Integrating content and language in specialized language teaching and learning with the help of ICT
Bibliographic record
Abstract
In recent times globalization has had a significant impact on content-teaching methodologies. Mobility in Europe is also a major issue that stimulated the implementation of such practice. David Marsh and Peeter Mehisto have pointed out that many governments have adopted some form of second-language-medium instruction. But reading in a second language per se does not make an example of language teaching, as the focus is primarily on content, not language. The instrumental use of a vehicular language – generally a second or foreign language to learners – does not imply analyzing and practicing the communicative structures of the vehicular language itself. Conversely, as Andy Kirkpatrick argued, all good language teaching needs to be based on content that engages the learner, but this is meant to provide useful contexts of use which will enhance the learning experience. Integration of content and language can only be achieved through the combination of professional expertise and linguistic competence provided by subject teachers and language teachers. Yet, although team teaching would seem to be the ideal solution, this is very difficult to achieve in practice for a number of reasons. Drawing on the successful experience of Canadian immersion programmes, where teachers are trained to teach French, and the subject through French, we believe that a second-language medium of instruction should ideally use teachers trained in both language and content pedagogy. ICT plays a fundamental role in achieving this dual goal as the case study presented in this paper proves. By analyzing a scenario of content and language integrated learning based on the use of e-learning technologies, the paper points out the relevance of involving students in ICT-based activities which give them a combination of professional expertise and linguistic competence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".