Monitoring of Teachers’ Views on Both CLIL and the Development of Pluriliteracies: A Longitudinal Qualitative Study
Bibliographic record
Abstract
In Content and Language Integrated Learning (CLIL), the design of a language policy at school level is not only bound up with the number of languages used for learning and the number of hours devoted to teaching those languages, but also with the fact that language becomes specialised in relation to the subject, which impacts on the methodology used. These are the reasons for both language teachers and subject teachers to work together in design and implementation; and for the teachers’ use of a translanguaging-based approach to language learning (San Isidro, 2018). Previous research has dealt with teachers’ opinions (Calvo & San Isidro, 2012; Coonan, 2007; Infante et al., 2009; Pladevall-Ballester, 2015) on the difficulties of curriculum integration and its effects on both the different languages of instruction and the learning of content; or on the difficulties of language and content integration. However, methodology-oriented research on teachers’ views and work in specific contexts is direly needed so as to gain a deep insight into the methodological commonalities that make CLIL what it is. Our qualitative study is focused on a two-year monitoring of teachers’ (N=6) views on CLIL implementation in a rural multilingual setting in Galicia. The teachers were monitored by means of interviews held between 2012 and 2014. After being trained, they took part in a CLIL project based on curriculum integration with two different groups of students. The findings reported showed that 1) teachers’ initial views on CLIL implementation turned more positive over the two years; 2) teachers believed that CLIL provides a very good framework for the development of pluriliteracies; 3) their views regarding content learning in CLIL turned more neutral in the course of the two years; and 4) teachers stressed the need for methodology-oriented training.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".