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Repairing Errors with Elaborative Feedback in Computerised Learning Environments

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Title: Repairing Errors with Elaborative Feedback in Computerised Learning Environments
Language: English
Authors: Tomás Martínez (ORCID 0000-0001-6165-9085), Arantxa García, Raquel Cerdán, Eduardo Vidal-Abarca
Source: Journal of Computer Assisted Learning. 2026 42(2).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 19
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Error Correction, Feedback (Response), Computer Uses in Education, Multiple Choice Tests, Automation, Performance, Learning Processes, Student Needs, Problem Solving, Computer Assisted Testing, Instructional Effectiveness
DOI: 10.1002/jcal.70199
ISSN: 0266-4909
1365-2729
Abstract: 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 (EF[subscript Explicative]), another focused on correcting incorrect ideas (EF[subscript Refutative]), and another contained a neutral message (NF[subscript Control]). 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 EF[subscript Explicative] is more difficult to process than EF[subscript Refutative], although the effects on performance on a second response attempt varied between studies. When the text was unavailable, EF[subscript Refutative] produced a significantly higher proportion of correct responses than EF[subscript Explicative], and both groups performed better than NF[subscript Control]. 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.
Abstractor: As Provided
Notes: https://osf.io/wk28s
Entry Date: 2026
Accession Number: EJ1500517
Database: ERIC
FullText Text:
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  Data: Repairing Errors with Elaborative Feedback in Computerised Learning Environments
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  Data: English
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Tomás+Martínez%22">Tomás Martínez</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6165-9085">0000-0001-6165-9085</externalLink>)<br /><searchLink fieldCode="AR" term="%22Arantxa+García%22">Arantxa García</searchLink><br /><searchLink fieldCode="AR" term="%22Raquel+Cerdán%22">Raquel Cerdán</searchLink><br /><searchLink fieldCode="AR" term="%22Eduardo+Vidal-Abarca%22">Eduardo Vidal-Abarca</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2026 42(2).
– Name: Avail
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: Y
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  Data: 19
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  Label: Publication Date
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  Data: 2026
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Error+Correction%22">Error Correction</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Uses+in+Education%22">Computer Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+Choice+Tests%22">Multiple Choice Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Performance%22">Performance</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Needs%22">Student Needs</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Testing%22">Computer Assisted Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Effectiveness%22">Instructional Effectiveness</searchLink>
– Name: DOI
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  Data: 10.1002/jcal.70199
– Name: ISSN
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  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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 (EF[subscript Explicative]), another focused on correcting incorrect ideas (EF[subscript Refutative]), and another contained a neutral message (NF[subscript Control]). 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 EF[subscript Explicative] is more difficult to process than EF[subscript Refutative], although the effects on performance on a second response attempt varied between studies. When the text was unavailable, EF[subscript Refutative] produced a significantly higher proportion of correct responses than EF[subscript Explicative], and both groups performed better than NF[subscript Control]. 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.
– Name: AbstractInfo
  Label: Abstractor
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  Data: As Provided
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  Data: https://osf.io/wk28s
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  Data: 2026
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  Data: EJ1500517
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        Value: 10.1002/jcal.70199
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      – SubjectFull: Error Correction
        Type: general
      – SubjectFull: Feedback (Response)
        Type: general
      – SubjectFull: Computer Uses in Education
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      – SubjectFull: Multiple Choice Tests
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      – SubjectFull: Student Needs
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      – SubjectFull: Problem Solving
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      – SubjectFull: Computer Assisted Testing
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      – SubjectFull: Instructional Effectiveness
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      – TitleFull: Repairing Errors with Elaborative Feedback in Computerised Learning Environments
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