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...
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| Published in: | Learning and instruction Vol. 83; p. 101620 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.02.2023
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| ISSN: | 0959-4752, 1873-3263 |
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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.
•Artificial neural networks provided automatic adaptive feedback in teacher education.•Automatic adaptive feedback fostered the quality of justifications in simulations.•Automatic adaptive feedback did not affect individual learners' diagnostic accuracy.•Static feedback is inferior for collaborators' accuracy.•Artificial neural networks fostered learning of complex reasoning outcomes. |
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| AbstractList | 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.
•Artificial neural networks provided automatic adaptive feedback in teacher education.•Automatic adaptive feedback fostered the quality of justifications in simulations.•Automatic adaptive feedback did not affect individual learners' diagnostic accuracy.•Static feedback is inferior for collaborators' accuracy.•Artificial neural networks fostered learning of complex reasoning outcomes. |
| ArticleNumber | 101620 |
| Author | Sailer, Michael Hofmann, Riikka Kiesewetter, Jan Fischer, Frank Gurevych, Iryna Bauer, Elisabeth Glas, Julia |
| Author_xml | – sequence: 1 givenname: Michael orcidid: 0000-0001-6831-5429 surname: Sailer fullname: Sailer, Michael email: Michael.Sailer@psy.lmu.de organization: Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany – sequence: 2 givenname: Elisabeth orcidid: 0000-0003-4078-0999 surname: Bauer fullname: Bauer, Elisabeth organization: Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany – sequence: 3 givenname: Riikka orcidid: 0000-0001-8042-0272 surname: Hofmann fullname: Hofmann, Riikka organization: Faculty of Education, University of Cambridge, Cambridge, UK – sequence: 4 givenname: Jan orcidid: 0000-0001-8165-402X surname: Kiesewetter fullname: Kiesewetter, Jan organization: Institute for Medical Education, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany – sequence: 5 givenname: Julia surname: Glas fullname: Glas, Julia organization: Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany – sequence: 6 givenname: Iryna surname: Gurevych fullname: Gurevych, Iryna organization: Ubiquitous Knowledge Processing Lab, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany – sequence: 7 givenname: Frank surname: Fischer fullname: Fischer, Frank organization: Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany |
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| Keywords | Teacher education Natural language processing Simulation-based learning Adaptive feedback Artificial intelligence |
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