Immersion teachers’ perspective on grammar instruction in language immersion in Finland
Bibliographic record
Abstract
Abstract Previous studies have shown that grammar is a central source of difficulty for immersion students and that their teachers often lack knowledge of how to integrate linguistic aims with content teaching. This descriptive, enquiry-based study reports on Finnish immersion teachers’ ( n = 54) perspectives on grammar instruction in language immersion in Finland — a theme under-researched in Finland thus far. Our online questionnaire consists of 18 questions comprising three sections: the informants’ professional background, their views on the role of grammar in immersion and their views on correcting grammatical inaccuracies. We analyse our data using quantitative and statistical methods (Pearson’s χ² as a statistical test). Our findings show that most of our informants are experienced immersion teachers teaching languages to 13–15-year-old immersion students. They do not prioritise grammatical accuracy when planning their instruction, although they consider it an important aspect of second language proficiency. They also discuss grammatical accuracy to a greater extent with their students than with their colleagues and are more tolerant of inaccuracies in spoken output. Implications are discussed regarding the necessity of improving grammar instruction in immersion and immersion students’ grammatical accuracy.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".