Le biais d'auto-évaluation de compétence scolaire Risque ou opportunité pour la réussite des élèves ?
Bibliographic record
Abstract
Beliefs about competence can be a limitation for some students and an asset for others in acquiring the basic knowledge that is essential for their academic and social future. The SchoolBias program focused on students' judgments of their academic competence, in particular the difference between their actual potential and the way they evaluate their competence. This difference reflects the self- evaluation bias of academic competence. This bias can be positive (overestimation of competence) or negative (underestimation of competence). The program consisted of two distinct levels of analysis. At an intra-individual level (student), the aim was to gather information about the dynamics of positive and negative self-evaluation biases, and their effects on basic learning at different levels of schooling. At an inter-individual level (teacher judgment), we investigated how teachers judged students who had either positively or negatively biased assessments of their academic competence. Lastly, we extended the study of the impact of these perceptions to other cultures.Twenty-two studies were conducted in ordinary classroom situations. They involved more than 5000 students of various grades in elementary and junior high schools, and more than 200 teachers. They combined longitudinal (developmental trajectory analyses) and cross-sectional approaches as well as experimental and correlational designs. A method of structured cognitive interviews adapted for children was also used to analyze the thinking patterns of positively and negatively biased students. The study of students' competence beliefs in different cultures (China, France, Canada-Quebec, and Russia) also required a specific methodology for cross-cultural validation of the scales and the material used.First of all, the results of the trajectory analyses showed that as early as the middle of elementary school, students can present a self-evaluation bias in one fundamental subject, without necessarily presenting a bias in other subjects. These results therefore underline the importance of not only considering the self-evaluation bias of academic competence at a general or global level, but also at a specific one. They also indicate that overestimating one's academic competence is beneficial to the student, whereas underestimating it is detrimental to their academic adjustment throughout their schooling. At the intra-individual level, students who overestimate themselves are more motivated, self-regulate well, are more actively involved in their learning, and perform better than their negativelybiased peers. At the inter-individual level, since they appear to be more in line with schoolexpectations, teachers judge them better than others. This pattern is also found in other cultural systems. Finally, we observed that teachers lack the ability to correctly identify positively and negatively biased students.In conclusion, the results of this research program should encourage education professionals to better understand the complexity of student self-evaluations of competence, and how they impact the learning process and teacher judgment. It also opens up avenues of reflection for better handling of students with unrealistic negative self-perceptions.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".