@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} } @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{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{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} } @unpublished{GaubeJussupowKokjeetal., author = {Gaube, Susanne and Jussupow, Ekaterina and Kokje, Eesha and Khan, Jowaria and Bondi-Kelly, Elizabeth and Schicho, Andreas and Kitamura, Felipe Campos and Koch, Timo Kevin and Ezer, Timur and Mottok, J{\"u}rgen and Lermer, Eva and Ghassemi, Marzyeh and Colak, Errol}, title = {Underreliance Harms Human-AI Collaboration More Than Overreliance in Medical Imaging}, publisher = {OSFPreprints}, doi = {10.31219/osf.io/4wv8j}, abstract = {Importance: The use of artificial intelligence (AI) to support clinicians in diagnostic decision-making holds significant potential; however, evidence regarding its clinical utility remains mixed. In many cases, the interaction between healthcare professionals and AI systems does not improve collaborative performance compared to the standalone performance of humans or AI. Currently, the underlying mechanisms that limit human-AI collaboration are poorly understood. Objective: To examine the impact of AI advice on diagnostic decision-making among experts and novices, focusing on understanding the role of explainability (XAI) on users' reliance on advice. Design, Setting, and Participants: A mixed-methods design combining a crossover experimental design with a think-aloud and an eye-tracking study arm was conducted in 2023. Participants were task experts (radiologists) and novices (non-radiologist physicians and medical trainees) from 10 countries, with the think-aloud and eye-tracking conducted in Germany. Intervention: Participants reviewed 50 patient cases containing head CT scans and patient information. Every case was reviewed in three time-separate sessions in randomized order. In each session, participants were exposed to a different experimental condition: (a) control, i.e., no AI prediction presented; (b) basic advice, i.e., AI prediction without annotations; and (c) XAI advice, i.e., AI prediction with annotations. For each case, participants had to determine if the patients had an intracranial hemorrhage (ICH), rate their confidence, and, if applicable, the usefulness of the AI advice. Main Outcome(s) and Measure(s): Diagnostic performance, confidence in the diagnosis, case reading time, and AI advice usefulness ratings. Results: The data analysis included 125 participants. The mean age was 28.5 years (SD = 6.72), and 55.2\% identified as female. Underreliance on correct AI advice was associated with high uncertainty and had a more detrimental impact on diagnostic performance than overreliance on incorrect advice. XAI advice reduced underreliance and improved performance and confidence, particularly when reviewing more difficult cases with ICH. AI advice, particularly XAI, did not reduce reading time. XAI was perceived as more useful than basic AI advice, especially among novices. Conclusions and Relevance: Our findings indicate that underreliance on AI might be more harmful than overreliance, highlighting the need to develop efficient counterstrategies beyond current XAI methods.}, language = {en} } @inproceedings{GrabingerEzerHauseretal., author = {Grabinger, Lisa and Ezer, Timur and Hauser, Florian and Mottok, J{\"u}rgen}, title = {The impact of eyenalyzer}, series = {ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain}, booktitle = {ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain}, publisher = {IATED}, isbn = {978-84-09-63010-3}, issn = {2340-1095}, doi = {10.21125/iceri.2024.0271}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-78215}, pages = {695 -- 701}, abstract = {Empirical research poses numerous challenges for beginners. This is especially true for data analysis - a task that usually requires knowledge from two distinct areas: statistics and programming. To support prospective researchers with that task, we developed a web-based tool called eyenalyzer. It supports common activities in the data analysis phase of empirical studies in a way that is suitable for novices in both, statistics and programming. The present article describes a controlled experiment investigating the impact of this tool with a total of 20 participants. All of them are given a set of common data analysis tasks. Half of the participants complete the tasks using eyenalyzer, the other half can use anything except for eyenalyzer. For each task and participant, we record the time in minutes, the task score, and the perceived difficulty. The results confirm that our tool is a valuable support for novice researchers: With eyenalyzer, the participants are significantly faster, achieve higher scores, and perceive the tasks to be less difficult.}, language = {en} } @inproceedings{StauferNadimpalliHauseretal., author = {Staufer, Susanne and Nadimpalli, Vamsi Krishna and Hauser, Florian and Bittner, Dominik and Grabinger, Lisa and Bugert, Flemming and Ezer, Timur and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {Persistence of Questionnaire Data on Learning Styles, Learning Strategies AND Personality Traits}, series = {ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain}, booktitle = {ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain}, publisher = {IATED}, isbn = {978-84-09-63010-3}, issn = {2340-1095}, doi = {10.21125/iceri.2024.1524}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-78246}, pages = {6310 -- 6319}, abstract = {In the context of learning, questionnaires are carried out to gain information about learners. This learner profile has various benefits, like adapting the learning content, promoting of self-reflection, or increasing motivation in learning and