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Record W2326599554 · doi:10.3138/cmlr.2802

Putting Students at the Centre of Classroom L2 Writing Assessment

2016· article· en· W2326599554 on OpenAlexvenueno aff
Icy Lee

Bibliographic record

VenueCanadian Modern Language Review/ La Revue canadienne des langues vivantes · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsSummative assessmentFormative assessmentAssessment for learningWriting assessmentMathematics educationPedagogyPsychology

Abstract

fetched live from OpenAlex

In many educational contexts, L2 writing assessment tends to emphasize its summative functions (i.e., assessment of learning – AoL) more than its formative potential (i.e., assessment for – AfL). While the teacher plays a dominant role in AoL, central to AfL is the role of the students, alongside that of the teacher and peers. A student-centred approach to L2 writing assessment involves learners actively in setting goals, monitoring their progress, and deciding how to address the gaps in their learning. Such a focus, also referred to as assessment as learning (AaL), puts students at the centre of classroom assessment. In the L2 writing literature, however, descriptions and explanations about how AaL can be implemented in the writing classroom are scant. This article attempts to provide a clear understanding of AaL and how a student-centred approach to classroom assessment can be applied in the L2 writing classroom, thus contributing new knowledge to the existing literature on classroom L2 writing assessment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.004
Scholarly communication0.0070.005
Open science0.0020.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.002

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 head0.017
GPT teacher head0.310
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations42
Published2016
Admission routes1
Has abstractyes

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