Teachers’ Classroom Roles under CLIL: A Ten-Year Systematic Review
Bibliographic record
Abstract
Content and Language Integrated Learning (CLIL) has garnered global popularity in foreign language education, particularly in teaching English. While there is a plethora of research on the general perceptions of CLIL teachers, the examination of their classroom roles remains largely unexplored. This article reviewed 31 studies from 2014 to 2023 using the PRISMA framework to explore: (1) CLIL teachers’ perceptions on their classroom roles in existing studies; (2) factors influencing CLIL teachers’ classroom roles; (3) ideal roles for CLIL teachers according to the literature; (4) strategies to help improve CLIL teachers’ roles in the classroom. Findings reveal that the majority of CLIL teachers perceived the integration of language and content teaching as central to their classroom role, although some placed greater emphasis on language instruction than content teaching, and vice versa. Internal factors like teachers’ understanding of CLIL and external factors such as students’ level of proficiency in the target language influence teachers’ roles. CLIL teachers should be also tasked with cultivating students’ cognitive skills and contemplating the utilization of the first language when deemed necessary for the success of CLIL. Several strategies were identified through the review to enhance the roles of CLIL teachers, including training, professional development, and support from educational authorities. Future empirical research is needed to validate the discussed strategies for the development of CLIL teachers and to focus on optimizing the balance between the use of target languages, such as English, and L1 in CLIL-based classrooms to enhance outcomes in foreign language education.
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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".