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Project Area A - Tasks and Strategies

Project Area A investigates the foundations and effects of the personalisation of simulations, which aims at coordinating task selection and learning support with participants’ learning prerequisites in order to optimise the cognitive and metacognitive strategies applied in the simulations.

a01

Prof. Dr. Martin Fischer & Prof. Dr. Ralf Schmidmaier

Promoting diagnostic and intervention skills in simulations through representational scaffolding: Effects of case typicality & case complexity

A01 focuses on the personalised simulation of medical patient cases, which are tailored to reflect varying degrees of typicality as a form of representational support. This adaptation is based on the learners’ learning prerequisites and their engagement in diagnostic and intervention activities within the simulated scenarios. Key questions include which learning prerequisites or process measures are effective adjustment bases for advancing diagnostic and intervention skills by learning with typical or atypical cases that are personalised on a meso-level.

Selected publications

  • 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
  • Fink, M. C., Reitmeier, V., Stadler, M., Siebeck, M., Fischer, F., & Fischer, M. R. (2021). Assessment of diagnostic competencies with standardized patients versus virtual patients: Experimental study in the context of history taking. Journal of Medical Internet Research, 23(3), Article e21196. https://doi.org/10.2196/21196
  • 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., … 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., Seidel, T., Hetmanek, A., Wecker, C., Fischer, M. R., Ufer, S., Schmidmaier, R., Neuhaus, B., Siebeck, M., Stürmer, K., Obersteiner, A., Reiss, K., Girwidz, R., Fischer, F., & Opitz, A. (2019). Facilitating diagnostic competences in simulations in high-er education: A framework and a research agenda for medical and teacher education. Frontline Learning Research, 7(4). https://doi.org/10.14786/flr.v7i4.384
  • 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 Sci-ences 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

a02

Prof. Dr. Stefan Ufer & Prof. Dr. Birgit Neuhaus

Personalised facilitation of skills for diagnosis and intervention during mathematical learning tasks: Case characteristics, cueing and knowledge activation

A02 focuses on the effects of case characteristics and cues on diagnostic and intervention skills for mathematics teaching, while additionally considering the moderating effects of pedagogical content knowledge as a learning prerequisite. Activating professional knowledge is investigated as a central learning process. Personalisation comprises a targeted variation of case characteristics, knowledge activation prompting and cueing of diagnostic information. The project compares the effects of personalised scaffolding based on learning prerequisites only with personalisation that takes into account learner activities in previous simulations.

Selected publications

  • 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
  • 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.,... Fisch-er, M. R. (2022). Representational scaffolding in digital simulations – learning professional practices in higher education. Infor-mation and Learning Sciences, 123(11/12), 645-665. https://doi.org/10.1108/ils-06-2022-0076
  • 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 compe-tence-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’ diag-nostic 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 real-time adap-tivity 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 sixth-grade 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

a03

Prof. Dr. Andreas Obersteiner, Prof. Dr. Stefan Küchemann & Prof. Dr. Matthias Stadler

Personalised support for task-based diagnosing and intervening in mathematics and physics based on eye movements and log data

A03 experimentally investigates the effects of pedagogical content knowledge as a prerequisite for learning to diagnose and intervene in mathematics and physics. A particular focus is on exploiting the diagnostic potential of tasks to generate evidence about pupils’ understanding and on selecting learning tasks for pupils. Eye-tracking and log data will be used to identify effective adjustment bases for personalised learning process scaffolding. Learning process scaffolding will be implemented by prompting learners to apply pedagogical content knowledge during the simulation.

Selected publications

  • 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
  • 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
  • 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
  • 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., 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. 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
  • 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

a04

Prof. Dr. Maria Bannert, Prof. Dr. Tilman Michaeli, Prof. Dr. Jürgen Pfeffer & Prof. Dr. Tina Seidel

Promoting diagnostic and intervention skills with simulations for programming & debugging: Effects of personalised representational and learning process scaffolding

A04 investigates pre-service teachers’ learning of diagnosing and intervention skills for programming and debugging. Beginning computer science teachers’ self-regulation processes during simulation-based learning will be investigated as a function of representational scaffolding, which adjusts the salience of cues and additional learning process scaffolding implemented through dashboards with metacognitive prompts. By investigating the personalisation of these scaffolds, the project con-tributes to our understanding of the design features that are conducive to the self-regulated processing of simulations.

Selected publications

  • 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 Pro-gramming 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. In 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

a05

Prof. Dr. Sarah Hofer & Prof. Dr. Stefan Küchemann

Personalised formative assessment training in simulated experimental physics instruction: Individual differences in the interplay between scaffolding and self-regulation

A05 focuses on diagnostic and intervention skills for teaching and learning with experiments in physics education. The project investigates the interplay of individual learner prerequisites with cognitive and metacognitive learning process scaffolding, as well as representational scaffolding, by reducing informational complexity and situation dynamics. The project focuses on metacognitive learning processes in terms of self-directed learning as an adjustment base for personalisation. Drawing on a virtual reality simulation, this project conceptualises and studies personalised representational and learning process scaffolding.

Selected publications

  • 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 situa-tions. 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 line-graphs: 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
  • Reinhold, F., Hofer, S. I., Hoch, S., Werner, B., Richter-Gebert, J., & Reiss, K. (2020). Digital support principles for sustained math-ematics learning in disadvantaged students. Plos one, 15(10), e0240609. https://doi.org/10.1371/journal.pone.0240609
  • Steinert, S., Ruf, V., Dzsotjan, D., Großmann, N., Schmidt, A., Kuhn, J., & Küchemann, S. (2024). A refined approach for evaluating small datasets via binary classification using machine learning. Plos one, 19(5), e0301276. https://doi.org/10.1371/journal.pone.0301276

 

a06

Prof. Dr. Matthias Stadler & Prof. Dr. Andreas Obersteiner

The interplay of cognitive prerequisites and knowledge in simulated learning environments

A06 investigates the interplay between learners’ general cognitive prerequisites and domain-specific content knowledge during diagnosing mathematical understanding, as well as during solving MicroDYN tasks, as examples of a knowledge-lean domain. The project investigates approaches to automatically infer participants’ knowledge from their diagnostic activities. The results will be used to provide personalised learning process scaffolding or representational scaffolding, which is personalised on the macro- or meso-level based on participants’ knowledge. A methodological contribution of the project will be to automatically identify the use of general problem-solving strategies based on the temporal and sequential structures of diagnostic activities

Selected publications

  • 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.
  • 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 für 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
  • Stadler, M., Radkowitsch, A., Schmidmaier, R., Fischer, M. R., & Fischer, F. (2020). Take your time: Invariance of timeon-task in problem solving tasks across expertise levels. Psychological Test and Assessment Modeling, 65(4), 415–525.
  • 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.