Use of language learning strategies and self efficacy beliefs as predictors of English proficiency
Notice bibliographique
Résumé
The greater demands of internationalization made many young people from different nations to learn English, the most widely used communication instrument in Europe, the USA, Canada, and Australia. Indeed, to some, the lack of knowledge of English is seen as ‘linguistic deprivation’ because, due to its role as the language of the world, “any literate, educated person on the face of the globe is in a very real sense deprived if he does not know English” (Burchfield, 1986, p.283). The spread of English has been indispensable and being proficient in English is vital for many university students. Proficiency is affected by many factors, yet mostly learner-related factors come to forefront in recent years. Realizing that some people show rapid progress in language learning, whereas the others struggle to learn making slow progress, researchers turn to learner characteristics and preferences. Learners, therefore, have become the main focus in the studies trying to find out how the learners approach language learning tasks and whether the learners have certain characteristics which dispose them to good or poor learning. Besides the age and previous language learning experiences, Naiman, Fröhlick, Stern, and Todesco (1978) listed cognitive factors such as intelligence and language aptitude, personality factors and cognitive style, attitudes and motivation as the learner characteristics that are considered relevant and influential to the language learning. The list can be widened with other influential factors including the language learning strategy choice and the self-efficacy beliefs that the students hold. Language learning strategies have been one of the main focuses in the field of language learning as “rather than mere passive receptacles for knowledge, learners become thinking participants who can influence both the processes and the desired outcome of their own learning” (Oxford, 2008, p.52). Innumerous studies have been conducted to define and classify the language learning strategies (Naiman et al., 1978; O’Malley & Chamot, 1990; Oxford, 1990), yet no consensus has been reached. Despite these differences in definition and categorization, the researchers all agree on the idea that language learning strategies are effective on the achievement of the students (Chen, 1990; Goh & Foong, 1997; Green & Oxford, 1995; Wharton, 2000). Self-efficacy belief that the students hold about themselves is another factor that comes to play in the process of learning language. Bandura (1984) defines self-efficacy as “people’s judgments of their capabilities to organize and execute courses of action required to attain designated types of performances” (p.391) and considers it to be the central element in the Social Cognitive Theory. Since Bandura introduced the concept of self-efficacy in 1977, educational researchers have investigated the role of self-efficacy in learning (Huang & Chang, 1996; Linnenbrick & Pintrich, 2003; Mills, Pajares, & Herron, 2007; Pajares, 2002). These studies, despite the differences in the variables studied and in the results seen at the end, emphasize that self-efficacy is an indispensable part of learning and a good predictor for the success of the learner. Considering this theoretical framework and greater importance given to learning English at Turkish Higher Education Institutions, the current study was conducted to answer the following research question: “To what extent do gender, English self-efficacy level, and language learning strategy use predict the English proficiency scores of the language preparatory school students?
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».