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PUBLICATIONS

2026

  1. Feedback-Strategien für Programmierlernsysteme: Von empirischen Analysen zur automatisierten BereitstellungLohrDissertation, Friedrich-Alexander-Universität Erlangen-Nürnberg▼ PDF
  2. 4.2 GenAI in Programming Education: Hypes, Hoaxes, and HopesHahnel, Benario, Keuning, Kiesler, Kohn, Komm, Lewis et al.Generative AI in Programming Education▼ PDF
  3. Using the Potential of GenAI Tools for AccessibilityKiesler, Alshaigy, Elglaly, Fronza, Gerdes, Huff et al.Proc. of the 31st ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE)▼ PDF

2025

  1. You're (Not) My Type – Can LLMs Generate Feedback of Specific Types for Introductory Programming Tasks?Lohr, Keuning, KieslerJournal of Computer Assisted Learning 41 (1), e13107▼ PDF
  2. Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning ObjectsLohr, Berges, Chugh et al.Journal of Computer Assisted Learning 41 (1), e13101▼ PDF
  3. 'Ignore These Errors for Now' – How Experts Provide Feedback on Steps Novices Take Towards Solving Programming ProblemsLohr, Kiesler, Keuning, JeuringProc. of the ACM Global Computing Education Conference 2025 (CompEd), Vol. 1▼ PDF
  4. Efficient Exam Correction at Scale – Streamlining Paper-Based Assessments with the VoLl-KOrN SystemBetzendahl, Lohr, Berges, KohlhaseDigitales Lehren und Lernen an der Hochschule: Strategien – Bedingungen – Umsetzung, transcript▼ PDF
  5. Der Weg ist das Ziel – Ein skalierbarer Wechsel von summativem zu formativem Assessment in der Programmierausbildung mit AntwortklassenLohr, BergesProc. 7. Workshop „Automatische Bewertung von Programmieraufgaben" (ABP 2025)▼ PDF
  6. The Potential of Answer Classes in Large-scale Written Computer-Science Exams – Vol. 2Lohr, Berges, Kohlhase, RabeCommentarii informaticae didacticae (HDI 2023)▼ PDF
  7. Introducing a Self-Study Course for Learning Textual Programming at Highschool with APFEL and ALeALohr, Wechsler, BergesWorkshop KI und Bildung 2024▼ PDF
  8. ALeA: Advancing Personalized Learning with Adaptive Assistance and Semantic AnnotationGrelka, Lohr, BergesWorkshop KI und Bildung 2024▼ PDF

2024

  1. 🏆 'Let Them Try to Figure It Out First' – Reasons Why Experts (Do Not) Provide Feedback to Novice ProgrammersLohr, Kiesler, Keuning, JeuringProc. of the 2024 Innovation and Technology in Computer Science Education (ITiCSE)▼ PDF
  2. 🏆 Adaptive Learning Systems in Programming Education: A Prototype for Enhanced Formative FeedbackLohr, Berges, Chugh, StrieweProc. of DELFI 2024▼ PDF
  3. Term Extraction for Domain ModelingKruse, Lohr, Berges, Kohlhase, Moghbeli, SchützProc. of DELFI 2024▼ PDF

2023

  1. Exploring the Potential of Large Language Models to Generate Formative Programming FeedbackKiesler, Lohr, Keuning2023 IEEE Frontiers in Education Conference (FIE)▼ PDF
  2. Learning Support Systems Based on Mathematical Knowledge ManagementBerges, Betzendahl, Chugh, Kohlhase, Lohr, MüllerIntelligent Computer Mathematics (CICM), Springer▼ PDF
  3. The Y-Model – Formalization of Computer Science Tasks in the Context of Adaptive Learning SystemsLohr, Berges, Kohlhase et al.2023 IEEE 2nd German Education Conference (GECon)▼ PDF
  4. Learning with ALeA: Tailored Experiences through Annotated Course MaterialKruse, Berges, Betzendahl, Kohlhase, Lohr, MüllerINFORMATIK 2023 – Designing Futures: Zukünfte gestalten▼ PDF
  5. The Potential of Answer Classes in Large-scale Written Computer-Science ExamsLohr, Berges, Kohlhase, RabeHochschuldidaktik Informatik 2023 (HDI23)▼ PDF
  6. Von Autonomem Fahren bis Zahnarzt – Vorstellungen von Schüler:innen zu Künstlicher Intelligenz und ihre Integration in den InformatikunterrichtLindner, Müller-Unterweger, Löffler, Lohr, BergesINFOS 2023 – Informatikunterricht zwischen Aktualität und Zeitlosigkeit▼ PDF

2022

  1. Towards Giving Timely Formative Feedback and Hints to Novice ProgrammersJeuring, Keuning, Marwan, Bouvier, Izu, Kiesler, Lehtinen, Lohr, Petersen, SarsaProc. of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education (ITiCSE-WGR)▼ PDF
  2. Steps Learners Take When Solving Programming Tasks, and How Learning Environments (Should) Respond to ThemJeuring, Keuning, Marwan, Bouvier, Izu, Kiesler, Lehtinen, Lohr, Petersen, SarsaProc. of the 27th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE), Vol. 2▼ PDF

2021

  1. Towards Criteria for Valuable Automatic Feedback in Large Programming ClassesLohr, BergesHochschuldidaktik Informatik 2021 (HDI21)▼ PDF