Supporting English Language Learners with an Adaptive Mobile Application
Notice bibliographique
Résumé
English language learners (ELL) have dedicated considerable time and effort to the development of their language proficiency. This has included the use of a variety of mobile assisted language learning (MALL) tools that are either unproven or that have undergone limited evaluations of their effectiveness. The majority of these evaluations have been performed with beginner foreign-language learners at the post-secondary level. Moreover, dedicated MALL tools rarely support the learner’s ability to communicate in English. I propose and demonstrate the feasibility of an adaptive MALL approach that aims to scaffold ELL vocabulary and communication needs. This scaffolding recommends learning materials to ELLs by employing the ecological approach to dynamically reason over logs of learner interactions with a MALL tool. \nThe highly personalized approach to supporting learners that is operationalized through this tool was developed following user-centered design principles. The development of the learning content generation and recommendation mechanisms that are included as part of this approach to supporting English language learners was validated through two studies. An additional exploratory evaluation of this adaptive approach to supporting ELL communication and learning activities was performed before evaluating its influence on ELL vocabulary knowledge, communication, and affect through two studies. These studies considered the effectiveness of the proposed MALL approach from multiple perspectives. The first took place in a Japanese high school and focused on the relationship between student vocabulary knowledge and system usage. The second involved advanced English language learners and took place in the greater Toronto area. This study aimed to determine the relationships among system usage, user communicative success, and user affect. \nThe work presented in this thesis shows that the use of the proposed approach can support ELL communication, vocabulary development, and affect. The evaluation of this approach allowed the creation of models that predict learning outcomes based on learners’ MALL usage and knowledge. Combining the results of these studies with those of the formative evaluations, indicates that a mobile tool that employs the ecological approach to learner modeling can support the learning activities, vocabulary learning outcomes, affect, and communication of English language learners.
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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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».