabingerUrlhartetal., author = {Maier, Robert and Grabinger, Lisa and Urlhart, David and Mottok, J{\"u}rgen}, title = {Causal Models to Support Scenario-Based Testing of ADAS}, series = {IEEE Transactions on Intelligent Transportation Systems}, journal = {IEEE Transactions on Intelligent Transportation Systems}, publisher = {IEEE}, issn = {1524-9050}, doi = {10.1109/TITS.2023.3317475}, pages = {1 -- 17}, abstract = {In modern vehicles, system complexity and technical capabilities are constantly growing. As a result, manufacturers and regulators are both increasingly challenged to ensure the reliability, safety, and intended behavior of these systems. With current methodologies, it is difficult to address the various interactions between vehicle components and environmental factors. However, model-based engineering offers a solution by allowing to abstract reality and enhancing communication among engineers and stakeholders. Applying this method requires a model format that is machine-processable, human-understandable, and mathematically sound. In addition, the model format needs to support probabilistic reasoning to account for incomplete data and knowledge about a problem domain. We propose structural causal models as a suitable framework for addressing these demands. In this article, we show how to combine data from different sources into an inferable causal model for an advanced driver-assistance system. We then consider the developed causal model for scenario-based testing to illustrate how a model-based approach can improve industrial system development processes. We conclude this paper by discussing the ongoing challenges to our approach and provide pointers for future work.}, language = {en} } @inproceedings{NadimpalliBugertBittneretal., author = {Nadimpalli, Vamsi Krishna and Bugert, Flemmimg 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{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}, pages = {3121 -- 3130}, language = {en} } @inproceedings{DeMottokBrada, author = {De, Sangita and Mottok, J{\"u}rgen and Brada, Premek}, title = {Evaluation of Semantic Interoperability of Automotive Service API Models Based on Metamodels Similarity Metrics Using a Semi-automated Approach}, series = {iCETiC 2022 : 5th International Conference on Emerging Technologies in Computing 2022: Aug 15, 2022 - Aug 16, 2022, Chester, UK}, booktitle = {iCETiC 2022 : 5th International Conference on Emerging Technologies in Computing 2022: Aug 15, 2022 - Aug 16, 2022, Chester, UK}, editor = {Miraz, M.H. and Southall, G. and Ali, M. and Ware, A.}, publisher = {Springer}, address = {Cham}, doi = {10.1007/978-3-031-25161-0_2}, pages = {14 -- 28}, abstract = {In recent years, mapping of application software components' ontologies semantically emerged as a big research challenge in automotive application domain that manipulates several cross-enterprise synergy knowledge application frameworks. The same knowledge formalized by different experts in different vehicle application frameworks leads to heterogeneous representations of components' interface data. Consequently, this causes the most daunting impediment in semantic interoperability between the service components in cooperative automotive systems. From a modeling perspective, in the absence of standardized domain-based unified modeling techniques, the orchestration and resolution of semantic data interoperability between various vehicle application frameworks' components' interface models remain a challenge. However, this challenge could be addressed using ontological metamodeling by specifying semantic associations between components' interface model concepts based on the domain knowledge. Apart from the semantic mapping of interface ontological metamodels, this work also defines quality metrics to determine the degree of semantic alignment achieved between the various interface ontologies. Additionally, to reduce development time and cost towards semantic interoperability, this work proposes a semi-automated plugin tool for the applicability of the evaluated quality metrics to semantic mapping of real-world components' interface models.}, 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{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{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} } @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} }