The Development of English Writing Skills Through Techniques of Sentence Skeleton and Signpost Word Analysis for English Major Students
Bibliographic record
Abstract
The purposes of this research were to develop English writing skills through techniques of sentence skeleton and signpost word analysis for English major students, and to compare the writing skills before and after the study. The sample consisted of 43 English major juniors at Faculty of Education, Ubon Ratchathani Rajabhat University, enrolling in the course of teaching and learning English I, the second semester, academic year 2017, gained by cluster sampling. The research instruments were a performance test of writing skills, and writing drills. The data analyzed by employing percentage, mean, standard deviation, and t-test. After the study, the students had better writing skills with 40.63 percent of average score than those with 14.80 percent before the study, the individual average score was at a weak level with 38.90 percent, while the small group’s was at a fair level with 69.78 percent, the individual writing skills were significantly higher than those before the study at the .01 level, and the small group writing skills were significantly higher than those of the individual’s at the .01 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 | 0.002 | 0.006 |
| 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 | 0.000 | 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".