MétaCan
Menu
Back to cohort
Record W2899464207 · doi:10.17507/jltr.0906.06

Oral Portfolio in Spanish as a Third Language: Harnessing the Potential of Self- and Peer-Assessment

2018· article· en· W2899464207 on OpenAlexaff
Pierre-Luc Paquet, Sara Downs

Bibliographic record

VenueJournal of Language Teaching and Research · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsFormative assessmentPortfolioSelf-assessmentPeer assessmentPsychologyComputer scienceMathematics educationPedagogyBusiness

Abstract

fetched live from OpenAlex

Even though research in second language acquisition has demonstrated the importance of oral production and interaction, there is a growing tendency toward distance learning. Therefore, in order to include oral practice and evaluation in an online course, a new pedagogical tool was designed, namely the oral portfolio. This article describes and analyzes an oral portfolio which included learner production and self- and peer-assessment. Combining these aspects provided data on both linguistic and metalinguistic abilities. The results revealed a relationship between oral competency and self- and peer-assessment abilities, suggesting a beneficial role of metalinguistic reflection in the development of oral communication skills. Moreover, this study explored how self- and peer-assessment could be better implemented in a language course. Based on the observations gathered throughout the study, we believe that learners need to be trained and to develop the formative assessment competency, in order to maximize the benefits, for assessment to be as sustainable as possible.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.367
Teacher spread0.338 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Language Teaching and ResearchSame topicSecond Language Learning and TeachingFrench-language works237,207