Repairing Errors With Elaborative Feedback in Computerised Learning Environments.
Background: Although automatic elaborative feedback (EF) is effective for teaching conceptual learning in science, there is insufficient evidence on how to adapt it in computer‐based question‐answering activities. Objectives: This study aims to examine how we can make automatic EF more effective and...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 2; pp. 1 - 20 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2026
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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=192476905&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192476905 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Apr2026 vid: 42 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 192476905 192476905 192476905 10.1002/jcal.70199 192476905 ppf: 1 ppct: 19 formats: tig: atl: Repairing Errors With Elaborative Feedback in Computerised Learning Environments. aug: au: Martínez, Tomás García, Arantxa Cerdán, Raquel Vidal‐Abarca, Eduardo affil: Universitat de València, València, Spain sug: subj: Adaptation, Psychological Learning Computer-Assisted Instruction Learning Environment Problem Solving Concept Formation Human Male Female Adult Students, Undergraduate Psychosocial Factors Colleges and Universities Spain Spain Educational Technology Skill Acquisition Professional Knowledge Science Education Teaching Methods Experimental Studies Comparative Studies Descriptive Statistics Multivariate Analysis of Variance One-Way Analysis of Variance Post Hoc Analysis Questionnaires Funding Source Adult: 19-44 years Male Female ab: Background: Although automatic elaborative feedback (EF) is effective for teaching conceptual learning in science, there is insufficient evidence on how to adapt it in computer‐based question‐answering activities. Objectives: This study aims to examine how we can make automatic EF more effective and tailored according to the knowledge revision process proposed in studies with refutative texts. Methods: Students were required to read a science text and then answer a series of inferential multiple‐choice questions. After each answer, students received corrective feedback (right/wrong) plus automatic EF, according to their experimental condition, and then had a second attempt to answer. Three types of EFs were compared: one focused on elaborating the correct answer (EFExplicative), another focused on correcting incorrect ideas (EFRefutative), and another contained a neutral message (NFControl). Two studies were conducted, one without text access while responding after EF, and the other with access to the text. Results and Conclusions: The results of both studies show that EFExplicative is more difficult to process than EFRefutative, although the effects on performance on a second response attempt varied between studies. When the text was unavailable, EFRefutative produced a significantly higher proportion of correct responses than EFExplicative, and both groups performed better than NFControl. Nevertheless, when the text was available, these results were partially attenuated. After discovering errors in their learning process, learners tend to initiate a revision of their knowledge. Feedback that is congruent with this revision process was found to increase efficiency. Key Points: What is currently known about this topic? ○Answering questions during learning improves long‐term retention, especially if we include feedback and explanations.○Explanations should be tailored to the learning context and provide immediate hints, justifications, or solutions.○In science education, errors and misconceptions are difficult to correct and prevent students from acquiring accurate knowledge.○When students read rebuttal information, they are more engaged in correcting the error.What does this paper add? ○Helps to construct more effective explanations and tailor feedback to the learner's needs.○Extends previous research on refutative texts to analyse how students process and interact with elaborative feedback during question‐solving.Implications for practice/or policy ○After discovering errors in their learning, learners need to initiate a process of revision.○Explanations, feedback, or help given to learners should focus on this review process.○Access to the text can partially compensate for the lack of elaborative feedback, but it is less effective than tailored feedback. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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