TY - CHAP A1 - Röhrl, Simon A1 - Staufer, Susanne A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Bugert, Flemming A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Bittner, Dominik A1 - Ezer, Timur A1 - Mottok, Jürgen T1 - PYTHIA - AI SUGGESTED INDIVIDUAL LEARNING PATHS FOR EVERY STUDENT T2 - INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024 N2 - 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. KW - Personalized Learning Paths KW - Learning Management System KW - Software Architecture KW - Moodle KW - Artificial Intelligence Y1 - 2024 SN - 978-84-09-59215-9 U6 - https://doi.org/10.21125/inted.2024.0783 SN - 2340-1079 SP - 2871 EP - 2880 ER - TY - CHAP A1 - Staufer, Susanne A1 - Hauser, Florian A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Nadimpalli, Vamsi Krishna A1 - Röhrl, Simon A1 - Bugert, Flemming A1 - Bittner, Dominik A1 - Mottok, Jürgen T1 - EVALUATION OF THE LEARNING MANAGEMENT SYSTEM PYTHIA T2 - EDULEARN Proceedings: 16th International Conference on Education and New Learning Technologies, Palma, Spain. 1-3 July, 2024 N2 - 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. KW - Learning management system (LMS) KW - questionnaire (study) KW - learning elements KW - learning paths KW - usability KW - graphical user interface (GUI) KW - Moodle KW - higher education area (HEA) Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-73474 SN - 2340-1117 SP - 9089 EP - 9098 PB - IATED ER - TY - CHAP A1 - Staufer, Susanne A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Bittner, Dominik A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Ezer, Timur A1 - Röhrl, Simon A1 - Mottok, Jürgen T1 - Learning elements in LMS - a survey among students T2 - INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024 N2 - 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. KW - Learning elements KW - learning management system KW - learning objects KW - questionnaire KW - higher education area KW - content organization Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-71135 SN - 978-84-09-59215-9 SN - 2340-1079 SP - 4224 EP - 4231 PB - IATED ER - TY - CHAP A1 - Staufer, Susanne A1 - Nadimpalli, Vamsi Krishna A1 - Hauser, Florian A1 - Bittner, Dominik A1 - Grabinger, Lisa A1 - Bugert, Flemming A1 - Ezer, Timur A1 - Röhrl, Simon A1 - Mottok, Jürgen T1 - Persistence of Questionnaire Data on Learning Styles, Learning Strategies AND Personality Traits T2 - ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain N2 - 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. KW - learning style KW - learning strategy KW - personality traits KW - questionnaire study Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-78246 SN - 978-84-09-63010-3 SN - 2340-1095 SP - 6310 EP - 6319 PB - IATED ER - TY - JOUR A1 - Röhrl, Simon A1 - Staufer, Susanne A1 - Bugert, Flemming A1 - Nadimpalli, Vamsi Krishna A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Bittner, Dominik A1 - Ezer, Timur A1 - Altweck, Ricardo A1 - Graf, Sabine A1 - Kriza, Thomas A1 - Mottok, Jürgen T1 - Ethical Considerations of AI in Education: A Case Study based on Pythia Learning Enhancement System JF - IEEE Access N2 - As the use of AI in education increases, addressing ethical concerns is essential for its responsible use and implementation. This study explores the ethical considerations of AI in education by analyzing a concrete application, the Pythia Learning Enhancement System, which enables personalized and adaptive educational experiences. In this case study, the concrete implementation of Pythia, contrary to high-level abstract analyses, provides practical insights into these ethical issues. To achieve this goal, we first define Pythia and outline its implementation. Using the various topics covered by Pythia, the ethical issues associated with each of these topics are outlined, resulting in a list of ten distinct ethical issues. Because there is overlap in issues covered by similar applications, they can benefit from this identification. An ethical (human rights) and didactic (constructivism) theoretical background is then provided as a basis for further discussion. This is used to build the arguments used in the main study. This consists of analyzing each of the issues and providing recommendations to mitigate potential harm arising from these issues. This helps to provide guidelines for future applications. In the analysis, the ethical discussion is mainly based on the human rights of the stakeholders involved, emphasizing human dignity. After the analysis, each of the issues is contextualized with the recent AI Act of the European Union, providing a novel mapping of the issues of AI in education to the AI Act. Finally, the limitations of the study are stated and a plan for future research is presented. KW - Ethics KW - Artificial intelligence KW - Education KW - Stakeholders KW - Psychology KW - Privacy KW - Search problems KW - Guidelines KW - Focusing KW - Europe Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-83403 SN - 2169-3536 N1 - Corresponding author der OTH Regensburg: Simon Röhrl VL - 11 SP - 1 EP - 25 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - CHAP A1 - Bugert, Flemming A1 - Staufer, Susanne A1 - Bittner, Dominik A1 - Nadimpalli, Vamsi Krishna A1 - Ezer, Timur A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Mottok, Jürgen T1 - Ariadne's Thread for Unravelling Learning Paths: Identifying Learning Styles via Hidden Markov Models T2 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece N2 - 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. KW - Surveys KW - Knowledge engineering KW - Feedback loop KW - Learning management systems KW - Uncertainty KW - Viterbi algorithm Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578825 SN - 2165-9567 SP - 1 EP - 7 PB - IEEE ER - TY - CHAP A1 - Bugert, Flemming A1 - Grabinger, Lisa A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Nadimpalli, Vamsi