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Project Area B - Social Interaction, Collaboration and Feedback

Project Area B investigates the conditions of the effective personalisation of simulations related to social interaction, feedback and collaboration.

b01

Prof. Dr. Frank Fischer & Prof. Dr. Martin Fischer

Learning to give peer feedback: Effects of scaffolding in simulations on diagnostic and intervention-related skills, and peer feedback skills

B01 explores the conditions for providing effective peer feedback with simulations in medical education. Effects will be considered on (meta-)cognitive and social learning processes as well as on diagnostic and intervention skills and professional peer feedback skills. The project applies the per-sonalisation of peer feedback by matching peer performances as representational scaffolding. This project will investigate the effects of adjusting the salience of relevant information to learning prerequisites and prior activities in the simulation.

Selected publications

  • Bauer, E., Greisel, M., Kuznetsov, I., Berndt, M., Kollar, I., Dresel, M., Fischer, M. R., & Fischer, F. (2023). Using natural language processing to support peer‐feedback in the age of artificial intelligence: A cross‐disciplinary framework and a research agenda. British Journal of Educational Technology, 54(5), 1222–1245. https://doi.org/10.1111/bjet.13336
  • Berndt, M., Strijbos, J.‑W., & Fischer, F. (2018). Effects of written peer-feedback content and sender’s competence on perceptions, performance, and mindful cognitive processing. European Journal of Psychology of Education, 33(1), 31–49. https://doi.org/10.1007/s10212-017-0343-z
  • Brandl, L., Stadler, M., Richters, C., Radkowitsch, A., Fischer, M. R., Schmidmaier, R., & Fischer, F. (2024). Collaborative problem-solving in knowledge-rich domains: A multi-study structural equation model. International Journal of Computer-Supported Collaborative Learning, 19(3), 341-368. https://doi.org/10.1007/s11412-024-09425-4
  • Chernikova, O., Holzberger, D., Heitzmann, N., Stadler, M., Seidel, T., & Fischer, F. (2024). Where salience goes beyond authenticity. Zeitschrift für Pädagogische Psychologie, 38(1-2), 15–25. https://doi.org/10.1024/1010-0652/a000357
  • Kollar, I., & Fischer, F. (2010). Peer assessment as collaborative learning: A cognitive perspective. Learning and Instruction, 20(4), 344–348. https://doi.org/10.1016/j.learninstruc.2009.08.005
  • Machts, N., Chernikova, O., Jansen, T., Weidenbusch, M., Fischer, F., & Möller, J. (2024). Categorization of simulated diagnostic situations and the salience of diagnostic information. Zeitschrift Für Pädagogische Psychologie, 38(1-2), 3–13. https://doi.org/10.1024/1010-0652/a000364
  • 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). https://doi.org/10.3205/zma001344
  • Radkowitsch, A., Sailer, M., Schmidmaier, R., Fischer, M. R., & Fischer, F. (2021). Learning to diagnose collaboratively – Effects of adaptive collaboration scripts in agent-based medical simulations. Learning and Instruction, 75, 101487. https://doi.org/10.1016/j.learninstruc.2021.101487
  • Richters, C., Stadler, M., Radkowitsch, A., Schmidmaier, R., Fischer, M. R., & Fischer, F. (2023). Who is on the right track? Behavior-based prediction of diagnostic success in a collaborative diagnostic reasoning simulation. Large-Scale Assessments in Education, 11(1). https://doi.org/10.1186/s40536-023-00151-1  
  • 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 for Medical Education, 41(4), Article Doc48. https://doi.org/10.3205/zma001703

b02

Prof. Dr. Ralf Schmidmaier, Prof. Dr. Frank Fischer & Prof. Dr. Maria Bannert

Personalised fostering of collaborative intervention skills in medicine

B02 focuses on the conditions for effectively developing collaborative intervention skills in a medical context using simulated agents as collaboration partners. Representational scaffolding will be applied by adapting case complexity, while learning process scaffolding will be provided through collaboration scripts with varying degrees of structure. B02 will compare the effects of adaptive (computer-generated) and adaptable (learner-regulated) instructional support, depending on students’ learning prerequisites. To more closely approximate real-world practice, the project will investigate the effects of a role-play simulation with students, in addition to an agent-based computer simulation.

