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Record W2341706033 · doi:10.18192/olbiwp.v5i0.1125

Logiciel d’annotation de vidéos FSL : Outil d’aide à la compréhension orale des documents authentiques

2013· article· en· W2341706033 on OpenAlexaffvenue
Natalia Dankova

Bibliographic record

VenueOLBI Journal · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsPronunciationComputer scienceSoftwareMultimediaVocabularyListening comprehensionAnnotationFeature (linguistics)Active listeningWorld Wide WebArtificial intelligencePsychologyLinguisticsCommunication

Abstract

fetched live from OpenAlex

A new open access online software FSL teachers to develop learning activities based on multimedia content in order to help learners 1) improve their listening comprehension, pronunciation and oral expression skills; 2) extend their vocabulary and 3) discover sociocultural aspects of foreign language. An original feature of the software is that it enables teachers to annotate videos with questions, comments or activities without altering their content. This software is user-friendly and as easy to use as Word. Thanks to this software, students are able to watch a video by themselves, with the help of a virtual teacher. Students can do different exercises autonomously and test their listening comprehension online. Another main feature of the software is that it makes it easy to find a particular sequence in a video. Thanks to this software, you can also develop online tests for your students.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.275
Teacher spread0.227 · 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.

Study designNot applicable
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 routes2
Has abstractyes

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