The Development of a Literacy Curriculum Using Activity-Based Learning, Digital Curriculum and Spatial Identity to Enhance Literacy Skills of Elementary Students
Bibliographic record
Abstract
This study uses a research and development model (R&D) that aims to develop a literacy curriculum using activity-based learning, digital curriculum and spatial identity to enhance literacy skills of elementary students under the office of education, Chiang Mai municipality. The target groups include 235 elementary students, 12 elementary teachers and 34 Thai language teaching pre-service teachers. Data were collected in the academic year 2022-2023. The main tools used include a literacy curriculum, a literacy skills assessment test for participating students, a competency assessment form for designing learning activities of Thai language teaching pre-service teachers, a teaching management competency assessment form for teachers. For the data analysis, mean values, standard deviation, and T-test dependent are used. The research findings reveal that: Firstly, the literacy curriculum is composed of the following elements: 1) principles, 2) objectives, 3) activity organization in five stages including (1) the text comprehension stage, (2) vocabulary expansion stage, (3) profound sentence comprehension stage, (4) specialized reading proficiency stage, and (5) effective written communication stage, and 4) measurement and evaluation. Secondly, the outcomes of the use of the innovative literacy curriculum show that (1) the students exhibited significantly higher literacy skills after than before studying at a statistical significance level of 0.05, (2) the Thai language teaching pre-service teachers demonstrated a high level of competency for designing learning activities, and (3) the teachers showed a high level of learning management competency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".