(De)motivating Zero‐Performing Students With Negative Feedback: Does the Salience of Performance Information Matter?
Background: Providing students with information on their current performance could help them improve by stimulating their reflection, but negative feedback that saliently mirrors task‐related failure can harm motivation. In the context of automated scoring based on artificial intelligence, we explor...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 4; pp. 1 - 17 |
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| Autores principales: | , , , |
| Formato: | research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2025
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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=186918266&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186918266 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2025 vid: 41 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 186918266 186918266 186918266 10.1111/jcal.70070 186918266 ppf: 1 ppct: 16 formats: tig: atl: (De)motivating Zero‐Performing Students With Negative Feedback: Does the Salience of Performance Information Matter? aug: au: Steinbach, Marlene Fleckenstein, Johanna Kuklick, Livia Meyer, Jennifer affil: Leibniz Institute for Science and Mathematics Education, Germany, Kiel, Germany sug: subj: Feedback Motivation Writing Academic Performance Self Concept Students, High School Psychosocial Factors Funding Source Germany Human Computer-Assisted Instruction Learning Environment Randomized Controlled Trials Random Assignment Pretest-Posttest Design Self Report Task Performance and Analysis Descriptive Statistics Summated Rating Scaling Scales Coefficient alpha Questionnaires Algorithms Artificial Intelligence Double-Blind Studies Multiple Regression Models, Statistical Data Analysis Software Male Female Adolescence Adolescent: 13-18 years Male Female ab: Background: Providing students with information on their current performance could help them improve by stimulating their reflection, but negative feedback that saliently mirrors task‐related failure can harm motivation. In the context of automated scoring based on artificial intelligence, we explored how feedback on written texts might be designed to be least detrimental for zero‐performing students who are likely to receive negative feedback frequently and might suffer from its motivational consequences. Objectives: This experiment set out to investigate whether making the negative performance information in automated feedback messages less salient reduces the potential threat of negative feedback for zero‐performing students' task‐specific self‐concept, intrinsic value, and performance. Methods: A sample of 105 (Mage = 13.97 years) zero‐performing students received negative feedback with either more or less salient performance information after completing an English writing task. We used regression analysis to examine pre–post effects and group differences in self‐concept, intrinsic value, and performance. Results and Conclusions: The analyses showed that zero‐performing students' performance improved but their self‐concept and intrinsic value declined over the course of two writing tasks, with feedback provided after the initial task. Contrary to expectations, our findings showed that students' task‐specific self‐concept and intrinsic value declined more in the condition with less salient performance information (i.e., without a red cross as a salient visual performance cue). Our findings highlight the motivational potential of performance information and are discussed in terms of the need for further research into how negative feedback can be designed to effectively motivate and support zero‐performing learners. pubtype: Academic Journal doctype: research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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