First and second language knowledge in the language classroom
Bibliographic record
Abstract
This feasibility study investigated how language instruction can be designed to help learners build on first language (L1) knowledge in acquiring a new language. It seems likely that learners will benefit from activities that draw their attention to features of their L1, but attempts to bridge the first and second language (L2) curricula often break down because the teachers typically work in isolation and are uncertain how to proceed. We attempted to address these problems by designing a series of cross-linguistic awareness (CLA) activities to be implemented on a trial basis with 48 young francophone learners of English (age 9—10 years) at a school in Montreal, Quebec. We observed language instruction in their French (L1) classes and identified features and themes that lent themselves to reinvestment in their English (L2) classes. Then 11 CLA teaching packages were developed and piloted with in an intensive year-long English as a second language (ESL) program. Classroom observations, interviews with both L1 and L2 teachers, and learner journal responses indicated that the activities were well received and that CLA instruction can usefully address a wide variety of linguistic features. Problems highlighted by the study are discussed; we also outline new research that will explore whether this promising experimental pedagogy leads to distinct language learning benefits.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".