Perfectionistic Students: Contributing Factors, Impacts, and Teacher Strategies
Bibliographic record
Abstract
Aims: Students with perfectionistic tendencies may be present in classrooms; teachers would benefit from knowledge regarding contributing factors to, outcomes of, and evidence regarding intervention.The purpose of this paper was to provide an overview of evidence regarding how perfectionism has been defined, the role of biology and environmental factors in perfectionistic tendencies, the impact of perfectionism in the academic setting, and whether there is any benefit to interventions designed to alter perfectionistic tendencies.Results: It was found that biological factors are related to perfectionistic tendencies; however, environmental factors such as parenting style/family characteristics are also important.Perfectionistic tendencies are evident in childhood, and are believed to remain fairly stable over time.Some interventions may reduce levels of perfectionistic tendencies in certain individuals, but it seems that reducing perfectionistic thoughts and behaviours may also be detrimental for some.Conclusion: Because there is a lack of research directly regarding perfectionism in the school system, generalizations from students and nonstudents samples are used to develop some recommendations for teachers working with students who may be perfectionistic.Suggestions for teachers who have students with perfectionistic tendencies are provided.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".