Trait Based Assessment on Teaching Writing Skill for EFL Learners
Bibliographic record
Abstract
This study was conducted in order to investigate the effectiveness of trait based assessment on teaching writing skill for EFL learners. Designed as pre-experimental study with one group pretest and posttest design, it examined 20 students of the second semester of English Department of Hamzanwadi University in the academic year 2016/ 2017 as the samples. Purposive sampling technique was used in determining the samples. Writing test and analytical scoring rubric were the instruments used to collect the data. Then the data were analyzed by using descriptive statistics and paired sample t-test to test the hypothesis. The result of descriptive statistics analysis revealed that trait based assessment is effective on teaching writing skill for EFL learners since the mean score of posttest 60 was higher than mean score of pretest 28.20. While for hypothesis testing by using paired sample t-test at significance (2-tailed) value level was .000, it was lower than .05. Therefore, it means that the hypothesis of this study was accepted. In other word, trait based assessment was significantly effective in improving students’ writing skill.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".