Adaptive feedback from artificial neural networks facilitates pre-service teachers' diagnostic reasoning in simulation-based learning

  • In simulations, pre-service teachers need sophisticated feedback to develop complex skills such as diagnostic reasoning. In an experimental study with N = 178 pre-service teachers about simulated pupils with learning difficulties, we investigated the effects of automatic adaptive feedback, which is based on artificial neural networks, on pre-service teachers' diagnostic reasoning. Diagnostic reasoning was operationalised as diagnostic accuracy and the quality of justifications. We compared automatic adaptive feedback with static feedback, which we provided in form of an expert solution. Further, we experimentally manipulated whether the learners worked individually or in dyads on the computer lab-based simulations. Results show that adaptive feedback facilitates pre-service teachers’ quality of justifications in written assignments, but not their diagnostic accuracy. Further, static feedback even had detrimental effects on the learning process in dyads. Automatic adaptive feedback inIn simulations, pre-service teachers need sophisticated feedback to develop complex skills such as diagnostic reasoning. In an experimental study with N = 178 pre-service teachers about simulated pupils with learning difficulties, we investigated the effects of automatic adaptive feedback, which is based on artificial neural networks, on pre-service teachers' diagnostic reasoning. Diagnostic reasoning was operationalised as diagnostic accuracy and the quality of justifications. We compared automatic adaptive feedback with static feedback, which we provided in form of an expert solution. Further, we experimentally manipulated whether the learners worked individually or in dyads on the computer lab-based simulations. Results show that adaptive feedback facilitates pre-service teachers’ quality of justifications in written assignments, but not their diagnostic accuracy. Further, static feedback even had detrimental effects on the learning process in dyads. Automatic adaptive feedback in simulations offers scalable, elaborate, process-oriented feedback in real-time to high numbers of students in higher education.show moreshow less

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Metadaten
Author:Michael SailerORCiDGND, Elisabeth BauerORCiDGND, Riikka Hofmann, Jan Kiesewetter, Julia Glas, Iryna Gurevych, Frank Fischer
URN:urn:nbn:de:bvb:384-opus4-1090310
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/109031
ISSN:0959-4752OPAC
Parent Title (English):Learning and Instruction
Publisher:Elsevier BV
Place of publication:Amsterdam
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2023/11/10
Tag:Developmental and Educational Psychology; Education
Volume:83
First Page:101620
DOI:https://doi.org/10.1016/j.learninstruc.2022.101620
Institutes:Philosophisch-Sozialwissenschaftliche Fakultät
Philosophisch-Sozialwissenschaftliche Fakultät / Empirische Bildungsforschung
Philosophisch-Sozialwissenschaftliche Fakultät / Empirische Bildungsforschung / Lehrstuhl für Learning Analytics and Educational Data Mining
Dewey Decimal Classification:3 Sozialwissenschaften / 37 Bildung und Erziehung / 370 Bildung und Erziehung
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)