Content and Language Integrated Learning (CLIL): present and future (as a Finnish innovation)
Bibliographic record
Abstract
The paper considers Content and Language Integrated Learning (CLIL) as one of the approaches to achieve the purpose of learning foreign languages, represented in the Common European Framework of Reference for Languages adopted by the Council of Europe in 2001. To acquire a language means not merely to obtain communication skills in one, two or even three languages, studied separately, but “to develop a linguistic repertoire in which all language skills are present,” as mentioned in the European Recommendations on Language Education. People who possess even little knowledge can achieve a certain level of communication proficiency using all their linguistic “tools”, experimenting with alternative forms of expression in different languages and dialects, using paralinguistic means (mimics, gestures, facial expressions, etc.) and radically simplifying their use of language [1; P. 19]. Researchers in Finland, whose success in the education system is recognized worldwide, are searching for methods and approaches to achieve this purpose of foreign language education. One of their attempts is Content and Language Integrated Learning (CLIL). The paper reveals: the history and the origins of CLIL. According to C. Nieminen it includes the method of immersion, created and widely used in Canada. This research also outlines the advantages and factors limiting the usage of CLIL, as well as the prospects for further implementation of this approach to the study of foreign languages in different countries. In Ukraine this training method has not yet become widely applied, only some cases of CLIL implementation take place in specialized schools and in higher education institutions at foreign language departments. Therefore, according to national scholars Ukraine focuses on improving the level of foreign language proficiency, profound research and implementation of the CLIL methodology in schools and higher education institutions all over the country.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".