AI‐Based Adaptive Feedback in Simulations for Teacher Education: An Experimental Replication in the Field.
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 1; pp. 1 - 21 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183981468&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183981468 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2025 vid: 41 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183981468 183981468 183981468 10.1111/jcal.13123 183981468 ppf: 1 ppct: 20 formats: tig: atl: AI‐Based Adaptive Feedback in Simulations for Teacher Education: An Experimental Replication in the Field. aug: au: Bauer, Elisabeth Sailer, Michael Niklas, Frank Greiff, Samuel Sarbu‐Rothsching, Sven Zottmann, Jan M. Kiesewetter, Jan Stadler, Matthias Fischer, Martin R. Seidel, Tina Urhahne, Detlef Sailer, Maximilian Fischer, Frank affil: Learning Analytics and Educational Data Mining, University of Augsburg, Augsburg, Germany sug: subj: Natural Language Processing Feedback Methods Simulations Faculty Education Outcomes of Education Diagnostic Reasoning Education Judgment Education Learning Methods Funding Source Germany Human Randomized Controlled Trials Random Assignment Field Studies Pretest-Posttest Design Neural Networks (Computer) Computer-Assisted Instruction Multivariate Analysis of Covariance Analysis of Variance Data Analysis Software Descriptive Statistics Comparative Studies Colleges and Universities Education, Medical ab: Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks enhanced preservice teachers' diagnostic reasoning in a digital case‐based simulation. However, the effectiveness of the simulation with the different feedback types and the generalizability to field settings remained unclear. Objectives: We tested the generalizability of the previous findings and the effectiveness of a single simulation session with either feedback type in an experimental field study. Methods: In regular online courses, 332 preservice teachers at five German universities participated in one of three randomly assigned groups: (1) a simulation group with NLP‐based adaptive feedback, (2) a simulation group with static feedback and (3) a no‐simulation control group. We analysed the effect of the simulation with the two feedback types on participants' judgement accuracy and justification quality. Results and Conclusions: Compared with static feedback, adaptive feedback significantly enhanced justification quality but not judgement accuracy. Only the simulation with adaptive feedback significantly benefited learners' justification quality over the no‐simulation control group, while no significant differences in judgement accuracy were found. Our field experiment replicated the findings of the laboratory study. Only a simulation session with adaptive feedback, unlike static feedback, seems to enhance learners' justification quality but not judgement accuracy. Under field conditions, learners require adaptive support in simulations and can benefit from NLP‐based adaptive feedback using artificial neural networks. pubtype: Academic Journal doctype: pictorial research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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