Munich Center of the Learning Sciences
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Project Area A

Research goal

Identify conditions of effective personalization of learning with simulations when tasks and strategies are of particular importance.

Selected publications

  • Fischer, F., Bauer, E., Seidel, T., Schmidmaier, R., Radkowitsch, A., Neuhaus, B. J., Hofer, S. I., Sommerhoff, D., Ufer, S., Kuhn, J., Küchemann, S., Sailer, M., Koenen, J., Gartmeier, M., Berberat, P., Frenzel, A., Heitzmann, N., Holzberger, D., Pfeffer, J., Lewalter, D., Niklas, F., Schmidt-Hertha, B., Gollwitzer, M., Vorholzer, A., Chernikova, O., Schons, C., Pickal, A. J., Bannert, M., Michaeli, T., Stadler, M., & Fischer, M. R. (2022). Representational scaffolding in digital simulations – learning professional practices in higher education. Information and Learning Sciences, 123(11/12), 645-665. https://doi.org/10.1108/ILS-06-2022-0076
  • Heitzmann, N., Stadler, M., Richters, C., Radkowitsch, A, Schmidmaier, R., Weidenbusch, M., & Fischer, M. R. (2022). Learners’ adjustment strategies following impasses in simulations – effects of prior knowledge. Learning and Instruction, Article 101632. https://doi.org/10.1016/j.learninstruc.2022.101632
  • Brandl, L., Richters, C., Radkowitsch, A., Obersteiner, A., Fischer, M.-R., Schmidmaier, R., Fischer, F., & Stadler, M. (2021). Simulation-Based Learning of Complex Skills: Predicting Performance With Theoretically Derived Process Features. Psychological Test and Assessment Modeling, 63(4), 542-560. https://www.psychologie-aktuell.com/fileadmin/Redaktion/Journale/ptam-2021-4/PTAM__4-2021_6_kor.pdf
  • Fink, M. C., Reitmeier, V., Stadler, M., Siebeck, M., Fischer, F., & Fischer, M. R. (2021). Assessment of diagnostic competences with standardized patients versus virtual patients: Experimental study in the context of history taking. Journal of Medical Internet Research. https://doi.org/10.2196/21196
  • Heitzmann, N., Opitz, A., Stadler, M., Sommerhoff, D., Fink, M. C., Obersteiner, A., Schmidmaier, R., Neuhaus, B. J., Ufer, S., Seidel, T., Fischer, M. R., & Fischer, F. (2021). Cross-Disciplinary Research on Learning and Instruction – Coming to Terms. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.562658
  • Fink, M.C., Radkowitsch, A., Bauer, E., Sailer, M., Kiesewetter, J., Schmidmaier, R., Siebeck, M., Fischer, F. & Fischer, M. (2020). Simulation research and design: a dual-level framework for multi-project research programs. Educational Technology Research and Development, 68(6). https://doi.org/10.1007/s11423-020-09876-0
  • Radkowitsch, A., Fischer, M. R., Schmidmaier, R., & Fischer, F. (2020). Learning to diagnose collaboratively: validating a simulation for medical students. GMS Journal for Medical Education, 37(5). doi: 10.3205/zma001344
    Stadler, M., Radkowitsch, A., Schmidmaier, R., Fischer, M., & Fischer, F. (2020). Take your time: Invariance of time on-task in problem solving tasks across expertise levels. Psychological Test and Assessment Modeling, 62(4), 517-525. 
