Dominic Lohr
[person id=”2020″ format=”page” show=”raum”]
Awards
- : Best Demo Award (DELFI (GI)) – 2024
- : Best Student Paper Award (ITiCSE 2024 (ACM)) – 2024
Lectures
No matching entries found.Publications
2025
- Betzendahl, J., Lohr, D., Berges, M., & Kohlhase, M. (2025). Efficient Exam Correction at Scale: Streamlining Paper-Based Assessments with the VoLl-KOrN System. In Mrohs, L., Franz, J., Herrmann, D., Lindner, K., Staake, T. (Eds.), Digitales Lehren und Lernen an der Hochschule Strategien - Bedingungen - Umsetzung. Bielefeld: transcript.
BibTeX: Download - Grelka, F., Lohr, D., & Berges, M. (2025, June). ALeA : Advancing Personalized Learning with Adaptive Assistance and Semantic Annotation. Paper presentation at Workshop KI und Bildung 2024, Bamberg, DE.
DOI: 10.20378/irb-108296
BibTeX: Download - Lohr, D., Berges, M., Chugh, A., Kohlhase, M., & Müller, D. (2025). Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects. Journal of Computer Assisted Learning, 41, e13101. https://doi.org/10.1111/jcal.13101
DOI: 10.1111/jcal.13101
BibTeX: Download - Lohr, D., Keuning, H., & Kiesler, N. (2025). You’re (Not) My Type - Can LLMs Generate Feedback of Specific Types for Introductory Programming Tasks? Journal of Computer Assisted Learning, 41, e13107. https://doi.org/10.1111/jcal.13107
DOI: 10.1111/jcal.13107
BibTeX: Download - Lohr, D., Wechsler, L., & Berges, M. (2025, June). Introducing a self-study course for learning textual programming at highschool with APFEL and ALeA. Paper presentation at Workshop KI und Bildung 2024, Bamberg, DE.
DOI: 10.20378/irb-108298
BibTeX: Download
2024
- Kruse-Kurbach, T., Lohr, D., Berges, M., Kohlhase, M., Moghbeli Damaneh, H., & Schütz, M. (2024). Term Extraction for Domain Modeling. In Gesellschaft für Informatik e.V. (Hrg.), Proceedings of the 22. Fachtagung Bildungstechnologien (DELFI). Fulda, DE.
DOI: 10.18420/delfi2024_33
BibTeX: Download - Lohr, D., Berges, M., Chugh, A., & Striewe, M. (2024). Adaptive Learning Systems in Programming Education: A Prototype for Enhanced Formative Feedback. In Gesellschaft für Informatik e.V. (Eds.), Proceedings of DELFI 2024. Fulda, DE.
DOI: 10.18420/delfi2024_57
BibTeX: Download - Lohr, D., Kiesler, N., Keuning, H., & Jeuring, J. (2024). „Let Them Try to Figure It Out First“ – Reasons Why Experts (Do Not) Provide Feedback to Novice Programmers. In ACM (Eds.), Proceedings of the 29th ACM Conference on on Innovation and Technology in Computer Science Education. Milan, IT.
DOI: 10.1145/3649217.3653530
BibTeX: Download
2023
- Berges, M., Betzendahl, J., Chugh, A., Kohlhase, M., Lohr, D., & Müller, D. (2023). Learning Support Systems based on Mathematical Knowledge Management. In Lecture Notes in Computer Science. Cambridge, GB: Cham: Springer.
BibTeX: Download - Kiesler, N., Lohr, D., & Keuning, H. (2023). Exploring the Potential of Large Language Models to Generate Formative Programming Feedback. In Proceedings of the 2023 IEEE ASEE Frontiers in Education Conference. Texas, US.
BibTeX: Download - Kruse, T., Berges, M., Betzendahl, J., Kohlhase, M., Lohr, D., & Müller, D. (2023). Learning with ALeA: Tailored experiences through annotated course material. In Maike Klein, Daniel Krupka, Cornelia Winter, Volker Wohlgemuth (Eds.), INFORMATIK 2023. Designing Futures: Zukünfte gestalten (pp. 395-398). Berlin, DE: Bonn: Köllen Druck+Verlag.
