Simultaneous versus Consecutive Movie Narration: An Attempt at Boosting L2 Oral Communication Skills
Bibliographic record
Abstract
Fluency in one of the most significant components of oral communication, and in the communication era, when it is essential for almost everyone to master speaking skills in foreign languages, especially English, this component needs to be put in the spotlight. Diverse steps have been taken in the world of SLA to promote learners' fluency in L2, yet there is still much to do in this arena. The present study aims at probing the effectiveness of simultaneous movie narrations as a new fluency-booster strategy. To this end, 66 students of IELTS speaking preparation classes in an institute in Tehran, Iran were selected and put into 2 groups of 33 (each group distributed in 3 classes). The homogeneity of the sample was checked by a mock IETLS test obtained from Cambridge IELTS 10 (Cambridge Local Examinations syndicate ( 2015) and through a MANOVA. The treatments contained 24 hours of training and practice on IELTS speaking strategies, offered to them in 16 sessions of 90 minutes. The first half of each session was allocated to teaching and practice on the institute’s main course book – Focusing on IELTS - Listening and Speaking Skills (Thurlow & O'Sullivan, 2011). In the second 45 minutes, learners of both groups were exposed to the same 10-minute movie extracts, and practiced their narration and speaking skills in pair-group activities. Learners of the consecutive narration group narrated the movie plot and actions with delay (consecutively) whereas those of the simultaneous narration group narrated them simultaneously as the movie was being played. The results of the statistical analysis of the posttest highlighted that simultaneous narration group learners outperformed those of the consecutive narration group in terms of oral communication skills. The findings of this study have pedagogical implications for English teachers, teacher trainers, exam preparation teachers and simultaneous interpreters’ trainer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".