Enhancing Learning Achievement in Sentence Structure among Grade 8 Students using the GPAS 5 STEP Learning Management Model
Bibliographic record
Abstract
This study aimed to investigate the effectiveness of a learning management plan designed using the GPAS 5 Step model in enhancing the learning achievement of 31 Thai language learners in the area of sentence structure. Participants were selected through cluster sampling from a population of Thai language learners in a public school. The research instruments included the learning management plan, a learning achievement test, and a questionnaire to assess participant satisfaction with the learning experience. Data were collected using a one-group pretest-posttest design. Descriptive statistics, paired samples t-test, and an effectiveness index with a criterion of 80/80 were employed for data analysis. The results indicated that the learning management plan designed using the GPAS 5 Step model had a significant positive impact on participants' learning achievement in sentence structure. Additionally, participants reported high levels of satisfaction with the learning experience, highlighting the effectiveness of the GPAS 5 Step model in creating an engaging and satisfying learning environment. These findings contribute to the growing body of research supporting the effectiveness of the GPAS 5 Step model and emphasize the importance of participant satisfaction in language learning contexts.
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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 | 0.000 | 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 | 0.001 |
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".