Developing Computer Literacy of Bilingual Students via CLIL Methodology
Bibliographic record
Abstract
The research intended to present the scientific basis of CLIL and validate CLIL technology's implementation in a Russian higher education institution to develop computer literacy of Russian bilingual learners experimentally. Consequently, we acquired two essential scientifically proven procedures for implementing science education and math for multilingual learners in the educational institutions of The Republic of Tatarstan, Russia. The initial one indicates that bilingual learners can adjust their mother language to the bilingual education program's Russian education environment. The second one includes the concept of language immersion: educating bilingual learners utilizing the Russian language. In compliance with the first strategy, we devised computer-assisted learning assistance of CLIL implementation to evolve bilingual Tatar learners' computer literacy in teaching Informatics. The bilingual resource is made up of seven modules. It has been developed with the aid of commonly available technologies as well as Web 2.0 services. The investigation was managed to demonstrate the efficiency of CLIL technology to develop computer literacy of Tatar learners. The Institute of Philology and Intercultural Communication of Kazan Federal University was adopted as a site for the experiment: 69 students of the first year of education participated in the pilot experiment. The experiment outcomes revealed that CLIL, as a technology to teach Informatics, improves the development of bilingual learners' computer literacy.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".