FAMULUS - Fostering diagnostic competence in medical and teacher education via adaptive online-case-simulations
Project titel:
FAMULUS - Fostering diagnostic competence in medical and teacher education via adaptive online-case-simulations
Type of project and funding:
funded by the BMBF
Duration:
03/2017 - 02/2020
Project team:
Project descripton:
In the context digital higher education and teacher training, FAMULUS explores effects of online case simulations on the learning of diagnostic reasoning and argumentation. In particular, the project investigates the use of AI-based text analyses to provide automated adaptive feedback. Further investigations concern different case formats and the effects of collaborative learning. The project is initiated and lead by Prof. Dr. Frank Fischer. Research associates are Dr. Michael Sailer und Elisabeth Bauer. Between 03/2017 and 06/2020 funding is provided by the Federal Ministry of Education and Research (BMBF) and further analyses are supported by the Elite Network of Bavaria (ENB).
Research question:
1. Which effects do different case formats (whole case vs. serial cue) of online simulations have on epistemic-diagnostic processes and diagnostic competences of pre-service teachers with different levels of prior knowledge?
2. To what extent can epistemic-diagnostic activities and diagnostic competences of pre-service teachers, who are learning in online simulations, be analyzed automatically?
3. Which effects does automatic adaptive feedback have on pre-service teachers’ epistemic-diagnostic activities and diagnostic competences, compared to static expert solutions?
4. Do effects of automatic adaptive feedback on pre-service teachers’ epistemic-diagnostic activities and diagnostic competences differ for different social modes of learning (individual learners vs. collaboratively learning dyads of learners)?
5. To what extent do effects of automatic adaptive feedback and social mode of learning on pre-service teachers’ simulation-based learning of diagnostic competences replicate in a field study?
Coorperation partners:
- Institut für Didaktik und Ausbildungsforschung in der Medizin am Klinikum der Ludwig-Maximilians-Universität München
- Ubiquitous Knowledge Processing (UKP) / Computer Science Department, TU Darmstadt, Prof. Dr. Iryna Gurevych
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, 2787.
- Bauer, E., Sailer, M., Kiesewetter, Fischer, M. R., & Fischer, F. (2021). Pre-Service Teachers’ Argumentations in the Context of Assessment. ICLS 2021 Proceedings, 669- 672.
- Bauer, E., Sailer, M., Kiesewetter, J., Schulz, C., Pfeiffer, J., Gurevych, I., ... & Fischer, F. (2019). Using ENA to analyze pre-service teachers’ diagnostic argumentations: a conceptual framework and initial applications. In International Conference on Quantitative Ethnography (pp. 14-25). Springer, Cham.
- Bauer, E., Sailer, M., Kiesewetter, J., Shaffer, D. W., Schulz, C., Pfeiffer, J., ... & Fischer, F. (2020). Pre-Service Teachers’ Diagnostic Argumentation: What is the Role of Conceptual Knowledge and Cross-Domain Epistemic Activities?. ICLS 2020 Proceedings, 2399-2400.
- Kiesewetter, J., Sailer, M., Jung, V. M., Schönberger, R., Bauer, E., Zottmann, J. M., ... & Fischer, M. R. (2020). Learning clinical reasoning: how virtual patient case format and prior knowledge interact. BMC medical education, 20(1), 1-10.
- Pfeiffer, J., Meyer, C. M., Schulz, C., Kiesewetter, J., Zottmann, J., Sailer, M., ... & Gurevych, I. (2019). FAMULUS: interactive annotation and feedback generation for teaching diagnostic reasoning. arXiv preprint arXiv:1908.11254.
- Schulz, C., Meyer, C. M., Kiesewetter, J., Sailer, M., Bauer, E., Fischer, M. R., ... & Gurevych, I. (2019). Analysis of automatic annotation suggestions for hard discourse-level tasks in expert domains. arXiv preprint arXiv:1906.02564.
- Schulz, C., Sailer, M., Kiesewetter, J., Meyer, C. M., Gurevych, I., Fischer, F., & Fischer, M. R. (2017). Fallsimulationen und automatisches adaptives Feedback mittels Künstlicher Intelligenz in digitalen Lernumgebungen. e-teaching. org Themenspecial “Was macht Lernen mit digitalen Medien erfolgreich?”, 1-14.