The Current Situation and Needs for Developing Curriculum to Promote Reading Habits and Reading Literacy Using Isan Local Literature for Primary School Learners
Bibliographic record
Abstract
This research examines the current situations and needs for curriculum development to promote reading habits and reading literacy using Isan local literature for primary school learners. The research process starts with reviewing relevant literature and previous research regarding curriculum development principles, promoting reading habits and reading literacy, and the use of Isan local literature in primary learners. The next process is analyzing the current situation and needs for curriculum development by collecting data from stakeholders through questionnaires and focus group discussions. The research instruments included an analysis record form, a questionnaire on current conditions and needs, and an unstructured interview. The data analysis was conducted using frequency, percentage, mean, and standard deviation. The findings of this study reveal a significant gap between the current teaching practices and the high level of demand for developing a curriculum that promotes reading habits and reading literacy using Isan local literature. Current practices among primary school Thai language teachers were rated at a low level across all components, while the needs for curriculum development were consistently rated at the highest level. In conclusion, the findings validate the need for a curriculum that promotes reading habits and reading literacy through local literature among primary school students.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".