Features of using information technologies in teaching French as a native and foreign language in schools in France, Canada, Morocco, and Russia
Bibliographic record
Abstract
Problem statement. France, Canada, Morocco and Russia have different unique experiences of using information technologies (IT) in teaching French to school students. At the same time, the digital educational resources used in these countries accommodate diverse approaches and methodologies for teaching. Against this backdrop, a key problem is the near absence of comparative analysis examining how modern information technologies are employed in French language education across different countries. It is advisable to identify best practices and, based on these findings, develop recommendations for optimizing relevant teaching approaches in Russian schools. Methodology . The study employs a comparative analysis of existing approaches to teaching French and using IT across different countries, development of corresponding recommendations for Russian schools and experimentally verifying the effectiveness of the proposed approaches and digital resources. Results . The digital educational resources have been rated in compliance with the defined evaluation criteria. The comparative analysis results yielded recommendations for implementing IT in Russian schools based on international experience and the conducted pedagogical experiment confirmed the hypothesis that examining approaches and comparatively analyzing the specifics of using information technologies in teaching French as both a native and foreign language in Francophone countries (France, Canada, and Morocco) and taking into account the results of such an analysis when improving the methodological system of teaching French in Russia enhances the effectiveness of student preparation. Conclusion . The study established criteria for a comparative analysis of the use of IT in French language education across different countries, which made it possible to identify the specifics of their use in France, Canada, Morocco and Russia. Based on these results, recommendations have been developed to enhance the methodological framework for teaching French in Russian schools. It has been experimentally confirmed that the use of IT based on international experience enhances student learning outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".