  • Heitzmann, N., Seidel, T., Opitz. A., Hetmanek, A., Wecker, C., Fischer, M., Ufer, S., Schmidmaier, R., Neuhaus, B., Siebeck, M., Stürmer, K., Obersteiner, A., Reiss, K., Girwidz, R., & Fischer, F. (2019). Facilitating Diagnostic Competences in Simulations: A Conceptual Framework and a Research Agenda for Medical and Teacher Education. Frontline Learning Research, 7(4), 1-24. https://doi.org/10.14786/flr.v7i4.384
  • Förtsch, C., Sommerhoff, D., Fischer, F., Fischer, M. R., Girwidz, R., Obersteiner, A., Reiss, K., Stürmer, K., Siebeck, M., Schmidmaier, R., Seidel, T., Ufer, S., Wecker, C., & Neuhaus, B. J. (2018). Systematizing Professional Knowledge of Medical Doctors and Teachers: Development of an Interdisciplinary Framework in the Context of Diagnostic Competences. Education Sciences, 8(4), 207. https://doi.org/10.3390/educsci8040207
  • Kramer M., Stürmer, J., Förtsch, C., Seidel, T., Ufer, S., Fischer, M.R., & Neuhaus, B.J. (2022). Diagnosing the Instructional Quality of Biology Lessons Based on Staged Videos: Developing DiKoBi, A Video-Based Simulation. In F. Fischer, & A. Opitz (Eds.). Learning to Diagnose with Simulations - Examples from Teacher Education and Medical Education. Springer. https://doi.org/10.1007/978-3-030-89147-3_6
  • Richters, C., Stadler, M., Radkowitsch, A., Behrmann, F., Weidenbusch, M., Fischer, M.R., Schmidmaier, R., & Fischer, F. (2022). Making the rich even richer? Interaction of structured reflection with prior knowledge in collaborative medical simulations. In A. Weinberger, W. Chen, D. Hernández-Leo, & B.Che (Eds.), Proceedings of the 15th International Conference on Computer-Supported Collaborative Learning - CSCL 2022 (pp. 155-162). International Society of the Learning Sciences. https://2022.isls.org/proceedings/ --> Best Paper Award
  • Bauer, E., Fischer, F., Kiesewetter, J., Shaffer, D. W., Fischer, M. R., Zottmann, J. M., & Sailer, M. (2020). Diagnostic activities and diagnostic practices in medical education and teacher education: an interdisciplinary comparison. Frontiers in Psychology, 11, Article 562665. https://doi.org/10.3389/fpsyg.2020.562665
  • Braun, L. T., Borrmann, K. F., Lottspeich, C., Heinrich, D. A., Kiesewetter, J., Fischer. M. R., & Schmidmaier, R. (2019). Scaffolding clinical reasoning of medical students with virtual patients: Effects on diagnostic accuracy, efficiency, and errors. Diagnosis, 6(2), 137-149. https://doi.org/10.1515/dx-2018-0090
  • Braun, L. T., Lenzer, B., Fischer, M. R., & Schmidmaier R. (2019). Complexity of clinical cases in simulated learning environments:proposal for a scoring system. GMS Journal for Medical Education, 36(6), Article Doc80. https://doi.org/10.3205/zma001288
  • Fink, M. C., Heitzmann, N., Reitmeier, V., Siebeck, M., Fischer, F., & Fischer, M. R. (2023). Diagnosing virtual patients: The interplay between knowledge and diagnostic activities. Advances in Health Sciences Education, 28(4), 1245-1264. https://doi.org/10.1007/s10459-023-10211-4
  • Klein, M., Otto, B., Fischer, M. R., & Stark, R. (2019). Fostering medical students’ clinical reasoning by learning from errors in clinical case vignettes: Effects and conditions of additional prompting procedures to foster self-explanations. Advances in Health Sciences Education, 24(2), 331–351. https://doi.org/10.1007/s10459-018-09870-5
  • Richters, C., Schmidmaier, R., Popov, V., Schredelseker, J., Fischer, F., & Fischer, M. R. (2024). Intervention skills – A neglected field of research in medical education and beyond. GMS Journal of Medical Education, 41(4), Doc48. https://doi.org/10.3205/zma001703
  • Richters, C., Stadler, M., Radkowitsch, A., Behrmann, F., Weidenbusch, M., Fischer, M. R., Schmidmaier, R., & Fischer, F. (2024). Fostering collaboration in simulations: How advanced learners benefit from collaboration scripts and reflection. Learning and Instruction, 93, 101912. https://doi.org/10.1016/j.learninstruc.2024.101912
