| Exploring the potential of large language models to generate formative programming feedback N Kiesler, D Lohr, H Keuning 2023 IEEE Frontiers in Education Conference (FIE), 1-5, 2023 | 63 | 2023 |
| Towards giving timely formative feedback and hints to novice programmers J Jeuring, H Keuning, S Marwan, D Bouvier, C Izu, N Kiesler, T Lehtinen, ... Proceedings of the 2022 Working Group Reports on Innovation and Technology …, 2022 | 44 | 2022 |
| Steps learners take when solving programming tasks, and how learning environments (should) respond to them J Jeuring, H Keuning, S Marwan, D Bouvier, C Izu, N Kiesler, T Lehtinen, ... Proceedings of the 27th ACM Conference on on Innovation and Technology in …, 2022 | 11 | 2022 |
| Learning support systems based on mathematical knowledge management M Berges, J Betzendahl, A Chugh, M Kohlhase, D Lohr, D Müller International Conference on Intelligent Computer Mathematics, 84-97, 2023 | 7 | 2023 |
| The Y-Model – Formalization of Computer Science Tasks in the Context of Adaptive Learning Systems D Lohr, M Berges, M Kohlhase, D Müller, M Rapp 2023 IEEE 2nd German Education Conference (GECon), 1-6, 2023 | 7 | 2023 |
| Towards Criteria for Valuable Automatic Feedback in Large Programming Classes D Lohr, M Berges Hochschuldidaktik Informatik 2021 (HDI21) 9, 2021 | 7 | 2021 |
| "Let Them Try to Figure It Out First" - Reasons Why Experts (Do Not) Provide Feedback to Novice Programmers D Lohr, N Kiesler, H Keuning, J Jeuring 🏆 Proceedings of the 2024 on Innovation and Technology in Computer Science …, 2024 | 4 | 2024 |
| Learning with ALeA: Tailored experiences through annotated course material T Kruse, M Berges, J Betzendahl, M Kohlhase, D Lohr, D Müller | 4 | 2023 |
| You’re (Not) My Type – Can LLMs Generate Feedback of Specific Types for Introductory Programming Tasks? D Lohr, H Keuning, N Kiesler Journal of Computer Assisted Learning, 2025 | 3 | 2025 |
| The Potential of Answer Classes in Large-scale Written Computer-Science Exams D Lohr, M Berges, M Kohlhase, F Rabe Hochschuldidaktik Informatik 2023 (HDI23) 10, 179 - 190, 2023 | 3 | 2023 |
| Adaptive Learning Systems in Programming Education: A Prototype for Enhanced Formative Feedback D Lohr, M Berges, A Chugh, M Striewe 🏆 Proceedings of DELFI 2024, 10.18420/delfi2024_57, 2024 | 2 | 2024 |
| Towards Criteria for Valuable Automatic Feedback in Large Programming Classes.(2021) D Lohr, M Berges | 2 | 2021 |
| The Y Model-Formalization of Computer-Science Tasks in the Context of Intelligent Tutoring Systems D Lohr, M Berges, M Kohlhase, D Müller, M Rapp | 1 | 2022 |
| Leveraging Large Language Models to Generate Course‐Specific Semantically Annotated Learning Objects D Lohr, M Berges, A Chugh, M Kohlhase, D Müller Journal of Computer Assisted Learning 41 (1), e13101, 2025 | | 2025 |
| The Potential of Answer Classes in Large-scale Written Computer-Science Exams--Vol. 2 D Lohr, M Berges, M Kohlhase, F Rabe arXiv preprint arXiv:2501.10368, 2024 | | 2024 |
| Term Extraction for Domain Modeling T Kruse, D Lohr, M Berges, M Kohlhase, H Moghbeli, M Schütz Proceedings of DELFI 2024, 10.18420/delfi2024_33, 2024 | | 2024 |
| Von Autonomem Fahren bis Zahnarzt-Vorstellungen von Schüler: innen zu Künstlicher Intelligenz und ihre Integration in den Informatikunterricht A Lindner, M Müller-Unterweger, P Löffler, D Lohr, M Berges Gesellschaft für Informatik eV, 2023 | | 2023 |
Natalie KieslerProfessor for Teaching and Learning in Higher Education, Computer Science, Nuremberg TechVerified email at th-nuernberg.de