Bibliographic record
Abstract
The article summarizes my own experience of conducting reviews for exams in French language courses. Through more than ten years of teaching French language and linguistics, I have developed pre-exam review sessions based on game models to help my students repeat term lessons in an engaging and memorable atmosphere. The article is intended as a practical guide for creating and organising such a review game. It is constituted of seven parts with subtitles, which should make it easy to navigate. After a short introduction describing challenges encountered in end-of-year reviews, the reader will find a general description of the game in part 2 and examples of one complete round of it in part 4, while the interceding part 3 will provide practical tips on support materials, which themselves make up part 5. These supports deal with ways of formulating questions and displaying answers using animations in PowerPoint presentations. The final section, part 6, will offer advice on using a course website and classroom aids to increase student attendance and encourage more effective classroom participation. The brief conclusion enumerates the beneficial effects of the game, underlines the value of a well-prepared PowerPoint presentation, and gives examples of students’ positive feedback on this format of material review.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".