A Mixed-Method Examination of Adopting Focus-on-Form TBLT for Children’s English Vocabulary Learning
Bibliographic record
Abstract
This study investigated the influence of focus-on-form task-based language teaching (TBLT) on Taiwanese children’s English vocabulary acquisition and retention. Focus-on-form TBLT here refers to instruction in which the teacher and students interact, negotiate, and respond to each other on the subject of a second language (L2) vocabulary. The participants (N = 71) were all enrolled in the third grade of a central Taiwan elementary school. The experimental group (N = 42) received TBLT lessons with a focus on form. In contrast, the control group (N = 29) received more conventional lessons based on the presentation-practice-production (PPP) model. Quantitative data were collected from three vocabulary tests. Qualitative data were gleaned from the teacher/researcher’s journal logs. Although the statistical comparisons showed no significant differences between the two groups in the three VKS tests, the qualitative data suggest that students in the two groups responded differently in terms of their in-class interaction and personal involvement. It seems that interaction and output production induced in the experimental group possibly facilitated the comprehension and acquisition of L2 vocabulary. The study also provides pedagogical implications for implementing TBLT with a focus on form to increase the retention of L2 vocabulary.
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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 | 0.034 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".