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
  • Brandl, L., Stadler, M., Richters, C., Radkowitsch, A., Fischer, M. R., Schmidmaier, R., & Fischer, F. (2024). Collaborative Problem-Solving in Knowledge-Rich Domains: A Multi-Study Structural Equation Model. International Journal of Computer-Supported Collaborative Learning. Advance online publication. https://doi.org/10.1007/s11412-024-09425-4
  • Braun, L.T., Zottmann, J.M., Adolf, C., Lottspeich, C., Then, C., Wirth, S., Fischer, M.R. & Schmidmaier, R. (2017). Representation scaffolds improve diagnostic efficiency in medical students. Medical Education, 51(11), 1118-1126. https://doi.org/10.1111/medu.13355
  • 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
  • Molenaar, I., de Mooij, S., Azevedo, R., Bannert, M., Järvelä, S., & Gasevic, D. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. Computers in Human Behavior, 139, 107540. https://doi.org/10.1016/j.chb.2022.107540
  • Radkowitsch, A., Sailer, M., Schmidmaier, R., Fischer, M.R. & Fischer, F. (2021). Learning to diagnose collaboratively – Effects of adaptive collaboration scripts in agent-based medical simulations. Learning and Instruction, 75, 101487. https://doi.org/10.1016/j.learninstruc.2021.101487
  • Richters, C., Stadler, M., Radkowitsch, A., Behrmann, F., Weidenbusch, M., Fischer, M.R., Schmidmaier, R., & Fischer, F. (2024a). 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
  • Tausendfreund, O., Braun, L.T., & Schmidmaier, R. (2022). Types of therapeutic errors in the management of osteoporosis made by physicians and medical students. BMC Medical Education, 22, 323. https://doi.org/10.1186/s12909-022-03384-w
  • Vogel, F., Wecker, C., Kollar, I. & Fischer, F. (2017). Socio-cognitive scaffolding with computer-supported collaboration scripts: A meta-analysis. Educational Psychology Review, 29(3), 477–511. https://doi.org/10.1007/s10648-016-9361-7
  • Wölfel, T., Beltermann, E., Lottspeich, C., Vietz, E., Fischer, M.R., & Schmidmaier, R. (2016). Medical ward round competence in internal medicine - an interview study towards an interprofessional development of an Entrustable Professional Activity (EPA). BMC Medical Education, 16, 174. https://doi.org/10.1186/s12909-016-0697-y

b03

Dr. Nicole Heitzmann, Prof. Dr. Stefan Ufer & PD Dr. Leah Braun

Transforming errors into learning opportunities: Effects of personalised feedback after errors in simulations for mathematics and medical education

B03 investigates how personalised feedback in simulations, where learners encounter their own or advocative errors, can facilitate cognitive, metacognitive and social learning processes, as well as diagnostic knowledge and skills in medical and mathematics education. The project compares macro-level and meso-level personalisation strategies for feedback following errors to determine which approach enhances learning processes and diagnostic knowledge and skills more effectively.

