Enhancing ESL Learners Speaking Skills through Asynchronous Online Discussion Forum
Bibliographic record
Abstract
Communicating orally in a second or foreign language such as English can be a difficult task especially for the low proficiency learners as they may lack the confidence and feel shy and apprehensive when interacting verbally in the target language. This may hold them back from expressing themselves vocally though they are fully aware that in order to be fluent in the target language, they need to practise speaking not only in the classroom but also outside the classroom. This paper discusses the use of an asynchronous online discussion forum (AODF) as a communication tool to assist the low proficiency ESL learners to build their confidence and practise using the target language orally. As an online discussion forum, the Multimedia Enhance Discussion Forum (MEDiF) was specifically designed to be used by a selected group of low proficiency ESL learners at tertiary level for one academic semester. This online forum allows the learners to audio and video-record their discussions, listen to the recorded discussions and respond to their friends’ ideas and opinions. Interviews and observations of the discussions on the MEDiF were conducted to gather data. The findings generally indicate positive responses from the ESL learners although there were also some obstacles faced by them while utilizing the MEDiF.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".