Online Language Learning for Thai EFL Learners: An Analysis of Effective Alternative Learning Methods in Response to the Covid-19 Outbreak
Notice bibliographique
Résumé
Online language learning had already been an increasingly popular and useful method of language acquisition prior to spike in demand for alternative learning methods brought upon by the Covid-19 pandemic. As a tool that allows learning to continue without undue risk of exposure to the virus, it has increasingly become a new normal for students around the world, and Thai EFL learners specifically. As online learning has become more widely used in response to this worldwide plight, it has become accepted as an important tool and approach that can overcome the inherent dangers and limitations present in on-campus learning. This has become accepted in educational institutions around the world and is no less true for the Thai educational system. Educational technology (EdTech) has made significant progress in recent years, moving far beyond the simple ability of communication with your professor allowed by email and more recent systems of online assignment submissions. As the sophistication of EdTech advancements grows, it’s applications can be used to expand the reach and approachability of lessons beyond a formal classroom environment; thereby, increasing both the motivation and effectiveness of the learner. Modern teaching philosophy, across all subjects, actively encourages the incorporation of technology to aid in the facilitation of optimum teaching delivery. This is especially important for those teaching a new language (for our purposes, English to Thai learners), as these technological tools can facilitate greater understanding in an environment where the language barrier is otherwise an impediment. This paper looks to further analyze the effectiveness of online learning methods and tools, for both the instructors and the language learners. Furthermore, this paper will propose and review methods to integrate and use this technology in the classroom or other learning environment available. As distance learning is becoming a more prevalent methodology due to the untenable nature of holding class in enclosed, densely packed, spaces (i.e., your standard classroom), knowing more of both the technologies and their effectiveness in a language learning environment is of paramount importance. In this paper, the terms online instruction and integration are widely used and their use in the context of language learning is provided. This paper also explores the EdTech and devices relevant to the discussion and provides explanations of their use. To provide a proper foundation, we will also be discussing prior literature and findings pertaining to the use of technology in the context of English language learning. This paper will also provide and discuss the reaction and results obtained from online language learners using the proposed medium. These results were gathered from a combination of recorded online observations and measured learning outcome objectives. From the combination of material provided, studied, and analyzed, this paper concludes with a presentation of potential methods that may help instructors improve the English language acquisition of their 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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».