Selected publications

  • Braun, L. T., Zottmann, J. M., Adolf, C., Lottspeich, C., Then, C., Wirth, S., Fischer, M. R., & Schmidmaier, R. (2017a) Representation scaffolds improve diagnostic efficiency in medical students. Medical Education, 51(11), 1118-1126. https://doi.org/10.1111/medu.13355. Epub 2017 Jun 6. PMID: 28585351.
  • Braun, L. T., Zwaan, L., Kiesewetter, J., Fischer, M. R., & Schmidmaier, R. (2017b) Diagnostic errors by medical students: results of a prospective qualitative study. BMC Medical Education, 17(1), 191. https://doi.org/10.1186/s12909-017-1044-7
  • Chernikova, O., Heitzmann, N., Fink, M., Timothy, V., Seidel, T., & Fischer, F. (2020a). Facilitating Diagnostic Competences in Higher Education—A Meta-analysis in Medical and Teacher Education. Educational Psychology Review, 32(1), 157–196. (joint fist authorship). https://doi.org/10.1007/s10648-019-09492-2
  • Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer, F. (2020b). Simulation-based learning in higher education: A meta-analysis. Review of Educational Research, 90(4), 499-541. https://journals.sagepub.com/doi/10.3102/0034654320933544
  • Fink, M. C., Heitzmann, N., Siebeck, M., Fischer, F., & Fischer, M. R. (2021). Learning to diagnose accurately through virtual pa-tients: Do reflection phases have an added benefit? BMC Medical Education, 21(1), 523. https://doi.org/10.1186/s12909-021-02937-9
  • Heitzmann, N., Fischer, F., & Fischer, M. R. (2018). Worked examples with errors: When self-explanation prompts hinder learning of teachers’ diagnostic competences on problem-based learning. Instructional Science, 46(2), 245-271. https://doi.org/10.1007/s11251-017-9432-2
  • Heitzmann, N., Seidel, T., Opitz, Ansgar, 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 in Higher Education. Frontline Learning Research, 7(4), 1–24. https://doi.org/10.14786/flr.v7i4.384
  • Heitzmann, N., Stadler, M., Richters, C., Radkowitsch, A., Schmidmaier, R., Weidenbusch, M., & Fischer, M. R. (2023). Learners’ adjustment strategies following impasses in simulations—Effects of prior knowledge. Learning and Instruction, 83, 101632. https://doi.org/10.1016/j.learninstruc.2022.101632
  • Kron, S., Sommerhoff, D., Achtner, M., Stürmer, K., Wecker, C., Siebeck, M., & Ufer, S. (2022). 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
  • Kyaruzi, F., Strijbos, J.-W., & Ufer, S. (2020). Impact of a Short-Term Professional Development Teacher Training on Students’ Perceptions and Use of Errors in Mathematics Learning. Frontiers in Education, 5. https://www.frontiersin.org/article/10.3389/feduc.2020.559122

b04

Prof. Dr. Frank Niklas, Prof. Dr. Michael Sailer & Prof. Dr. Stefan Ufer

Symptoms of learning and behavioural difficulties: Enhancing the diagnostic and intervention-related skills with adaptive feedback

B04 investigates the conditions under which personalised feedback in simulations can support students with varying professional specialisations and corresponding knowledge bases in diagnosing and addressing learning and behavioural difficulties in children. Natural language processing (NLP) methods will be employed to adapt feedback to learners’ prior knowledge and automatically analyse activities within the simulation. The project analyses the effects of different forms of feedback (static/macro-adaptive/micro-adaptive) on participants’ learning processes, as well as diagnostic and intervention-related skills.

Selected publications

  • Bauer, E., Sailer, M., Niklas, F., Greiff, S., Sarbu-Rothsching, S., Zottmann, J. M., Kiesewetter, J., Stadler, M., Fischer, M. R., Seidel, T., Urhahne, D., Sailer, M., & Fischer, F. (in press). AI-Based Adaptive Feedback in Simulations for Teacher Education: An Experimental Replication in the Field. Journal of Computer Assisted Learning. https://dx.doi.org/10.1111/jcal.13123
  • Berner, V.-D., Segerer, R., Oesterlen, E., Seitz-Stein, K., & Niklas, F. (2022). “Good” or “well calculated”? Effects of feedback on performance and self-concept of 5- to 7-year-old children in math. Educational Psychology, 42(3), 296-315. https://doi.org/10.1080/01443410.2021.2001790
  • Codreanu, E., Sommerhoff, D., Huber, S., Ufer, S., Seidel, T. (2020). Between authenticity and cognitive demand: Finding a balance in designing a video-based simulation in the context of mathematics teacher education. Teaching and Teacher Education, 95, Article 103146. https://doi.org/10.1016/j.tate.2020.103146
  • Kron, S., Sommerhoff, D., Achtner, M., Stürmer, K., Wecker, C., Siebeck, M., & Ufer, S. (2022). 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, 135-172. https://doi.org/10.1007/s13138-022-00200-2
  • Niklas, F. & Schneider, W. (2014). Casting the die before the die is cast: The importance of the home numeracy environment for preschool children. European Journal of Psychology of Education, 29(3), 327-345. https://doi.org/10.1007/s10212-013-0201-6
  • Sailer, M., Bauer, E., Hofmann, R., Kiesewetter, J., Glas, J., Gurevych, I., & Fischer, F. (2023). Adaptive feedback from artificial neural networks facilitates pre-service teachers’ diagnostic reasoning in simulation-based learning. Learning and Instruction, Article 101620. https://doi.org/10.1016/j.learninstruc.2022.101620
  • Sailer, M., Ninaus, M., Huber, S. E., Bauer, E., & Greiff, S. (2024). The End is the Beginning is the End: The closed-loop learning analytics framework. Computers in Human Behavior, Article 108305. https://doi.org/10.1016/j.chb.2024.108305
  • Schmiedeler, S., Khambatta, K., Hartmann, J. & Niklas, F. (2020). „Wenn den Zappelphilipp die Aufschieberitis packt“: Zusammen-hänge zwischen Prokrastination und ADHS-Symptomen und mögliche Mediatoren. Zeitschrift für Pädagogische Psychologie, 34, 23-34. https://doi.org/10.1024/1010-0652/a000248  
  • Vogel, F., Kollar, I., Fischer, F., Reiss, K. & Ufer, S. (2022). Adaptable scaffolding of mathematical argumentation skills: The role of self-regulation when scaffolded with CSCL scripts and heuristic worked examples. International Journal of Computer-Supported Collaborative Learning, 17, 39–64. https://doi.org/10.1007/s11412-022-09363-z

