Development of Reading Skills for Thai Grade 1 Students Through Smart Training Application Innovation
Bibliographic record
Abstract
This research aimed to create a smart training application innovation to develop Thai language reading skills for Thai grade 1 students, targeting an efficiency standard of 80/80. The study compared the reading abilities of Thai grade 1 students before and after using the application. These students were from the Network School of the Teacher Professional Experience Center, Faculty of Education, Loei Rajabhat University. Specifically, 13 students from Ban Na Si School were selected using purposive random sampling for the first semester of the academic year 2023. This school was intentionally selected due to its eagerness to implement this innovative approach. The study utilized two versions of the smart training application: one focused on words without tone marks and the other on consonant blend words. Four lesson plans were designed (two for each version), and a 20-question reading achievement test was administered. Through the E1/E2 value calculation and a t-test, it was deduced that the application surpassed the 80/80 efficiency standard, scoring 81.15/86.15. Furthermore, students exhibited a significant improvement in their reading skills after using the application, as evidenced by a higher post-study performance at a 0.05 significance level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".