Determining the Evaluative Criteria of an Argumentative Writing Scale
Bibliographic record
Abstract
Even though many writing scales have been developed, instructors, educational administrators or researchers may have to develop new scales to fit their specific testing situation. In so-doing, one of the initial steps to be taken is to determine the evaluative criteria on which the scale is supposed to be based. As a part of a project that was proposed to develop a genre-based writing scale, a survey was carried out to investigate Malaysian lecturers’ views on the evaluative criteria to be considered in evaluating argumentative essays. For this purpose, a group of English as a Second Language (ESL) lecturers (n=88) were administered a questionnaire. The subscales of organization, content and language skills were recommended by factor analysis. A fourth subscale, task fulfillment, was added as a result of the qualitative analysis of the data. The findings can be useful for language teaching or assessing purposes. ra ,c ??o?rvation with the causal-comparative study between treatment group and control group. The result shows that a majority of students with TOA improved their oral English learning more than those with Traditional Teaching Method (TTM). It also finds that TOA is able to motivate the students to speak in class and a certain percentage of students can overcome their psychological problems in expressing ideas. The findings are that TOA affects students’ achievement in a favorable way which provides some theoretical, methodological and practical implications.
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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 | 0.033 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".