Exploring content and language co-construction in CLIL with semantic waves
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In content and language integrated learning (CLIL) classrooms, it is assumed that non-language content subjects provide more authentic communicative contexts for students to learn a foreign/second/additional language (L2). However, learning abstract concepts and academic language in an L2 simultaneously is also challenging for CLIL students. It is thus important for CLIL teachers to unpack and repack both abstract concepts and academic discourse for the students. ‘Semantic waves’, which model classroom practices of both unpacking and repacking, is arguably a key to understanding cumulative knowledge-building. Applying the concepts of semantic profiles and semantic waves, this paper analyses the classroom discourse of two CLIL science lessons in Hong Kong. In one lesson, the semantic profile mainly consists of downward shifts. The teacher adopted various useful strategies to unpack science concepts, especially with multimodalities, everyday L2 and students’ L1 resources. Yet, there was limited repacking. In contrast, some repacking was observed in another lesson, where the teacher provided explicit instruction on academic language and guided students through academic writing tasks. A semantic wave can thus be observed there. These findings on strategies for unpacking and repacking provide significant insights into knowledge building in CLIL contexts, and may hence illuminate CLIL pedagogical practices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it