Identifiability and accessibility in learning definite article usages: A quasi-experimental study with Japanese learners of English
Bibliographic record
Abstract
The present study investigated the effects of instruction on the use of the definite article the by Japanese learners of English by implementing two instructional treatments that varied in the extent to which they emphasized identifiability and accessibility. One instructional treatment, referred to as the traditional (TR) treatment, emphasized the linguistic/semantic notion of identifiability in which the serves to identify the referent. The other instructional treatment, operationalized as a mental space (MS) treatment, emphasized the cognitive notion of accessibility whereby the serves to mark an access path to the referent. The purpose of the comparison was to assess which types of metalinguistic information might be most effective for helping L2 learners of English to understand specific definite article usages. Three computer-assisted language learning (CALL) lessons averaging from 1.5 to 2 hours each were given individually to 83 Japanese learners of English, 42 in the TR group and 41 in the MS group. Counterbalanced versions of an article test were administered as a pre-test (Time 1), an immediate post-test (Time 2), and a delayed post-test (Time 3). Both groups exhibited significant increases at Time 2, which were maintained at Time 3, while the between-group comparisons showed that the MS group significantly outperformed the TR group at both Times 2 and 3. The different treatments had differential effects depending on the article types, with the MS group performing especially well on the most difficult conceptual usages.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".