When negative feedback harms: a systematic review of the unintended consequences of negative feedback on psychological, attitudinal, and behavioral responses
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".