DOI: 10.18420/inf2023_43
BibTeX: Download - Lindner, A., Müller-Unterweger, M., Löffler, P., Lohr, D., & Berges, M. (2023). Von Autonomem Fahren bis Zahnarzt - Vorstellungen von Schüler:innen zu Künstlicher Intelligenz und ihre Integration in den Informatikunterricht. In Lutz Hellmig, Martin Hennecke (Hrg.), Informatikunterricht zwischen Aktualität und Zeitlosigkeit (S. 93-102). Würzburg, DE: Bonn: Gesellschaft für Informatik.
DOI: 10.18420/infos2023-007
BibTeX: Download - Lohr, D., Berges, M., Kohlhase, M., Müller, D., & Rapp, M. (2023). The Y-Model - Formalization of Computer-Science Tasks in the Context of Adaptive Learning Systems. In Proceedings of the IEEE German Education Conference (GeCon). Berlin, DE.
DOI: 10.1109/GECon58119.2023.10295148
BibTeX: Download - Lohr, D., Berges, M., Kohlhase, M., & Rabe, F. (2023). The Potential of Answer Classes in Large-scale Written Computer-Science Exams. In Desel, Jörg; Opel, Simone (Eds.), Proceedings of the Hochschuldidaktik Informatik (HDI) 2021 (pp. 179 - 190). Aachen, DE.
BibTeX: Download
2022
- Jeuring, J., Keuning, H., Marwan, S., Bouvier, D., Izu, C., Kiesler, N.,... Sarsa, S. (2022). Steps Learners Take when Solving Programming Tasks, and How Learning Environments (Should) Respond to Them. In Proceedings of the Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 2. Dublin, IE.
DOI: 10.1145/3502717.3532168
BibTeX: Download - Jeuring, J., Keuning, H., Marwan, S., Bouvier, D., Izu, C., Kiesler, N.,... Sarsa, S. (2022). Towards Giving Timely Formative Feedback and Hints to Novice Programmers. In Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE (pp. 95-115). Dublin, IE: Association for Computing Machinery.
DOI: 10.1145/3571785.3574124
BibTeX: Download
2021
- Lohr, D., & Berges, M. (2021). Towards Criteria for Valuable Automatic Feedback in Large Programming Classes. In Desel, Jörg; Opel, Simone (Eds.), Proceedings of the Hochschuldidaktik Informatik (HDI) 2021 (pp. 181-186). Dortmund, DE.
BibTeX: Download
Projects
Funding source: BMBF / Verbundprojekt
Project leader: , ,
Prof. Dr. Marc-Pascal Berges
Professors
Contact
- Email: marc.berges@fau.de
- Phone: +49 9131 85-27922
- URL: Profilseite aufrufen
The joint project VoLL-KI further develops higher education on three levels: study programs, individual study planning and learning progress within a course. AI-based systems provide support at all three levels. For this purpose, data from the Computer-based Decision Support System for Higher Education in Bavaria (CEUS) will also be used. At the same time, CEUS can be extended with data from the VoLL-KI project. The analysis combines individual and group-specific data. This should help to make university teaching non-discriminatory in the long term. Based on the data, individual recommendations for the course of study can be created. Students can then view reasons for the recommendations, look at alternatives, and also provide feedback on the suggestions. In a pseudonomous form, those responsible for the study program can also access this data. The project is evaluated by means of surveys and data analysis. Scientists from the fields of AI, computer science, computer science didactics and educational research from three neighboring universities are involved in the project. The initial focus is on study programs in computer science that differ at the three locations: a large, engineering-oriented computer science, a medium-sized, interdisciplinary-oriented computer science, and a small, application-oriented computer science. The results will then be transferred to other study programs at the participating universities.
Funding source: Bayerisches Staatsministerium für Wissenschaft und Kunst (StMWK) (seit 2018)
Project leader: , ,
Prof. Dr. Marc-Pascal Berges
Professors
Contact
- Email: marc.berges@fau.de
- Phone: +49 9131 85-27922
- URL: Profilseite aufrufen
Progressive digitalization is changing not only the job market but also the educational landscape. With funding from the DigitalPakt Schule and in detail from the BAYERN DIGITAL II program, serious changes in computer science education are being driven forward, which entail new challenges at the various levels of education. The CS4MINTS project addresses these challenges along with the educational levels and ties in with measures already launched as part of the MINTerAKTIV project, such as strengthening …