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Enregistrement W3125198333 · doi:10.29038/eejpl.2016.3.2.dol

Word Skipping in Reading English as a Foreign Language: Evidence from Eye Tracking

2016· article· en· W3125198333 sur OpenAlex

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Notice bibliographique

RevueEast European Journal of Psycholinguistics · 2016
Typearticle
Langueen
DomainePsychology
ThématiqueVisual and Cognitive Learning Processes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEye trackingReading (process)Eye movementFixation (population genetics)PsychologyForeign languagePerceptionLinguisticsCognitive psychologyCognitionWord lists by frequencyComputer scienceSentenceArtificial intelligencePopulationMathematics education

Résumé

récupéré en direct d'OpenAlex

During reading, readers never fixate on all words in the text; shorter words sometimes gain zero fixation and skipped by the reader. Relying on E-Z Reader Model, this research hypothesized that a similar skipping effect also exists for a second language. The current study examined word skipping rates in EFL (English as a Foreign Language) with 75 EFL learners by using eye tracking methodology. The results showed that word skipping was affected by EFL reading proficiency significantly and articles (a, an, the) were skipped more than content words. Furthermore, more skilled learners were observed to have less fixation count and skipped more words during reading while less skilled learners employed more fixations and skipped less words. Eye tracking as a novel method to observe learner development and progress in EFL reading was also discussed. 
 References
 
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Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,011
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,700
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,011
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,050
Tête enseignante GPT0,372
Écart entre enseignants0,322 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle