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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".