IS REDOING FIRST YEAR OF HIGH-SCHOOL USEFULL? / EST-CE UTILE DE REDOUBLER SA PREMIÈRE ANNÉE DU SECONDAIRE (ÉTUDE DE CAS)?
Bibliographic record
Abstract
At the end of a school year, emotion, self-esteem, knowledge are considered by a teacher to decide whether a student is promoted or not. The aim of the study was to seek the success of students 12- to 14-year-olds who had failed a given course in the first year of high school and were still registered for a higher course in the second year of high school. Students were evaluated by their regular teachers over the years, and report cards were analyzed. Failing students were students with mean marks below 60 p.100 in a topic. No help was given during summertime. Still, students were more likely to achieve success the following year at that higher level, regardless of the topics. Up to 71 p.100 had recovered from their misunderstanding in the second semester of high school the next year. If we consider the less weak students, thus those who had failed year 1 with marks below 60 p.100 but above 49 p.100, nearly 80% of the students had recovered from their misunderstanding in the second semester. This study supports the idea that the relationship to knowledge should change in schools. We believe that intrinsic motivation may need enough time to occur at such an age. We also believe that the strong extrinsic motivation given by the teachers and the school institution of the school under scrutiny is recommended. Article visualizations:
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".