The Potentials and Limitations of Applying Content and Language Integrated Learning (CLIL) Approach to English Teaching for Medical Students
Bibliographic record
Abstract
The Faculty of Medicine, the University of Mataram, Indonesia, renewed its curriculum to teach English to aide Indonesian university graduates to enter the competitive international job markets. Adopting this new curriculum will affect the provision of teaching and learning activities. This paper attempts to justify whether the use of Content and Language Integrated Learning (CLIL) would fit within the current English curriculum. A comprehensive review of a literature was conducted to identify barriers and facilitators for the implementation of CLIL in higher education settings. Results of the literature review were then used to evaluate the potential strengths and limitations of the recently renewed curriculum when implemented at the Faculty of Medicine. Availing of curriculum and learning materials has the potential to sustain CLIL implementation in the Faculty of Medicine. However, factors that may impinge the successful implementation include: lecturers’ language, content pedagogical competences and the need to employ differentiated instructional modules. An ongoing professional development for lecturers prior to curriculum implementation could address these limitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| 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 | 0.001 |
| 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".