Web-Based Synchronous Speaking Platforms: Students’ Attitudes and Practices
Bibliographic record
Abstract
This study employed the interaction hypothesis (Long, 1983) to investigate attitudes towards different English accents (i.e., American and British) of 40 male undergraduate EFL students majoring in English. It explored the reasons for such views, as well as identifying the accent the participants found most effective for communication. The study also examined students’ attitudes to online speaking by means of a Synchronous Computer-Mediated Communication (SCMC) website known as ‘Cambly’. Students were granted free access to the Cambly website for live interaction with Native English Speakers (NES). Each student talked for 15 minutes with an American and British interlocutor, enabling the researcher to recognize the common topics appearing within these conversations. Data were collected using a mixed-method approach, employing a web-based survey of closed and open-ended questions, alongside the recorded conversations. The key findings reveal that students enjoyed the SCMC conversation and also found it beneficial for improving their speaking skills. Furthermore, SCMC allowed students to choose the topic and negotiate meaning with native speakers during a lengthy conversation. This study establishes that students preferred American to British accents and felt more confident in understanding American speakers. The study concludes by highlighting the practical implications for teaching speaking skills, also suggesting new directions for future research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 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".