The Effects of Peer-Video Recording on Students’ Speaking Performance
Bibliographic record
Abstract
The purpose of the current study is to investigate whether peer video recording helps non-English majored college students enhance their speaking performance. Eighty students were selected and assigned to two groups: an experimental group and a control group. Peer video recording was presented to experimental students while no training was given to students in the control group in the same task-based approach. The data, collected based on a pre-posttest design, were analyzed to find out whether or not there were differences between two groups in terms of fluency, grammar, vocabulary, pronunciation and interactive communication. A questionnaire-based survey was also implemented to explore students’ attitudes on the treatment—peer video recording task-based approach. The study’s results revealed that students in the group treated with peer video recording task-based approach significantly outperformed those in the control group in terms of fluency, grammar, pronunciation and interactive communication while students’ vocabulary score remained after the treatment. In addition, the data obtained from the questionnaire indicated the experimental students had positive attitudes towards the peer video task-based approach. The results from the study provide grounds for some suggestions and recommendations for the teachers, the students as well as the teaching and learning speaking in Vietnam.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".