Promoting Writing Competence and Positive Attitudes among College Students in a CLIL English Course
Bibliographic record
Abstract
The study targeted a group of 27 students at a college in Kuwait who were enrolled in a Content and Language Integrated Learning (CLIL) English course during the academic year 2015-2016. The purpose of the study was to monitor students’ assessments of their writing competence in English and to measure their attitudes toward the CLIL course. Data collection utilized a total of five focus-group interviews with the students which were recorded and transcribed, and a category system was generated to describe the commonalities in the participants’ responses. Additionally, an online survey using Google Forms was based on the categories delineated from the interview data. The Microsoft Excel program was used for counting the means, standard deviations, and percentages for each of the survey items. The results of the study indicated that the majority of the students (80%) thought that the CLIL course enhanced their writing competence both within and beyond the sentence level. Writing skills within the sentence level included the accurate use of punctuation marks and capitalization rules. Writing skills beyond the sentence level included paragraph organization, use of proper transition words, and writing cause-and-effect paragraphs. Approximately 20% of the students did not think CLIL improved their writing competence beyond the sentence level. Furthermore, the students displayed highly positive attitudes toward all aspects of the CLIL course. Implications were drawn for specialized teacher training to cope with the demands of CLIL courses, and longitudinal studies to track students’ development of writing competence over time.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".