b05

Prof. Dr. Doris Holzberger & Prof. Dr. Mario Gollwitzer

Diagnosing and intervening in teams: Personalising collaborative problem-simulations based on individual- and group-level characteristics

B05 investigates how personalised simulations for collaborative problem-solving (CPS) tasks in higher education can enhance social learning processes and diagnostic skills. The project focuses on identifying individual- and group-level prerequisites that affect group performance and learning incollaborative settings and investigating the conditions for effective personalised learning process scaffolding for social learning processes and CPS performance. The project will advance a taxonomy of critical situations and a standardised assessment tool for social learning processes.

Selected publications

  • Brandl, L., Stadler, M., Richters, C., Radkowitsch, A., Fischer, M. R., Schmidmaier, R., & Fischer, F. (2024). Collaborative problem-solving in knowledge-rich domains: A multi-study structural equation model. International Journal of Computer-Supported Collaborative Learning, 19, 341-368. https://doi.org/10.1007/s11412-024-09425-4
  • Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer, F. (2020). Simulation-based learning in higher education: A meta-analysis. Review of Educational Research, 90(4), 1-43. https://doi.org/10.3102/0034654320933544
  • 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. and 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
  • Gollwitzer, M., Magraw-Mickelson, Z., Vollan, B., & Süssenbach, P. (2021). Victim sensitivity in groups: When is one a detriment to all? Journal of Theoretical Social Psychology, 5(1), 3-13. https://doi.org/10.1002/jts5.76
  • Holzberger, D., Brandl, L., Stadler, M., Obersteiner, A., Chernikova, O., Pickal, A. J., Nickl, M., Richters, C., Kron, S., Wecker, C., Heitzmann, N., Irmer, M., Corves, C., Radkowitsch, A., Neuhaus, B. J., Fischer, M. R., Ufer, S., Fischer, F., Sommerhoff, D., Sei-del, T. (2024, in-principle accepted). The interplay between expectancy and value and its generalizability across simulation-based learning environments: An individual participant data meta-analysis. Registered Report in Journal of Educational Psychology, in-principle accepted.
  • Holzberger, D., & Prestele, E. (2021). Teacher self-efficacy and self-reported cognitive activation and classroom management: A multilevel perspective on the role of school characteristics. Learning and Instruction, 76, Article 101513. https://doi.org/10.1016/j.learninstruc.2021.101513
  • Holzberger, D., Maurer, C., Kunina-Habenicht, O., & Kunter, M. (2021). Ready to teach? A profile analysis on cognitive and motiva-tional-affective teacher characteristics at the end of pre-service teacher education and long-term effects on teachers’ occupational well-being. Teaching and Teacher Education, 100. https://doi.org/10.1016/j.tate.2021.103285
  • Magraw-Mickelson, Z., Süssenbach, P., & Gollwitzer, M. (2022). The virus of distrust: How one victim-sensitive group member can affect the entire group’s outcomes. European Journal of Social Psychology, 52(3), 487-499. https://doi.org/10.1002/ejsp.2832
  • Radkowitsch, A., Sailer, M., Schmidmaier, R., Fischer, M. R., & Fischer, F. (2021). Learning to diagnose collaboratively – effects of adaptive collaboration scripts in agent-based medical simulations. Learning and Instruction, 75, Article 101487 https://doi.org/10.1016/j.learninstruc.2021.101487
  • Soellner, N., Eiberle, M., Berberat, P., Schulz, C., Hinzmann, D., Rath, S., Haseneder, R., & Gartmeier, M. (2022). Just showing is not enough: First-person-view-videos as a feedback tool in resuscitation. Studies in Educational Evaluation, 72, 101100. https://doi.org/10.1016/j.stueduc.2021.101100

