Curriculum genres and task structure as frameworks to analyse teachers’ use of L1 in CBI classrooms
Bibliographic record
Abstract
Content-based education programmes, in which a second/foreign language (L2) is used as the medium of instruction when teaching non-language content subjects, aim at both content and L2 learning. With such dual goal in mind, and with the rapid expansion of the programmes to contexts where students might have only basic L2 proficiency, there have been consistent calls for reconsidering the roles of first language (L1) in the teaching and learning process. The functions of L1 in content-based classrooms have been well documented, but it is necessary to have a more systematic approach to planning and using L1. This paper seeks to address this gap by applying the notions of ‘curriculum genres’ and ‘task structure’ when analysing patterns of teachers’ use of L1 and L2 in a collection of content subject lessons observed in English-medium secondary schools in Hong Kong. With illustrative episodes presented in this paper, we would argue that ‘curriculum genres’ and ‘task structure’ can serve as useful tools for both researchers and teachers to analyse and critically reflect on patterns of pedagogic practices and language use in content-based classrooms. These will have significant implications for future research on using L1 and illuminate effective pedagogy in content-based 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".