The Use of 5 Step Technique (QSCCS) in Developing Grade 9 Students’ Summary Writing Skills
Bibliographic record
Abstract
The purposes of the study were to investigate the effectiveness of the QSCCS technique on the development of grade 9 students’ summary writing and 2) to study the students’ satisfaction with learning to write a summary with the QSCCS technique. The study was conducted in a quasi-experimental design using a single group of participants. The participants were 44 students in the Thai context. They were chosen using the purposive random sampling technique. The instruments were a learning management plan designed using the QSCCS technique, a pre-post-test, and a questionnaire. The students’ scores before and after the treatment were compared using a paired-sample test. The effectiveness of the learning management was analyzed considering the students’ performances during the learning process (E1) and the students’ post-test scores (E2). Mean scores and standard deviations were also used to analyze students’ questionnaire answers. The result of the study indicates the benefits of the QSCCS technique in developing grade 9 students’ summary writing skills. In addition, it was discovered that the participants were satisfied with learning to write a summary with the QSCCS technique. The results of the study provide an alternative instructional method for teaching summary writing.
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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 | 0.010 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".