The Effects of Task-Related Focus-on-Forms Instruction on Vocabulary Development in Thai EFL Primary School Students
Bibliographic record
Abstract
This quasi-experimental study investigated the effect of task-related focus-on-forms (FonFs) (i.e., written form and word parts) instructions on EFL vocabulary development in Thai primary school students. The participants were 72 sixth-grade Thai EFL students and were divided into two groups: the written form group participants (n = 37) who received the written instruction and the word parts group participants (n = 35) who received the word parts instruction. In the written form group, the teacher taught the one hundred and four target words by giving their definitions (in the form of target language explanations), followed by the participants’ spelling and example sentences; hence the focus was on the written form. The word parts group did the same as the written form group. Besides, they focused on word parts as another aspect of word form. One vocabulary size test was conducted to measure the number of participants' vocabulary words. Four tests were used to measure receptive and productive knowledge of vocabulary development, and two questionnaires were employed to explore the participants’ perceptions. Descriptive and inferential statistics were employed to analyze the data of the study. These findings indicate the significant effect of task-related focus-on-forms (FonFs) on vocabulary development among Thai primary school participants. In addition, the perception questionnaire data analysis also revealed that task-related FonFs in written form and word parts groups helped learn vocabulary. Pedagogical implications and suggestions for further studies are presented.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".