@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{RoehrlStauferBugertetal., author = {R{\"o}hrl, Simon and Staufer, Susanne and Bugert, Flemming and Nadimpalli, Vamsi Krishna and Hauser, Florian and Grabinger, Lisa and Bittner, Dominik and Ezer, Timur and Altweck, Ricardo and Graf, Sabine and Kriza, Thomas and Mottok, J{\"u}rgen}, title = {Ethical Considerations of AI in Education: A Case Study based on Pythia Learning Enhancement System}, series = {IEEE Access}, volume = {11}, journal = {IEEE Access}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {2169-3536}, doi = {10.1109/ACCESS.2025.3583975}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-83403}, pages = {1 -- 25}, abstract = {As the use of AI in education increases, addressing ethical concerns is essential for its responsible use and implementation. This study explores the ethical considerations of AI in education by analyzing a concrete application, the Pythia Learning Enhancement System, which enables personalized and adaptive educational experiences. In this case study, the concrete implementation of Pythia, contrary to high-level abstract analyses, provides practical insights into these ethical issues. To achieve this goal, we first define Pythia and outline its implementation. Using the various topics covered by Pythia, the ethical issues associated with each of these topics are outlined, resulting in a list of ten distinct ethical issues. Because there is overlap in issues covered by similar applications, they can benefit from this identification. An ethical (human rights) and didactic (constructivism) theoretical background is then provided as a basis for further discussion. This is used to build the arguments used in the main study. This consists of analyzing each of the issues and providing recommendations to mitigate potential harm arising from these issues. This helps to provide guidelines for future applications. In the analysis, the ethical discussion is mainly based on the human rights of the stakeholders involved, emphasizing human dignity. After the analysis, each of the issues is contextualized with the recent AI Act of the European Union, providing a novel mapping of the issues of AI in education to the AI Act. Finally, the limitations of the study are stated and a plan for future research is presented.}, language = {en} } @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{BugertGrabingerBittneretal., author = {Bugert, Flemming and Grabinger, Lisa and Bittner, Dominik and Hauser, Florian and Nadimpalli, Vamsi Krishna and Staufer, Susanne and Mottok, J{\"u}rgen}, title = {Towards Learning Style Prediction based on Personality}, series = {Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, booktitle = {Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, publisher = {ACM}, isbn = {978-1-4503-9956-2}, doi = {10.1145/3593663.3593682}, pages = {48 -- 55}, abstract = {This paper assesses the relation between personality, demographics, and learning style. Hence, data is collected from 200 participants using 1) the BFI-10 to obtain the participant's expression of personality traits according to the five-factor model, 2) the ILS to determine the participant's learning style according to Felder and Silverman, and 3) a demographic questionnaire. From the obtained data, we train and evaluate a Bayesian network. Using Bayesian statistics, we show that age and gender slightly influence personality and that demographics as well as personality have at least a minor effect on learning styles. We also discuss the limitations and future work of the presented approach.}, language = {en} } @inproceedings{BittnerHauserNadimpallietal., author = {Bittner, Dominik and Hauser, Florian and Nadimpalli, Vamsi Krishna and Grabinger, Lisa and Staufer, Susanne and Mottok, J{\"u}rgen}, title = {Towards Eye Tracking based Learning Style Identification}, series = {Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, booktitle = {Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023}, publisher = {ACM}, isbn = {978-1-4503-9956-2}, doi = {10.1145/3593663.3593680}, pages = {138 -- 147}, abstract = {The dropout rate at universities has been very high for years. Thereby, the inexperience and lack of knowledge of students in dealing with individual learning paths in various courses of study plays a decisive role. Adaptive learning management systems are suitable countermeasures, in which learners' learning styles are classified using questionnaires or computationally intensive algorithms before a learning path is suggested accordingly. In this paper, a study design for student learning style classification using eye tracking is presented. Furthermore, qualitative and quantitative analyses clarify certain relationships between students' eye movements and learning styles. With the help of classification based on eye tracking, the filling out of questionnaires or the integration of computationally or cost-intensive algorithms can be made redundant in the future.}, language = {en} } @inproceedings{NadimpalliBugertBittneretal., author = {Nadimpalli, Vamsi Krishna and Bugert, Flemming and Bittner, Dominik and Staufer, Susanne and R{\"o}hrl, Simon and Hauser, Florian and Ezer, Timur and Grabinger, Lisa and Maier, Robert and Mottok, J{\"u}rgen}, title = {Probabilistic Machine Learning for Simulating Complex Learner Profiles}, series = {2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), 06-08 November 2024, Paris, France}, booktitle = {2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), 06-08 November 2024, Paris, France}, publisher = {IEEE}, isbn = {979-8-3315-1663-5}, issn = {2473-2060}, doi = {10.1109/ITHET61869.2024.10837641}, pages = {1 -- 10}, abstract = {Data is the foundation of all machine learning applications. In education science, especially for the learner characteristics that drive personalized learning, it is difficult to collect and often uncertain. It is challenging to model, train, evaluate, and analyze the underlying algorithms when developing AI-based systems and having small sample sizes. To address these problems, we present a synthetic data generator utilising probabilistic models. This generator can effectively model and simulate complex learner profiles. To achieve this, we collected extensive data on learning styles, learning strategies, personalities, and preferred learning paths from 593 students over several semesters at a higher education level. Then, Bayesian networks, Hidden Markov Models, and Markov Chains are used to model the relationships between learner profiles. Using the Bayesian information criterion, and cross-validation with log-likelihood scores, we compare various models to select the best fitting one for synthesizing the data. The synthetic data is then evaluated using statistical validation techniques. In addition, we developed a simulation module with the option to simulate learner profiles based on manual user-defined inputs. The data and code used in this work are available as open source1110.5281/zenodo.13768136 [Titel anhand dieser DOI in Citavi-Projekt {\"u}bernehmen] contributing to open science and developers for customized simulated data. In the future, this data will refine the training, evaluation, analysis, and benchmarking of algorithms for personalized learning.}, language = {en} } @inproceedings{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} }