Online Language Learning for Thai EFL Learners: An Analysis of Effective Alternative Learning Methods in Response to the Covid-19 Outbreak
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".