therefore better learning results. Some researchers take the results of such questionnaires as ground truth or trust them blindly. Examining how persistent questionnaires are, asking the same questions twice, and comparing the results is a way to prove or falsify this statement. Our research procedure compares the results of three different questionnaires. The first questionnaire is the ILS (index of learning styles) according to the Felder-Silverman Learning Style Model (FSLSM) with about 44 questions. Learning strategies are examined with the LIST-K questionnaire consisting of 39 questions after Klingsieck. The third and last questionnaire is about personality traits. With only ten questions, Rammstedt et al. describe the BFI-10 questionnaire for personality traits. To compare results, the same study with the three named questionnaires is carried out twice with three to four months in between. The sample includes 35 students in their bachelor studies taking the course Software Engineering in a blended learning format. After collecting the questionnaire data from the students, a comparison is made in two different ways. First, we compare the data points based on the given answers. Second, a comparison is made based on the calculated questionnaire results because each of the three questionnaires has its own analysis method. For example, the analysis method of the ILS delivers as a result four integers representing the four characteristics of a learning style, while LIST-K makes a factor analysis. The results of the paper show that both the given answers and the calculated questionnaire results differ. Not one student has given the same answer inside each questionnaire with causes, like a change of learning style, learning strategy, and personality traits, or not filling in conscientiously.}, language = {en} } @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{StauferHauserEzeretal., author = {Staufer, Susanne and Hauser, Florian and Ezer, Timur and Grabinger, Lisa and Nadimpalli, Vamsi Krishna and R{\"o}hrl, Simon and Bugert, Flemming and Bittner, Dominik and Mottok, J{\"u}rgen}, title = {EVALUATION OF THE LEARNING MANAGEMENT SYSTEM PYTHIA}, series = {EDULEARN Proceedings: 16th International Conference on Education and New Learning Technologies, Palma, Spain. 1-3 July, 2024}, booktitle = {EDULEARN Proceedings: 16th International Conference on Education and New Learning Technologies, Palma, Spain. 1-3 July, 2024}, publisher = {IATED}, issn = {2340-1117}, doi = {10.21125/edulearn.2024.2189}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-73474}, pages = {9089 -- 9098}, abstract = {Learning management systems gain importance due to the Corona pandemic. To personalize such a learning management system, a Moodle adaption named Pythia was created. Pythia is implemented as a Moodle plugin solution. The possibility of generating learning paths with various algorithms and the change of the graphical user interface is significant. Learning paths are generated with two different algorithms. Nestor algorithm generates learning paths with a Bayesian network, while Tyche algorithm uses a Markov model. The graphical user interface is adapted to the generated learning paths where learning element symbols are customized and the individual learning paths are depicted. This paper presents the results of a qualitative survey among German students with the aim of evaluating the first version of the learning management system Pythia. 25 students took the subject "Software Engineering for Safe and Secure Systems" in the winter term of 2023/24. The 68 asked questions focus on usage behavior, graphical user interface, usability, preferences, volume, and quality of learning elements, and learning paths. Our Moodle course for the lecture contains seven sections, each section has minimum one subsection on which the learning path is calculated. Last three sections are designed by presenting minimum one learning element of each learning element category except the collaboration tool. In a previous work, ten learning element categories were designed and evaluated comprising manuscript, brief overview, learning goal, quiz, exercise, summary, three types of additional material, and collaboration tool. The first sections are left as they are initially designed by the lecturer (normal section). The survey considers the difference between normal sections and diverse sections. Furthermore, the generated learning paths are evaluated. 13 students undertook Nestor learning paths, whereas 12 undertook Tyche learning paths. The results suggest that for the majority of students, the learning element symbols are well-chosen. Moreover, all students wish the elements relevant to the exam to be labelled. 23 students are overwhelmed by the number of learning elements in the last sections of the Moodle course. In the future, some suggestions will be implemented such as the labelling of exam relevant learning elements. To overcome the overload of learning elements, a strategy could be developed like hiding learning elements not preferred by the learning path algorithm.