Form‐focused instruction and learner investment in L2 communication
Bibliographic record
Abstract
ABSTRACT The purpose of this study is to examine the role of form‐focused instruction (FFI) in relation to learner investment in second language (L2) communication. Although positive effects of FFI have been reported, most of this research has been conducted from a cognitive–interactionist perspective. Little attention has been paid to the social factors of FFI, including learner investment—a desire to learn a second/foreign language taking into consideration learners' socially constructed identities (Norton Peirce, 1995). Drawing on second language socialization theory (Duff, 2007) and using discursive practices (Young, 2009) as an analytic framework, this study examines how FFI influences learner investment in L2 communication in the classroom setting. Twenty‐four high school students in Japan participated in a study, where two Japanese teachers of English team‐taught four 50‐minute lessons. Each lesson contained a 15‐minute exclusively meaning‐focused activity and a 15‐minute form‐focused activity that included attention to both form and meaning. All students completed both types of activities. Data were collected through classroom observations, video‐recorded classroom interactions, stimulated recalls, interviews, questionnaires, and diaries, all of which were analyzed quantitatively and qualitatively. The results suggest that FFI created social contexts for learners to establish their identities as L2 learners, leading to greater investment in L2 communication.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".