Instructional Toolkit for Outcome-Based Instruction on English Grammar of Thai EFL Students in the Thonburi District in Bangkok
Bibliographic record
Abstract
The purposes of this research were to a) find out how well the new instructional toolkit works at improving students’ English grammar skills in verb tense structures, b) investigate the effects of Thai EFL students’ learning achievement in utilizing an instructional toolkit on the acquisition of English grammar, c) investigate Thai EFL undergraduate students’ knowledge retention after teaching using an instructional toolkit, d) study relationships among study levels, English proficiency levels, and learning achievement of Thai EFL students on the acquisition of English grammar before and after utilizing an instructional toolkit, and e) explore the Thai EFL students’ perceptions regarding the instructional toolkit in terms of design, retention development, and implementation. The sample was categorized into two groups: 100 primary students and 100 secondary students. All students were studying in public school in the Thonburi district in Bangkok, Thailand, using multilevel group research design. The instruments were a) an instructional toolkit, b) achievement tests, c) a questionnaire, and d) interviews. The data were collected before, during, and after conducting research. The data were analysed quantitatively, using SPSS to find out the frequency, mean (M), and standard deviation (SD) of the participants’ perceptions regarding use of the instructional toolkit, and qualitatively using content analysis. The research revealed that a vast majority (95%) of students and teachers acknowledged the toolkit’s efficiency and efficacy, with significant improvements noted post-intervention. Additionally, the toolkit effectively enhanced knowledge retention in verb tense structures across different educational levels, showing particular resonance with higher-grade students and those at intermediate proficiency. However, despite appreciation for its user-friendly design and retention-enhancing features, there was a clear recommendation for the inclusion of more diverse real-world examples in future iterations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".