Research Timeline: Form-focused instruction and second language acquisition
Bibliographic record
Abstract
This article provides a timeline of research on form-focused instruction (FFI). Over the past 40 years, research on the role of instruction has undergone many changes. Much of the early research concentrated on determining whether formal instruction makes any difference in the development of learner language. This question was motivated in part by a theoretical discussion in the field of cognitive psychology over the role of explicit versus implicit learning, on the one hand, and a debate in the field of second language acquisition (SLA) over the role of naturalistic exposure versus formal instruction, on the other. In the early 1980s, for example, based on the notion that the processes involved in second language (L2) learning are similar to those in first language (L1) learning, Krashen (e.g., Krashen 1981, 1982, 1985) made a distinction between learning and acquisition and claimed that an L2 should be acquired through natural exposure not learned through formal instruction. Thus, he claimed that FFI has little beneficial effect on language acquisition. This position, which has also been known as a ‘zero position’ on instruction, was also taken by a number of other researchers who argued that L1 and L2 learning follow similar processes and that what L2 learners need in order to acquire a second language is naturalistic exposure to meaning-focused communication rather than formal instruction (Dulay & Burt 1974; Felix 1981; Prabhu 1987; Schwartz 1993; Zobl 1995).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".