• Dominic Lohr: Best Demo Award (DELFI (GI)) – 2024
• Dominic Lohr: Best Student Paper Award (ITiCSE 2024 (ACM)) – 2024

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.
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• 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
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• Lohr, D., & Berges, M. (2025). Der Weg ist das Ziel – Ein skalierbarer Wechsel von summativem zu formativem Assessment in der Programmierausbildung mit Antwortklassen. In Proceedings of the Workshop Automatische Bewertung von Programmieraufgaben 2025.
  DOI: 10.18420/abp2025_05
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• 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
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• 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
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• Lohr, D., Kiesler, N., Keuning, H., & Jeuring, J. (2025). 'Ignore These Errors for Now' - How Experts Provide Feedback on Steps Novices Take Towards Solving Programming Problems. In CompEd 2025: Proceedings of the ACM Global on Computing Education Conference 2025 (pp. 149-155). Gaborone, BW.
  DOI: 10.1145/3736181.3747150
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• 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
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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
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• 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
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• 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
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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.
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• 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.
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• 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
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• 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
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• 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
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• 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.
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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
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• 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
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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.
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