Adaptive Feedback from Artificial Neural Networks Facilitates Pre-Service Teachers’ Diagnostic Reasoning in Simulation-based Learning

Authors
Sailer, Michael 
Bauer, Elisabeth 
Hofmann, Riikka 
Kiesewetter, Jan 
Glas, Julia 

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Article
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Abstract

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 in simulations offers scalable, elaborate, process-oriented feedback in real-time to high numbers of students in higher education.

Publication Date
Online Publication Date
2022-04-12
Acceptance Date
2022-04-01
Keywords
Journal Title
Learning and Instruction
Journal ISSN
0959-4752
Volume Title
Publisher
Elsevier
Sponsorship
This research was supported by a grant of the German Federal Ministry of Research and Education (Grant No.: 16DHL1040) and by the Elite Network of Bavaria (K-GS-2012-209).