EFL Learners’ Grammatical Awareness through Accumulating Formulaic Sequences of Morphological Structure (-ing)
Bibliographic record
Abstract
Even young EFL learners who have not yet learned L2 grammar will notice language patterns if, when retrieving exemplars (“item-based patterns”), they succeed in making form-meaning connections (FMCs). Item-based patterns, termed formulaic sequences (FS), serve as a basis for creative constructions. Although learners are implicitly sensitive to the frequency of the occurrence of constructions, item-based patterns are largely overlooked and are not retained. Because of the gap between elementary and secondary schools, students believe there is a difference between item-based patterns and the process of learning grammar. This phenomenon extends to EFL. The study investigated the extent to which Japanese students who had completed 150 hours of English lessons (age 13, N = 95) noticed linguistic patterns when using a grammatical judgment test. Targeting the present progressive form -ing as FS, the teacher used three treatments: (a) recall of chunking, (b) structured input and dictogloss, and (c) a ten-minute inductive explication of grammar in L1. The results revealed significant differences between pre- and post-tests for awkwardness of word order (31% < 59%) and omission of morphemes: -ing (61% < 74%). Overall, students who had received the instructional medium exhibited grammatical sensitivity to FS.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".