Application of a CALL System in the Acquisition of Adverbs in English
Bibliographic record
Abstract
In the paper, we examine whether and the extent to which CALL grammar instruction contributes to improving learners' performance and confidence in positioning adverbs in an English sentence. Over a two-week period two groups of ESL learners were exposed to six hours of grammar instruction. One group had teacher-fronted instruction while the other was exposed to CALL software. Both groups completed identical tasks in terms of format, instruction, task features, content and feedback. The groups were given a pretest, an immediate posttest, and a delayed posttest. Results showed a significant improvement on the intuition task and a significant confidence improvement on both intuition and production tasks for the computer group. The in-class and the control group showed no significant gains. It is hypothesized that frequency of exposure and practice accounted for the difference between the in-class and the computer group. It is also recognized that students' control of learning, availability of immediate feedback, and non-existence of negative psychological effect that can follow face-to-face negative feedback also contributed to the difference that was found.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".