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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.009
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.029
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