When Language Instruction and Second Language Acquisition Meet: An Interdisciplinary Approach
Bibliographic record
Abstract
There exist misconceptions regarding the objectives of language instruction and those of Second Language Acquisition (SLA). Many language instructors teach the grammar rules of the target language expecting students to internalize knowledge and produce immediately. But research on SLA shows a different picture; there are different stages of language acquisition that should not be changed deliberately through instruction of some grammar rules before or instead of other ones (cf. Van Patten, 2010). It is not the case that all language instructors are aware of or familiar with linguistic research on SLA. Another major concern is that standardization of models on teaching meets the objectives of the institution, but not always the needs and demands of each learner. Each classroom can count on a diverse audience, different types of learners and therefore diverse expectations. This workshop is intended for language instructors, and could be adapted for instructors of other disciplines since the main principles acknowledged here apply in other learning scenarios (i.e. boost the amount of practice of the most complex issues, recognize the diverse audience). This workshop combines the situations described above – considering what research shows us about SLA and a diverse audience in a language classroom (e.g. Foreign language learners vs. Heritage speaker learners). It adapts a practice activity integrating SLA research, and two instruction approaches: one for a diverse audience, and the other focused on form.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.030 | 0.031 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".