Developing a Process to Promote Reading Comprehension of Students in the Thai Language Department, Faculty of Education, Chiang Rai Rajabhat University
Bibliographic record
Abstract
This research was aimed at 1) creating a process to promote reading comprehension skills of students in the Thai language department, Faculty of Education, Chiang Rai Rajabhat University, 2) studying the process effectiveness, and 3) evaluating students' satisfaction with the process. The sample group consisted of 25 students in the Thai language department, Faculty of Education, Chiang Rai Rajabhat University, year 1 of the academic year 2020, selected by a Purposive Sampling method using the reading scores of the entrance examination. The research tools included 1) reading comprehension assessment forms prior and after using the process, 2) the process to promote reading comprehension, 3) reading comprehension practice form, 4) student satisfaction assessment form for the process. The research results presented the the process of enhancing reading comprehension ability, which consisted of 5 development stages, namely the evaluation stage, the stage of problem recognition and knowledge creation, the collaborative training stage for comprehension, the individual comprehension training stage and the reading comprehension evaluation stage. It was found that (1) the process had and efficiency of 84.34 /87.49, (2) students had higher reading comprehension ability, and (3) the student satisfaction with the process was in a high level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".