Exploring Students’ Perspectives toward Clarity and Familiarity of Writing Scoring Rubrics: The Case of Saudi EFL Students
Bibliographic record
Abstract
The main aim of this research study is to investigate the clarity and familiarity of three scoring rubrics used in a Saudi university’s preparatory year program (PYP) for assessing students’ writing achievement in midterm and final exams. This exploration is important in providing some evidence for the quality of scoring rubrics. To achieve that purpose, a 13-item online questionnaire was used to collect Saudi EFL PYP students’ perspectives toward two quality criteria concerning rubrics; 1) clarity of the information included in the PYP rubrics, and (2) familiarity of the rubrics to students. The subjects were 281 Arabic-speaking male and female EFL Saudi students enrolled in three different academic levels in a Saudi university’s PYP. The results suggest that the quality of the PYP rubrics is insufficient and the criteria set for providing evidence for the rubric qualities were not met. The results show that students tend to have a mild agreement on the clarity of the PYP rubrics, whereas they show a clear disagreement on their familiarity with the rubrics and with why and how the rubrics are used. The study implicates that administrators and teachers need to carefully consider the clarity and familiarity of rubrics in order to justify the decisions made about students’ writing abilities. Rubrics that are unclear or unfamiliar can make students feel confused and frustrated, as they cannot get a clear sense of their writing scores, as well as their strengths and weaknesses.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".