Putting Rubrics to the Test: The Effect of Rubric-Referenced Peer Assessment on EFL Learners’ Evaluation of Speaking
Bibliographic record
Abstract
This study attempted to shed some light on the effect of rubric-referenced peer assessment on EFL learners‟ speaking skill and on the cultivating the learners‟ awareness of having appropriate criteria for speaking, as one of the four major skills. This study explored the effect of rubrics on peer assessment of 18 Iranian EFL learners. First, learners assessed their classmates speaking performance based on their own presuppositions and assumptions. Subsequently, a spoken language rubric was introduced to them. They re-assessed their classmates‟ performances through using this rubric. Quantitative data analysis revealed significant difference between the results. In-depth qualitative analyses of comments and marginal notes written down by learners revealed that peers heed not only to institutional components specified in scoring scales but also to other irrelevant criteria such as the result of the speech act performed. The study has suggested that the use of a combination of peer assessment and rubric-referenced assessment encourages students to become more rationally responsible and reflective and has shown positive formative effects on student achievement and attitudes. The article concludes with some guidelines for practitioners. The findings of this study also provide insight into the effective assessment and recommendations for future research and practice are made.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".