  • Bauer, E., Sailer, M., Kiesewetter, J., Fischer, M. R., & Fischer, F. (2022). Diagnostic argumentation in teacher education: Making the case for justification, disconfirmation, and transparency. Frontiers in Education, 7, 977631. https://doi.org/10.3389/feduc.2022.977631
  • Hammer, S., & Ufer, S. (2023). Professional competence of mathematics teachers in dealing with tasks in lesson planning. Teaching and Teacher Education, 132, 104246. https://doi.org/10.1016/j.tate.2023.104246
  • Herppich, S., Praetorius, A.-K., Förster, N., Glogger-Frey, I., Karst, K., Leutner, D., Behrmann, L., Böhmer, M., Ufer, S., & Klug, J. (2018). Teachers' assessment competence: Integrating knowledge-, process-, and product-oriented approaches into a competence-oriented conceptual model. Teaching and Teacher Education, 76, 181-193. https://doi.org/10.1016/j.tate.2017.12.001
  • Irmer, M., Traub, D., Böhm, M., Förtsch, C., & Neuhaus, B. J. (2023). Using Video-Based Simulations to Foster pPCK/ePCK— New Thoughts on the Refined Consensus Model of PCK. Education Sciences, 13(3), 261. https://doi.org/10.3390/educsci13030261 
  • Kramer, M., Förtsch, C., Seidel, T., & Neuhaus, B. J. (2021). Comparing two constructs for describing and analyzing teachers’ diagnostic processes. Studies in Educational Evaluation, 68, 100973. https://doi.org/10.1016/j.stueduc.2020.100973
  • Kron, S., Sommerhoff, D., Achtner, M., Stürmer, K., Wecker, C., Siebeck, M., & Ufer, S. (2022a). Cognitive and motivational person characteristics as predictors of diagnostic performance: Combined effects on pre-service teachers’ diagnostic task selection and accuracy. Journal für Mathematik-Didaktik, 43(1), 135-172. https://doi.org/10.1007/s13138-022-00200-2
  • Nickl, M., Sommerhoff, D., Radkowitsch, A., Huber, S. A., Bauer, E., Ufer, S., Plass, J. L., & Seidel, T. (2024). Effects of realtime adaptivity of scaffolding: Supporting pre-service mathematics teachers’ assessment skills in simulations. Learning and Instruction, 94, 101994. https://doi.org/10.1016/j.learninstruc.2024.101994
  • Schadl, C., & Ufer, S. (2023). Mathematical knowledge and skills as longitudinal predictors of fraction learning among sixthgrade students. Journal of Educational Psychology, 115(7), 985-1003. https://doi.org/10.1037/edu0000808
  • Sommerhoff, D., Codreanu, E., Nickl, M., Ufer, S., & Seidel, T. (2023). Pre-service teachers’ learning of diagnostic skills in a video-based simulation: Effects of conceptual vs. interconnecting prompts on judgment accuracy and the diagnostic process. Learning and Instruction, 83, 101689. https://doi.org/10.1016/j.learninstruc.2022.101689
  • Brückner, S., Zlatkin-Troitschanskaia, O., Küchemann, S., Klein, P., & Kuhn, J. (2020). Changes in Students’ Understanding of and Visual Attention on Digitally Represented Graphs Across Two Domains in Higher Education: A Postreplication Study. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.02090
  • Klein, P., Küchemann, S., Brückner, S., Zlatkin-Troitschanskaia, O., & Kuhn, J. (2019). Student understanding of graph slope and area under a curve: A replication study comparing first-year physics and economics students. Physical Review Physics Education Research, 15(2), 020116. https://doi.org/10.1103/PhysRevPhysEducRes.15.020116
  • Küchemann, S., Klein, P., Becker, S., Kumari, N., & Kuhn, J. (2020). Classification of students’ conceptual understanding in STEM education using their visual attention distributions: a comparison of three machine-learning approaches. In Proceedings of the 12th International Conference on Computer Supported Education (Vol. 1, pp. 36–46). SciTePress. https://doi.org/10.5220/0009359400360046
  • Schons, C., Obersteiner, A., Fischer, F., & Reiss, K. (2024). Toward adaptive support of pre-service teachers' assessment competencies: Log data in a digital simulation reveal engagement modes. Learning and Instruction, 94, 101979. https://doi.org/10.1016/j.learninstruc.2024.101979
