Investigating the Application of Communicative Language Teaching Principles in Primary-Education: A Comparison of CLIL and FL Classrooms
Bibliographic record
Abstract
It is widely accepted that the learning of a new language, among other advantages, promotes respect and interest of the students towards other cultures and languages. The question is how learning languages can be promoted in educational settings. The aim of the present study is to explore the principles of communicative language teaching in primary-education CLIL and FL classrooms. More specifically, in this paper we address to what extent collaborative work, attention to language and content and corrective feedback are observed during teacher-student and peer interaction in these educational settings. Following an action research approach, ten Spanish and ten Maths sessions were observed and recorded. Furthermore, whole group interaction and peer interaction were analysed in relation to the participants’ attention to language and content. Results from the study show that communicative language teaching is the approach followed in CLIL and FL sessions, tasks being the organizing units. However, differences are observed in relation to attention to language and use of correction strategies. Our findings suggest the need to use strategies to draw attention to language and content in CLIL settings, and the importance of using a more even range of correction strategies both in CLIL and FL classrooms.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".