Using Language Objectives to Integrate Language and Content Instruction: A Case History of Planning and Implementation Challenges
Bibliographic record
Abstract
This paper provides a case study of the use of language objectives within a newly developed English language teacher education programme in Hong Kong. The programme development team decided to adopt a content-based language teaching approach to support students' second language development through content learning. The teacher education programme (the content) prepares Chinese-speaking students to become secondary level English teachers. Because of students' level of English language proficiency upon entry, the programme must provide substantial support for students' English language development in addition to their learning of subject content knowledge and ELT pedagogy. There is also a government-stipulated proficiency level for all qualified English language teachers, which essentially becomes the language syllabus for the programme. The language objectives serve as a medium for mapping the language syllabus on to the content curriculum. There are reasons to believe that the approach adopted has been successful in supporting students' language and content learning. This paper describes the programme development and implementation process and the problems and constraints met. The paper also shares the lessons learnt from the curriculum development and implementation process.
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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 | 0.033 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".