Évaluer sans noter, une inspiration pour les cégeps ? Revue systématique des écrits sur les pratiques évaluatives qui réduisent ou éliminent la notation
Bibliographic record
Abstract
This article explores assessment practices that reduce or eliminate grading (PRÉN) in the Quebec college context, and their impact on students' mental health and motivation. These practices, in opposition to the use of grading, aim to support learning without the need for grades, which are often perceived as a source of stress and unfairness. PRÉNs encourages qualitative feedback rather than the simple assignment of grades. To describe PRÉNs and explore their impact, a systematic review of the literature is presented in this article. The result is 16 papers looking at practices that fit within the terminology of PRÉN. Some of the papers reviewed concluded that these practices would have a positive impact on students' intrinsic and autonomous motivation. However, the results concerning mental health are more nuanced, with some people experiencing stress linked to the absence of traditional reference marks such as grades. The study concludes that further research is needed to better understand the impact of these practices in the Quebec context, and their potential for the success and perseverance of college students.
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 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 | 0.098 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".