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Record W1797460826

Putting Rubrics to the Test: The Effect of Rubric-Referenced Peer Assessment on EFL Learners’ Evaluation of Speaking

2013· article· en· W1797460826 on OpenAlexvenueno aff
Masoume Ahmadi, Naser Sabourian Zadeh

Bibliographic record

VenueJournal of academic and applied studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsRubricFormative assessmentPeer assessmentPsychologyPresuppositionTest (biology)Mathematics educationPeer feedbackPedagogyComputer scienceLinguistics
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.431
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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