Krishna A1 - Staufer, Susanne A1 - Mottok, Jürgen T1 - Towards Learning Style Prediction based on Personality T2 - Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023 N2 - 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. Y1 - 2023 SN - 978-1-4503-9956-2 U6 - https://doi.org/10.1145/3593663.3593682 SP - 48 EP - 55 PB - ACM ER - TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Staufer, Susanne A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Towards personalized learning paths in adaptive learning management systems: bayesian modelling of psychological theories T2 - Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023 N2 - 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. Y1 - 2023 U6 - https://doi.org/10.21125/iceri.2023.1144 SP - 4593 EP - 4603 PB - IATED ER - TY - CHAP A1 - Ezer, Timur A1 - Plößl, Moritz A1 - Grabinger, Lisa A1 - Bittner, Dominik A1 - Staufer, Susanne A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Hauser, Florian A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Deep learning for eye movement classification T2 - ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain Y1 - 2024 U6 - https://doi.org/10.21125/iceri.2024.1028 SP - 4056 EP - 4065 PB - IATED ER - TY - CHAP A1 - Bugert, Flemming A1 - Nadimpalli, Vamsi Krishna A1 - Bittner, Dominik A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Maier, Robert A1 - Röhrl, Simon A1 - Staufer, Susanne A1 - Hauser, Florian A1 - Mottok, Jürgen ED - Mottok, Jürgen ED - Hagel, Georg T1 - ML based Evaluation Methodology for Learning Path Recommender Systems T2 - Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025 N2 - 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. Y1 - 2025 SN - 9798400712821 U6 - https://doi.org/10.1145/3723010.3723022 SP - 40 EP - 48 PB - ACM ER - TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Bugert, Flemming A1 - Bittner, Dominik A1 - Staufer, Susanne A1 - Röhrl, Simon A1 - Hauser, Florian A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Maier, Robert A1 - Mottok, Jürgen T1 - Probabilistic Machine Learning for Simulating Complex Learner Profiles T2 - 2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), 06-08 November 2024, Paris, France N2 - 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 übernehmen] 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. KW - probabilistic models KW - synthetic data KW - learner characteristics KW - generative AI KW - higher education area Y1 - 2024 SN - 979-8-3315-1663-5 U6 - https://doi.org/10.1109/ITHET61869.2024.10837641 SN - 2473-2060 SP - 1 EP - 10 PB - IEEE ER - TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Staufer, Susanne A1 - Ezer, Timur A1 - Bugert, Flemming A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Röhrl, Simon A1 - Maier, Robert A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Predicting learner characteristics using machine learning T2 - ICERI 2024 Proceedings, 17th annual International Conference of Education, Research and Innovation,11-13 November, 2024, Seville, Spain N2 - 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 Y1 - 2024 U6 - https://doi.org/10.21125/iceri.2024.1454 SP - 6005 EP - 6014 PB - IATED ER - TY - CHAP A1 - Staufer, Susanne A1 - Bugert, Flemming A1 - Hauser, Florian A1 - Grabinger, Lisa A1 - Ezer, Timur A1 - Nadimpalli, Vamsi Krishna A1 - Bittner, Dominik A1 - Röhrl, Simon A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Tyche algorithm: Markov models for generating learning paths in learning management systems T2 - INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024 N2 - 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. KW - Markov model KW - learning style KW - learning elements KW - learning management system KW - higher education area Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-71148 SN - 978-84-09-59215-9 SN - 2340-1079 SP - 4195 EP - 4205 PB - IATED Academy ER - TY - GEN A1 - Bugert, Flemming T1 - Applying Ariadne: Dataset on Learning Styles and Moodle-Based Learning Paths [Data set] N2 - This dataset contains information queried from 22 students inside a Moodle based learning management system during the winter term 2023/24 at a German university. Abstract With the use of learning management systems students benefit from being recommended suitable learning elements based on their individual needs. In doing so, recommendation algorithms are applied which first query the student’s learning style. To improve the recommendation of learning elements a continuous analysis of the individual’s learning style is required. A frequent questionnaire assessment would however be too time consuming. Instead, in a prior study an algorithm has been designed to identify changes in learning styles from the student’s selection of learning elements. In this paper, we investigate the functionality of that algorithm by applying it on real student data. In particular, we test if the algorithm correctly indicates changes in learning styles. The utilised data is collected in our learning management system. To be precise, the data is obtained from 22 students enrolled in a software engineering course during the winter term of 2023/24. The data comprises two types of information for each student: 1) learning style collected at the start and end of the term, and 2) the user’s actual selection of learning elements inside the learning management system. The uniqueness of this study lies in the data and the evaluation strategy based on it. Having the learning style at the end of the semester period as ground truth allows us to test if the algorithm operates correctly with actual user data from our learning management system. The results validate the behaviour of our algorithm, yet they strongly suggest the need for an adaptation. Further research is required on how to parameterise the underlying models. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.12594911 ER -