FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning
| Autor: | Jan Kiesewetter, Iryna Gurevych, Elisabeth Bauer, Jonas Pfeiffer, Claudia Schulz, Frank Fischer, Michael Sailer, Martin R. Fischer, Christian M. Meyer, Jan M. Zottmann |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Process (engineering) Computer science Diagnostic reasoning 02 engineering and technology Constructive Teacher education 03 medical and health sciences Annotation 0302 clinical medicine Human–computer interaction ComputingMilieux_COMPUTERSANDEDUCATION 030221 ophthalmology & optometry 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Use case Computation and Language (cs.CL) |
| Zdroj: | EMNLP/IJCNLP (3) |
| DOI: | 10.18653/v1/d19-3013 |
| Popis: | Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data. Diagnosing is an exceptionally difficult skill to obtain but vital for many different professions (e.g., medical doctors, teachers). Previous case simulation systems are limited to multiple-choice questions and thus cannot give constructive individualized feedback on a student's diagnostic reasoning process. Given initially only limited data, we leverage a (replaceable) NLP model to both support experts in their further data annotation with automatic suggestions, and we provide automatic feedback for students. We argue that because the central model consistently improves, our interactive approach encourages both students and instructors to recurrently use the tool, and thus accelerate the speed of data creation and annotation. We show results from two user studies on diagnostic reasoning in medicine and teacher education and outline how our system can be extended to further use cases. Comment: EMNLP 2019 - Demo |
| Databáze: | OpenAIRE |
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