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Record W4383495740

Le biais d'auto-évaluation de compétence scolaire Risque ou opportunité pour la réussite des élèves ?

2021· preprint· en· W4383495740 on OpenAlexaffabout
Pascal Pansu, Anne‐Laure de Place, Thérèse Bouffard, Fabienne Blaise, Natacha Boissicat, Hélène Insel, Ludivine Jamain, Nadia Leroy, Fanny Verkampt, Laurent Lima, Dominique Pigière, Jérémy Pouille, Jacques Py, Claudine Schmidt-Lainé, Carole Vezeau

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2021
Typepreprint
Languageen
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.136
GPT teacher head0.368
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes2
Has abstractyes

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