Archival Literature

Articles in Journals

  1. [3] M. Kohlhase, M. Berges, J. Grubert, A. Henrich, D. Landes, J. L. Leidner, F. Mittag, D. Nicklas, U. Schmid, Y. Sedlmaier, A. U. Ende, and D. Wolter (2024) Project VoLL-KI – learning from learners. Künstliche Intellienz. External Links: Document Cited by: p1.
  2. [6] D. Müller (2023) An html/css schema for primitives – generating high-quality responsive html from generic . TeX users group conference (tug), pp. 275–286. External Links: Link Cited by: p1.
  3. [5] D. Müller and M. Kohlhase (2022) sTeX3 – a -based ecosystem for semantic/active mathematical documents. TeX users group conference (tug), pp. 197–201. External Links: Link Cited by: p1.
  4. [1] E. Bender, P. Hubwieser, N. Schaper, M. Margaritis, M. Berges, L. Ohrndorf, J. Magenheim, and S. Schubert (2015) Towards a competency model for teaching computer science. Peabody Journal of Education 90 (4), pp. 519–532. External Links: ISSN 0161-956X, Document Cited by: p1.
  5. [2] A. Kohlhase and M. Kohlhase (2008-06) Semantic knowledge management for education. Proceedings of the IEEE; Special Issue on Educational Technology 96 (6), pp. 970–989. External Links: Link Cited by: p1.
  6. [4] M. Kohlhase (2008) Using as a semantic markup format. Mathematics in Computer Science 2 (2), pp. 279–304. External Links: Link Cited by: p1.

