Online Learning Experiences for Speaking Activities among Malaysian Undergraduate ESL Students
Bibliographic record
Abstract
Speaking practice is crucial for ESL (English as a Second Language) students. Nevertheless, accomplishing speaking activities via online learning might be difficult for the college students at universities in Malaysia as they are non-native English speakers. There have been several studies about students' perceptions and preferences on online learning. Nonetheless, there was only a little research conducted to ascertain deep online learning perceptions and preferences of the undergraduate ESL students in Malaysia. The current article's goal was to look at ESL learners' opinions on the experiences through online connected to speaking activities at a public institution in Tanjong Malim. The mixed-research method of study was used. In this article, observation checklist, semi-structured student interview, and questionnaire were employed to get the information from the sample. The sample was then subjected to statistical analysis and standard deviation, mean score, as well as percentage to identify the Malaysian undergraduate ESL students toward online learning platforms, the difficulty that the learners encountered during the online learning, and the strategies implemented by undergraduate ESL students. Whereas the interview result was analysed by thematic analysis. The findings indicate that the use of technology for distant learning for undergraduate ESL students to improve speaking abilities was progressing well. This signifies that the students perceived that they have knowledge to use the online platforms with mean (x)= 4.07. However, several students noted that their main concern when undertaking online speaking exercises was the connection speed with mean (x)= 3.93. As a result, soliciting thoughts or ideas to boost their engagement in online speaking projects was beneficial to the pupils with mean (x)= 3.97.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".