  • Schons, C., Obersteiner, A., Reinhold, F., Fischer, F., & Reiss, K. (2023). Developing a simulation to foster prospective mathematics teachers’ diagnostic competencies: the effects of scaffolding. Journal für Mathematik-Didaktik, 44(1), 59-82. https://doi.org/10.1007/s13138-022-00210-0
  • Stadler, M., Hofer, S., & Greiff, S. (2020). First among equals: Log data indicates ability differences despite equal scores. Computers in Human Behavior, 111, 106442. https://doi.org/10.1016/j.chb.2020.106442
  • Strohmaier, A. R., MacKay, K. J., Obersteiner, A., & Reiss, K. M. (2020). Eye-tracking methodology in mathematics education research: A systematic literature review. Educational Studies in Mathematics, 104(2), 147-200. https://doi.org/10.1007/s10649-020-09948-1
  • Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and Learning, 9(2), 161-185. https://doi.org/10.1007/s11409-013-9107-6
  • Jang, W., Gao, H., Michaeli, T., & Kasneci, E. (2024). Exploring Communication Dynamics: Eye-tracking Analysis in Pair Programming of Computer Science Education. In Proceedings of the 2024 Symposium on Eye Tracking Research and Applications (pp. 1-7). ACM. https://doi.org/10.1145/3649902.3653942
  • Järvelä, S., & Bannert, M. (2021). Temporal and adaptive process of regulated learning – what can multimodal data tell? Learning and Instruction, 72. https://doi.org/10.1016/j.learninstruc.2019.101268
  • Kosel, C., Voggenreiter, A., Pfeffer, J., & Seidel, T. (2023). Measuring teachers’ visual expertise using the gaze relational index based on real-world eyetracking data and varying velocity thresholds. Journal of Expertise, 6(3), 267-281. https://doi.org/10.48550/arXiv.2304.05143
  • Hartl, A., Starke, E., Voggenreiter, A., Holzberger, D., Michaeli, T., & Pfeffer, J. (2024). Empowering Digital Natives: InstaClone - A Novel Approach to Data Literacy Education in the Age of Social Media. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education, (pp 484–490). ACM. https://doi.org/10.1145/3626252.3630839
  • Lim, L., Bannert, M., van der Graaf, J., Singh, S., Fan, Y., Surendrannair, S., Rakovic, M., Molenaar, I., Moore, J., & Gašević, D. (2022). Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning. Computers in Human Behavior, 139, Article 107547. https://doi.org/10.1016/j.chb.2022.107547
  • Michaeli, T., & Romeike, R. (2019b). Improving debugging skills in the classroom – the effects of teaching a systematic debugging process. Proceedings of the 14th Workshop on Primary and Secondary Computing Education (pp. 1-7). ACM. https://doi.org/10.1145/3361721.3361724
  • Raković, M., Fan, Y., van der Graaf, J., Singh, S., Kilgour, J., Lim, L., Moore, J., Bannert, M., Molenaar, I., & Gašević, D. (2022). Using learner trace data to understand metacognitive processes in writing from multiple sources. In A. Wise et al. (Eds.), Companion Proceedings of the 12th International Conference on Learning Analytics & Knowledge (pp. 130-141). https://doi.org/10.1145/3506860.3506876
  • Seidel, T., Schnitzler, K., Kosel, C., Stürmer, K., & Holzberger, D. (2021). Student characteristics in the eyes of teachers: Differences between novice and expert teachers in judgment accuracy, observed behavioral cues, and gaze. Educational Psychology Review, 33(1), 69-89. https://doi.org/10.1007/s10648-020-09532-2
  • Wachter, H. & Michaeli, T. (2024). Analyzing teachers' diagnostic and intervention processes in debugging using video vignettes. In Proceedings of the 17th International Conference on Informatics in Schools (pp. 1-13). Springer. https://doi.org/10.1007/978-3-031-73474-8_13
  • Edelsbrunner, P. A., Malone, S., Hofer, S. I., Küchemann, S., Kuhn, J., Schmid, R., ... & Lichtenberger, A. (2023). The relation of representational competence and conceptual knowledge in female and male undergraduates. International Journal of STEM Education, 10(1), 44. https://doi.org/10.1186/s40594-023-00435-6
  • Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., & Reiss, K. M. (2020). The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific metaanalysis. Computers & Education, 153, 103897. https://doi.org/10.1016/j.compedu.2020.103897
  • Hofer, S. I., Schumacher, R. & Rubin, H. J. (2017). The test of basic Mechanics Conceptual Understanding (bMCU): using Rasch analysis to develop and evaluate an efficient multiple choice test on Newton’s mechanics. International Journal Of STEM Education, 4(1). https://doi.org/10.1186/s40594-017-0080-5
  • Hofer, S. I., Schumacher, R., Rubin, H., & Stern, E. (2018). Enhancing physics learning with cognitively activating instruction: A quasi-experimental classroom intervention study. Journal of Educational Psychology, 110(8), 1175-1191. https://doi.org/10.1037/edu0000266
  • Hofer, S. I., Nistor, N., & Scheibenzuber, C. (2021). Online teaching and learning in higher education: Lessons learned in crisis situations. Computers in Human Behavior, 121, 106789. https://doi.org/10.1016/j.chb.2021.106789
  • Küchemann, S., Klein, P., Fouckhardt, H., Gröber, S., & Kuhn, J. (2020). Students’ understanding of non-inertial frames of reference. Physical Review Physics Education Research, 16(1), 010112. https://doi.org/10.1103/PhysRevPhysEducRes.16.010112
  • Küchemann, S., Becker, S., Klein, P., & Kuhn, J. (2021a). Gaze-based prediction of students' understanding of physics linegraphs: An eye-tracking-data based machine-learning approach. In H. C. Lane, S. Zvacek & J. Uhomoibhi J. (Eds.), CSEDU 2020. Communications in Computer Information Sciences (pp. 450-467). Springer. https://doi.org/10.1007/978-3-030-86439-2_23
  • Küchemann, S., Malone, S., Edelsbrunner, P., Lichtenberger, A., Stern, E., Schumacher, R., ... & Kuhn, J. (2021b). Inventory for the assessment of representational competence of vector fields. Physical Review Physics Education Research, 17(2), 020126. https://doi.org/10.1103/PhysRevPhysEducRes.17.020126
  • Brunner, K., Obersteiner, A., & Leuders, T. (2024). How pedagogical content knowledge sharpens prospective teachers’ focus when judging mathematical tasks: an eye-tracking study. Educational Studies in Mathematics, 115(2), 177–196. https://doi.org/10.1007/s10649-023-10281-6 
  • Krieger, F., Stadler, M., Bühner, M., Fischer, F., & Greiff, S. (2021). Assessing Complex Problem-Solving Skills in Under 20 Minutes. Psychological Test Adaptation and Development, 2(1), 80–92. https://doi.org/10.1027/2698-1866/a000009
  • Obersteiner, A., Reiss, K., & Ufer, S. (2013). How training on exact or approximate mental representations of number can enhance first-grade students’ basic number processing and arithmetic skills. Learning and Instruction, 23, 125–135. https://doi.org/10.1016/j.learninstruc.2012.08.004
  • Schons, C., Obersteiner, A., Reinhold, F., Fischer, F., & Reiss, K. (2023). Developing a Simulation to Foster Prospective Mathematics Teachers' Diagnostic Competencies: The Effects of Scaffolding. Journal Fur Mathematik-Didaktik, 44(1), 59–82. https://doi.org/10.1007/s13138-022-00210-0
  • Stadler, M., Fischer, F., & Greiff, S. (2019). Taking a Closer Look: An Exploratory Analysis of Successful and Unsuccessful Strategy Use in Complex Problems. Frontiers in Psychology, 10, 777. https://doi.org/10.3389/fpsyg.2019.00777
  • Stadler, M., Hofer, S., & Greiff, S. (2020). First among equals: Log data indicates ability differences despite equal scores. Computers in Human Behavior, 111, 106442. https://doi.org/10.1016/j.chb.2020.106442
  • Stadler, M., Pickal, A. J., Brandl, L., & Krieger, F. (2024). VOTAT in Action. Zeitschrift Für Psychologie, 232(2), 109–119. https://doi.org/10.1027/2151-2604/a000559
  • Wildgans-Lang, A., Scheuerer, S., Obersteiner, A., Fischer, F., & Reiss, K. (2022). Learning to diagnose primary students’ mathematical competence levels and misconceptions in document-based simulations. In F. Fischer & A. Opitz (Eds.), Learning to Diagnose with Simulations. Springer International Publishing.