Research agenda: Researching grammar teaching and learning in the second language classroom
Bibliographic record
Abstract
Abstract We propose five research tasks targeting grammar teaching and learning, focusing on extending previous research and exploring under-studied features and contexts. The first two tasks outline replications and extensions of seminal studies on pedagogical grammar, Toth (2008) and Samuda (2001), designed to advance our understanding of the teacher role in providing rich practice opportunities. Another task examines how features of peer interaction during oral communication might encourage attention to grammar among young second language (L2) classroom learners in school-based foreign language programs, a common yet under-studied context. A fourth task investigates the unique properties of spoken grammar across languages and effective approaches for its teaching and learning, and the fifth explores the (re)design and use of corpus-based tools to enhance accessibility and learner autonomy in data-driven grammar learning. Each task is designed to be feasible across a variety of classroom contexts and target languages. We highlight concrete implications for language pedagogy and include suggestions for capturing both learning outcomes and participants’ perspectives on their learning and teaching, using a range of quantitative and qualitative methodologies. We end with some thoughts on repetitive practice for learning certain features of grammar, and recommendations for collaborative research that would encourage greater replication of future studies.
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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 | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".