Papers at International, Peer-Reviewed Conferences

  1. [1] M. Berges, J. Betzendahl, A. Chugh, M. Kohlhase, D. Lohr, and D. Müller (2023) Learning support systems based on mathematical knowledge managment. In Intelligent computer mathematicsIntelligent Computer Mathematics (CICM) 2023, C. Dubois and M. Kerber (Eds.), LNAI. External Links: Link Cited by: p1.
  2. [9] T. Kruse, M. Berges, J. Betzendahl, M. Kohlhase, D. Lohr, and D. Müller (2023) Learning with alea: tailored experiences through annotated course material. In KI-bildung, Lecture Notes in Informatics. External Links: Link Cited by: p1.
  3. [10] D. Lohr, M. Berges, M. Kohlhase, D. Müller, and M. Rapp (2023) The Y-Model – formalization of computer-science tasks in the context of adaptive learning systems. In 2023 IEEE German Education Conference (GeCon), External Links: Link Cited by: p1.
  4. [11] D. Lohr, M. Berges, M. Kohlhase, and F. Rabe (2023) The potential of answer classes in large-scale written computer-science exams. In Proceedings of the 10th Symposium on Computer Science in Higher Education HDI23, Aachen, Germany, pp. . Note: Accepted External Links: Link Cited by: p1.
  5. [8] M. Kohlhase and D. Müller (2022) System description: sTeX3 – a -based ecosystem for semantic/active mathematical documents. In Intelligent computer mathematicsIntelligent Computer Mathematics (CICM) 2022, K. Buzzard and T. Kutsia (Eds.), LNAI, Vol. 13467, pp. 184–188. External Links: Link Cited by: p1.
  6. [13] D. Müller and M. Kohlhase (2022) Injecting formal mathematics into latex. In Intelligent computer mathematicsIntelligent Computer Mathematics (CICM) 2022, K. Buzzard and T. Kutsia (Eds.), LNAI, Vol. 13467, pp. 168–183. External Links: Link Cited by: p1.
  7. [7] P. Hubwieser, M. Berges, M. Striewe, and M. Goedicke (2017) Towards competency based testing and feedback: competency definition and measurement in the field of algorithms & data structures. In Proceedings of 2017 IEEE Global Engineering Education Conference (EDUCON), IEEE Conference Publications, pp. 517–526. External Links: ISBN 978-1-5090-5467-1, Document Cited by: p1.
  8. [16] P. Shah, M. Berges, and P. Hubwieser (2017) Qualitative content analysis of programming errors. In Proceedings of the 5th International Conference on Information and Education Technology, International Conference Proceedings Series, New York. External Links: ISBN 978-1-4503-4791-4, Document Cited by: p1.
  9. [4] M. Berges, M. Striewe, P. Shah, M. Goedicke, and P. Hubwieser (2016) Towards deriving programming competencies from student errors. In 4th International Conference on Learning and Teaching in Computing and Engineering (LaTiCE), Los Alamitos, pp. 19–23. Cited by: p1.
  10. [3] M. Berges and P. Hubwieser (2015) Evaluation of source code with item response theory. In Proceedings of the 20th SIGCSE Conference on Innovation and Technology in Computer Science Education, New York, pp. 51–56. Cited by: p1.
  11. [5] M. Berges (2015) Investigating novice programming abilities with the help of psychometric assessment. In Proceedings of Society for Information Technology & Teacher Education International Conference 2015, D. Slykhuis and G. Marks (Eds.), Las Vegas, NV, United States, pp. 90–95. Cited by: p1.
  12. [12] M. Margaritis, J. Magenheim, P. Hubwieser, M. Berges, L. Ohrndorf, and S. Schubert (2015) Development of a competency model for computer science teachers at secondary school level. In IEEE Global Engineering Education Conference, Los Alamitos, pp. 211–220. External Links: Document Cited by: p1.
  13. [15] A. Ruf, M. Berges, and P. Hubwieser (2015) Classification of programming tasks according to required skills and knowledge representation. In Informatics in Schools. Curricula, Competences, and Competitions, A. Brodnik and J. Vahrenhold (Eds.), Lecture Notes in Computer Science, Heidelberg, pp. 57–68. External Links: ISBN 978-3-319-25395-4 Cited by: p1.
  14. [2] M. Berges and P. Hubwieser (2013) Concept specification maps: displaying content structures. In Proceedings of the 18th ACM conference on Innovation and technology in computer science education, New York, USA, pp. 291–296. External Links: ISBN 978-1-4503-2078-8, Document Cited by: p1.
  15. [6] P. Hubwieser, M. Berges, J. Magenheim, N. Schaper, K. Bröker, M. Margaritis, S. Schubert, and L. Ohrndorf (2013) Pedagogical content knowledge for computer science in german teacher education curricula. In Proceedings of the 8th Workshop in Primary and Secondary Computing Education, New York, pp. 95–103. External Links: Link, ISBN 978-1-4503-2455-7, Document Cited by: p1.
  16. [14] A. Ruf, M. Berges, and P. Hubwieser (2013) Types of assignments for novice programmers. In Proceedings of the 8th Workshop in Primary and Secondary Computing Education, New York, pp. 43–44. External Links: ISBN 978-1-4503-2455-7, Document Cited by: p1.

Theses

  1. [1] P. Hutterer (2023-04-12) Integrating automated semantic annotation using machine learning into authoring workflows for flexiformal documents. M.Sc. Thesis, FAU Erlangen-Nürnberg. External Links: Link Cited by: p1.
  2. [2] M. Plivelic (2018-02) Using machine learning to support annotating of keywords in mathematical texts. B.Sc. Thesis, FAU Erlangen-Nürnberg. External Links: Link Cited by: p1.

Bachelors/Masters Project Reports

  1. [1] Cited by: p1.

Gray Literature

Papers at Peer-Reviewed Workshops

  1. [1] A. Chugh, M. Kohlhase, and D. Müller (2023) Presentation of active documents in ALeA. In MathUI 2023: the 14th workshop on mathematical user interfacesMathUI 2023: The 14th Workshop on Mathematical User Interfaces, A. Kohlhase (Ed.), Note: submitted External Links: Link Cited by: p1.
  2. [2] A. Kohlhase and M. Kohlhase (2023) More interactions in ALeA – towards new added-value services based on semantic markup. In MathUI 2023: the 14th workshop on mathematical user interfacesMathUI 2023: The 14th Workshop on Mathematical User Interfaces, A. Kohlhase (Ed.), External Links: Link Cited by: p1.

Technical Reports

  1. [1] M. Kohlhase and D. Müller The sTeX3 manual. Technical report External Links: Link Cited by: p1.

Unpublished

  1. [1] M. Kohlhase and M. Schütz (2024) Reusing learning objects via theory morphisms. Note: submitted External Links: Link Cited by: p1.