An exploratory study of the hypothesis of divisible versus unitary competence in second language proficiency
Notice bibliographique
Résumé
In this research Oiler's question 'Is language proficiency divisible into components?' was explored by determining which of three models best fit the experimental data: a model postulating numerous specific sources of variance (the extreme divisible model), a model postulating a single, large source of variance (the unitary model), or a model postulating a large general factor and several smaller specific factors. Following analysis of data gathered in a preliminary study, four tests which had clearly recognizable contrasts in content (grammar vs. vocabulary) and mode (listening vs. reading) were constructed to identify linguistic and method variance in a correlation matrix of language proficiency variables. These four measures were pilot tested, revised, and administered in conjunction with eight other language measures to a group of beginning-level ESL learners. The data were factor analyzed using image analysis to explore the relative congruency of the three models to the data. In addition, the relationships between the tests and the demographic variables age, sex, length of time in English Canada, and first language were also investigated. In the factor analysis, both of the methods used to determine the number of factors to be retained in the final solution indicated three. (The methods used were the Kaiser-Guttman criterion of selecting factors with eigen values greater than one in a principal components analysis and the inspection of a varimax rotation of a full image analysis to determine the first factor with negligible coefficients.) When transformed using a Harris-Kaiser oblique transformation (Independent Clusters), the data presented evidence for a grammar factor, a vocabulary factor and an age-related factor which may be linked closely to the hearing ability of the students. In addition, the analyses suggested the possibility that a listening-mode factor and what I have termed a 'speed of processing factor' were also influencing the variables. The factors, however, were highly correlated, suggesting the presence of a strong general factor underlying all of the measures. The analyses of the specific relationships between each of four demographic variables (age, sex, first language, and the length of time the subject had been in Canada) and each of the twelve language variables revealed a strong negative correlation between the language measures and two of the demographic variables, age and length of time in Canada. In addition, this set of analyses revealed that the Chinese as a group performed differently than non-Chinese as a group. The analysis of sex produced no significant findings. The conclusion of the study was that the language proficiency data in this study was best modelled by a large general factor and two specific, content-related factors, grammar and vocabulary. The possibility of specific factors related to mode was not ruled out.
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.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».