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Record W3200855460 · doi:10.1111/flan.12571

Effects of benchmarking and peer‐assessment on French learners' self‐assessments of accentedness, comprehensibility, and fluency

2021· article· en· W3200855460 on OpenAlexaff
Aki Tsunemoto, Pavel Trofimovich, Josée Blanchet, Juliane Bertrand, Sara Kennedy

Bibliographic record

VenueForeign Language Annals · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversité du Québec à MontréalConcordia University
Fundersnot available
KeywordsFluencyPronunciationBenchmarkingPsychologyPeer assessmentSelf-assessmentMedical educationMathematics educationPedagogyLinguisticsMedicine

Abstract

fetched live from OpenAlex

Abstract This study examined the effect of benchmarking and peer‐assessment activities on second language (L2) French learners' self‐assessments of accentedness, comprehensibility, and fluency. The learners, who included 25 L2 French students enrolled in a 15‐week university‐level French course, recorded two oral presentations at the beginning and the end of the course and self‐assessed their accentedness, comprehensibility, and fluency four times. In addition to regular course instruction, the treatment group also engaged in benchmarking (discussing and applying pre‐established evaluation criteria) and peer‐assessment (evaluating peers' speaking performances). The students' self‐assessments were compared with ratings of accentedness, comprehensibility, and fluency by 10 native‐speaking French raters. At the end of the course (i.e., for the second oral presentation), the treatment group showed greater alignment in self‐assessment of comprehensibility than the comparison group, relative to the external raters' assessments. Results highlight the value of assessment‐focused activities targeting L2 learners' awareness of pronunciation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.027
GPT teacher head0.323
Teacher spread0.296 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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