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Record W4386011020 · doi:10.1080/03075079.2023.2248490

When negative feedback harms: a systematic review of the unintended consequences of negative feedback on psychological, attitudinal, and behavioral responses

2023· review· en· W4386011020 on OpenAlexaff
Marlee Mercer, Duygu Biricik Gulseren

Bibliographic record

VenueStudies in Higher Education · 2023
Typereview
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyUnintended consequencesContext (archaeology)Negative feedbackAffect (linguistics)Social psychologyMultidisciplinary approachCognitionApplied psychologyCognitive psychology

Abstract

fetched live from OpenAlex

A plethora of research emphasizes the importance of performance feedback as an adaptive way to improve the outcomes of undergraduate students. However, research on negative performance feedback (i.e. communicating the results of a critical assessment related to student performance) and the potential unintended negative consequences remains fragmented and non-comprehensive. The current systematic review provides an integrative synthesis of the literature that details the unintended negative consequences of negative performance feedback on undergraduate students. It also identifies the contexts in which these consequences are more likely to occur. Using the 36 articles that fit the study’s search criteria, we found negative effects on self-efficacy, cognition, affect, and behavior. These factors were moderated by the characteristics of the feedback provider and the feedback receiver, the feedback style, and demographic variables. Based on our review, we propose a new integrative model. This study contributes to the literature and practice of teaching and learning by providing educators with an up-to-date review of negative feedback. It also provides a multidisciplinary examination and identifies future research directions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.410
GPT teacher head0.540
Teacher spread0.131 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations17
Published2023
Admission routes1
Has abstractyes

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