Language-Driven CLIL: Developing Written Production at the Secondary School Level
Bibliographic record
Abstract
This research study analyzes the effect the implementation of language-driven CLIL has on senior learners from Manuel J. Calle High School in Cuenca, Ecuador in relation to the development of written production in terms of Syntax, Content, Communicative Achievement, Organization, and Language compared to a non-language-driven CLIL classroom. There were 40 participants in the experimental group, and 38 participants in the control group. Learners from the experimental group received a condensed 35-hour intervention using CLIL. This study features an exploratory, mixed-method, and quasi-experimental research design. To collect qualitative data, an open-ended questionnaire was administered to explore the subjects learners preferred to study in a language-driven CLIL classroom. To collect quantitative data, a Pre and Post-Test based on the writing section of Cambridge Objective Primary English Test was administered. The data was analyzed through the Independent T-Test and Paired-T-Test to determine if there was a statistically significant difference present between the language-driven CLIL classroom and the non-language-driven CLIL classroom. The data was calculated through the Statistical Package for Social Sciences (SPSS). A survey was administered to collect data on learners’ perceptions about CLIL and then analyzed statistically. Results indicated that learners preferred to study History, Biology, and Spanish Language and Literature. Results also demonstrated that the experimental group also demonstrated improvement in all the examined parameters when compared to the control group. However, when results from both groups are compared, there is only a statistical improvement in Organization and Syntax.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".