The use of digital technologies in teaching French
Bibliographic record
Abstract
The article is devoted to analyzing the potential of digital technologies in teaching French in the context of modern educational digitalization. It traces three main dimensions of digital technologies in language education: as a pedagogical tool, as a learning resource, and as the content of instruction. Based on a comparative analysis, the paper presents the experience of implementing digital technologies in teaching French in Canada and Belgium. In the Canadian context, key educational initiatives and multimedia products are identified as providing instrumental and communicative support for French language learning. The Belgian model is considered a systemic example of integrating digital components at the cross-curricular level. Particular attention is paid to the functioning of the E-classe educational platform, which combines digital resources, didactic materials, and learning scenarios aimed at developing students’ linguistic, social, and digital competences. The article emphasizes that the effectiveness of digital technologies in teaching French depends on the quality of their pedagogical integration, the teacher’s readiness for innovation, and the support of the educational environment. Digital tools create conditions for personalized learning, increased motivation, gamification of the educational process, and the development of learner autonomy. Promising directions for further research include studying the impact of digital platforms on student motivation, the role of emotional intelligence and gamification in online learning, and the use of artificial intelligence for the individualization of language education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".