Enhancing English Learning in Thailand: Insights from Thai Teachers on the New Say Hello Textbook Series
Bibliographic record
Abstract
Textbooks are crucial in language teaching and learning within an EFL context. Therefore, this study examined language components within English textbooks used for primary language education in Thailand. It also explored Thai primary school English teachers’ perspectives of the “New Say Hello Series 1–3” English language textbooks utilized in their teaching. Through semi-structured interviews, insights into their experiences with the textbooks were gathered from six purposively selected Thai EFL teachers aged thirty to fifty-five. The qualitative data were transcribed and coded into themes. The transcribed data were inter-coded by another well-trained English teacher. Participants also cross-checked these data to ensure the trustworthiness of the findings. The analysis findings underscored the language components of textbooks in supporting curriculum goals and engaging students in learning English with appealing topics. The “New Say Hello” series effectively enhanced language education quality in Thailand by being age-appropriate, developmentally suitable, and culturally inclusive. The findings also showed that Thai EFL teachers reported positive experiences with the “New Say Hello” series, highlighting its interactive resources, alignment with varied learning styles, and emphasis on communicative language teaching by following the guidelines and well-structured progression of English language skills. Integrating Thai and various cultures into the textbooks was particularly valued for promoting cultural awareness and global citizenship among students. This research offered insights into curriculum development and teaching practices, suggesting that well-designed textbooks were crucial for improving language education outcomes. It also suggested further research into implementing textbooks and their influence on language acquisition in Thailand.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".