Creating and Developing a Student Worksheet for In-Tandem Activity
Bibliographic record
Abstract
Tandem activities entail communication between two partners of different cultures and mother tongues in order to use and improve their linguistic knowledge, to exchange information and to facilitate socio-cultural integration.In this respect, a set of educational materials has been created by the team of the «Tandem, bilinguisme et construction des savoirs disciplinaires: une approche du FLE/FOS en contact avec les langues de l'ECO » project.Nevertheless, all teaching material goes through successive stages, various approaches and can be improved.This study suggests an analysis of a student worksheet, Education, whose main objective is to know the educational system of the two cultures.This research mirrors two versions of the same worksheet (conventionally marked V1 and V2) in order to present the manner in which this teaching material developed.We used a comparative approach.Our points of interest were: the linguistic level of the target group, general/specific objectives, the set place and time, and the structure of the activities.The study's conclusions entailed two aspects: methodological and cultural.Thus, as far as the material's future users, we believe that it is preferable that all the students be at the threshold level of the foreign language.The activities suggested by the worksheet must make use of the participant's linguistic resources in an active process.To know the tandem partner's educational system, in order to be integrated in it, it is necessary for the general objectives to be aimed at identifying cultural similarities and differences.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".