}, language = {en} } @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{StauferBugertHauseretal., author = {Staufer, Susanne and Bugert, Flemming and Hauser, Florian and Grabinger, Lisa and Ezer, Timur and Nadimpalli, Vamsi Krishna and Bittner, Dominik and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {Tyche algorithm: Markov models for generating learning paths in learning management systems}, series = {INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024}, booktitle = {INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED Academy}, isbn = {978-84-09-59215-9}, issn = {2340-1079}, doi = {10.21125/inted.2024.1080}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-71148}, pages = {4195 -- 4205}, abstract = {In the intricate tapestry of the cosmos, where celestial threads weave stories of destiny, an enigmatic deity stands at the intersection of chance and fate — Tyche, the goddess of fortune. In science and probability, coincidence plays a distinctive role in Bayesian Networks (BNs) and Markov Models (MMs). This paper introduces the Tyche algorithm named after the goddess of fortune. The Tyche algorithm is a Markov model designed to generate learning paths in Learning Management Systems (LMSs). A learning path is a type of individualization that personalises the order of learning elements within an LMS course. Learning elements are fundamental components within an LMS course, depicting the learning content in diverse ways. In our case, the learning path tailoring is based on the learner's learning style according to Felder-Silverman Learning Style Model (FSLSM) - an indicator for the ideal pathway and learning element for the learner's optimum learning. The Tyche algorithm offers the advantage to provide students the most suitable learning path. Tyche is a MM structure with various matrices containing transition probabilities depending on the learning style. Nine categories of learning elements defined in a previous survey form the basis of the node structure of the MM. For a generic approach, a survey was designed to obtain the transition probabilities depending on the individual learning style. The survey with more than 100 German students participated is processed with the tool LimeSurvey. Students are asked about their learning style using the Index of Learning Styles (ILS) questionnaire according to Felder-Silverman and about the percentage probabilities of learning elements to get their individual sequence as learning path. The percentages are queried in two different ways. Firstly, the students were asked to provide a sequence of learning elements within each position of the sequence filled with probabilities for all learning elements. Secondly, a learning element is given and the students are asked to indicate which learning element they would work on next. The first way of asking for the probabilities is used to find the start node in the MM, whereas the second approach forms the probability matrices between the nodes within the MM. As result of the survey, the Tyche algorithm presents generic transition probabilities. It improves the learning process of individuals only by asking for their learning style: it generates individual learning paths through the learning elements within an LMS based on the MM explained above by solely getting the answers of the ILS questionnaire as input. In the future, other questionnaires such as BFI-10 for personality traits or LIST-K for learning strategies may offer a more comprehensive input. However, the next step is to evaluate Tyche with about 25 students in a software engineering lecture. This is planned for the year 2024.}, language = {en} } @inproceedings{StauferHauserGrabingeretal., author = {Staufer, Susanne and Hauser, Florian and Grabinger, Lisa and Bittner, Dominik and Nadimpalli, Vamsi Krishna and Bugert, Flemming and Ezer, Timur and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {Learning elements in LMS - a survey among students}, series = {INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024}, booktitle = {INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024}, publisher = {IATED}, isbn = {978-84-09-59215-9}, issn = {2340-1079}, doi = {10.21125/inted.2024.1087}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-71135}, pages = {4224 -- 4231}, abstract = {In higher education, improving learning and learning success are goals of general improvement. Lecturers teaches content and students acquire that content in an efficient way. To structure content, learning element categories are evaluated from the student's point of view in higher education area. The aim is to validate given definitions of ten learning element categories within a Learning Management System (LMS). This paper evaluates a categorization of learning elements for organizing learning content in online education within LMSs. Therefore, ten categories of learning elements and corresponding definitions were defined in a previous work as base for this paper. The learning elements to examine are manuscript, exercise, quiz, brief overview, learning goal, summary, collaboration tool, auditory additional material, textual additional material, and visual additional material. To validate the definitions and to get improvements to each learning element a survey is processed. Beside the demographic data questions, the survey consists of two questions to the acceptance of the definitions and asks for improvements. 148 students between the ages 19 and 35 participate in the survey in summer term 2023. The education level of the participants ranges from undergraduates to Ph.D. students. The results of this paper are that more than 80\% accept the given definitions. Some definitions of the learning elements are changed, but the changes are restricted to additions of maximal four words. This categorization of learning elements could lead to improvements in learning by giving the content more structure. With the structure students get the possibility to learn with preferred learning elements which could lead to more success in learning and to a decreasing dropout rate in universities. In the future, the learning elements allow to classify content within LMSs with the goal of generating individual learning paths. Furthermore, our project will integrate these learning elements, use them to generate learning paths, and could set a new standard in the way of personalized learning.}, language = {en} }