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Record W3133706844 · doi:10.1017/9781108589789.027

Teachers’ and Students’ Beliefs and Perspectives about Corrective Feedback

2021· book-chapter· en· W3133706844 on OpenAlexaff
YouJin Kim, Tamanna Mostafa

Bibliographic record

VenueCambridge University Press eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsCarleton UniversityUniversity of Victoria
Fundersnot available
KeywordsCorrective feedbackSituational ethicsPsychologyMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

The current chapter focuses on two main stakeholders of corrective feedback: teachers and learners, and it discusses whether and how teachers’ and learners’ beliefs or attitudes toward corrective feedback impact the effectiveness of corrective feedback. Previous research on both oral and written corrective feedback is reviewed. In terms of teachers’ beliefs of corrective feedback and their feedback practices, some research findings showed that teachers’ beliefs are not always in line with their actual classroom practices related to the use of different types of oral corrective feedback. Learners’ beliefs about the effectiveness of corrective feedback, particularly written corrective feedback, were found to be an important factor of learner engagement with corrective feedback. Recent corrective feedback research claims that teacher and learner beliefs are not static. Accordingly, the current literature review shows methodological changes over time, capturing the situational and dynamic patterns of learners’ and teachers’ beliefs about corrective feedback. The overall findings suggest that teachers’ and learners’ beliefs about CF are multifaceted and could be impacted by various contextual factors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.269
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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