 

b06

Prof. Dr. Bernhard Schmidt-Hertha & Prof. Dr. Martin Fischer

Curricular opportunities and university teachers’ skills to use adaptive simulation environments to support individuals and small learner groups

B06 aims to investigate the conditions for successfully incorporating adaptive simulation environments into educational programmes for teachers and medical professionals. The project will focus on identifying specific prerequisites for higher education teachers to effectively support individual and small groups of learners who learn with personalised simulations. The project seeks to clarify these prerequisites and then develop and test a training programme to enhance the relevant skills of higher education teachers to use personalised learning with simulations in their courses.

Selected publications

  • Bonnes, C., Leiser, C., Schmidt‐Hertha, B., Rott, K.J., & Hochholdinger, S. (2020). The relationship between trainers’ media‐didactical competence and media‐didactical self‐efficacy, attitudes and use of digital media in training. International Journal for Training & Development, 24(1), 74-88. https://doi.org/10.1111/ijtd.12171
  • Graichen, M., Mikelskis-Seifert, S., Hinderer, L., Scharenberg, K.,& Rollett, W.(2024). Unveiling Potential: Fostering Students’ Self Concepts in Science Education by Designing Inclusive Educational Settings. Education Sciences, 14(6), 632. https://doi.org/10.3390/educsci14060632
  • Jünger, J., Pante, S.V., Ackel-Eisnach, K., Wagener, S., & Fischer, M.R. (2020). Do it together! Conception and long-term results of the trans-institutional Master of Medical Education (MME) program in Germany. GMS Journal for Medical Education, 37(3), Doc33. https://doi.org/10.3205/zma001326
  • Kiesewetter, J., Sailer, M., Jung, V.M., Schönberger, R., Bauer, E., Zottmann, JM., Hege, I., Zimmermann, H., Fischer, F., & Fischer, M.R. (2020). Learning clinical reasoning: how virtual patient case format and prior knowledge interact. BMC Medical Education, 20(1), 73. https://doi.org/10.1186/s12909-020-1987-y
  • Kühl, S.J., Schneider, A., Kestler, H.A., Toberer, M., Kühl, M., & Fischer M.R. (2019). Investigating the self-study phase of an invert-ed biochemistry classroom - collaborative dyadic learning makes the difference. BMC Medical Education, 19(1), 64. https://doi.org/10.1186/s12909-019-1497-y. PMID: 30819178; PMCID: PMC6393989 
  • Rohs, M., Schmidt-Hertha, B., Rott, K., & Bolten, R. (2019). Measurement of media pedagogical competences of adult educators. European Journal for Research on the Education and Learning of Adults, 10(3), 304-324. https://doi.org/10.3384/rela.2000-7426.ojs393
  • Rott, K.J., Lao, L., Petridou, E., & Schmidt-Hertha, B. (2022). Needs and requirements for an additional AI qualification during dual vocational training: Results from studies of apprentices and teachers. Computers and Education: Artificial Intelligence, 100102. https://doi.org/10.1016/j.caeai.2022.100102
  • Sailer, M., Schultz-Pernice, F., & Fischer, F. (2021a). Contextual facilitators for learning activities involving technology in higher education: The C♭-model. Computers in Human Behavior, 121, 106794. https://doi.org/10.1016/j.chb.2021.106794
  • Schmidt-Hertha, B. (2020). Vermittlung medienpädagogischer Kompetenz in der Fort- und Weiterbildung von Lehrkräften. Zeitschrift für Pädagogik, 66(2), 191-207. https://doi.org/10.3262/ZP2002191
  • Tafertshofer, L., Werner, E.-M. & Schmidt-Hertha, B. (2018). Grundlagen der Reputation von Studienstandorten: Bewertungsmaß-stäbe für die Qualität von Hochschulstandorten und Studiengängen aus der Sicht von sozialwissenschaftlichen Professorinnen und Professoren. Beiträge zur Hochschulforschung, 2018 (2), 68-88.