Using CLIL Approach to Improve English Language in a Colombian Higher Educational Institution
Bibliographic record
Abstract
In Colombia, the development of communicative skills of English as a foreign language in students of Higher Educational Institutions (HEIs) is considered as a priority so the professionals can face the challenges of a globalized world. This project aimed to determine the effectiveness of using CLIL approach through the academic subjects in first learning level students of a Nursing program. The research had a mixed quasi experimental design of a control group not equivalent with measurements before and after CLIL interventions, which consisted in accompanying and guidance to six teachers who instruct the seven subjects of the academic program in which the experimental group was. Along 17 applications designed from the 4C’s (Content, Cognition, Communication and Culture), and the methodology collaborative work, students showed a significant progress in using communicative and cognitive abilities according with the development performances. The used tests to evaluate students’ English level showed from the statistical data analysis, applying T-student test, that initiating the process the mean of the control group was significantly higher than the mean of the experimental group, and posteriorly to CLIL approach applications, a mean improvement of the experimental group was observed becoming statistically similar to the mean of the control group. The research results provide a pedagogical path to strengthen bilingualism processes and to contribute with graduate’s communicative competences in a foreign language.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".