The Characteristics and Self-Regulation of Undergraduate Students in Online English Learning: A Case Study of A Private University in Thailand
Bibliographic record
Abstract
Online learning is readily available in Thailand, and scholars acknowledge its importance in assisting language learners to accomplish their foreign language goals. Currently, with the COVID-19 global pandemic, self-distancing is helping to reduce the infection rate. Since learning must continue, technologies play a vital role. Educators and students have managed to adjust to the unprecedented situation and continue with classes despite the many obstacles. Thus, this study examines the characteristic variables (motivation, belief in language, and anxiety) and self-regulation in online English learning classes, as well as investigating the relationship between the characteristic variables and self-regulation of undergraduate students at a private university in Thailand. The study involves 132 participants enrolled in an online English course during the pandemic, with a questionnaire and focus group interviews employed as the research instruments. The results showed that the students were highly motivated, exhibited positive beliefs, moderate anxiety, and high self-regulation toward online English learning. Two variables, namely motivation and positive beliefs, were found to be correlated with self-regulation in online English learning at the 0.01 and 0.05 significance level, respectively. Anxiety in online English learning was found to have no significant relationship with self-regulation in online English learning, indicating that students experiencing some level of anxiety during the online class could still exhibit self-regulated behavior. These findings are expected to provide a foundation for further research in the online learning field.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".