The Effectiveness of Drama as an Instructional Approach for the Development of Second Language Oral Fluency, Comprehensibility, and Accentedness
Bibliographic record
Abstract
Although the development of second language (L2) oral fluency has been widely investigated over the past several decades, there remains a paucity of research examining language instruction specifically aimed at improving this cognitive skill. In this study, the researchers investigate how instructional techniques adapted from drama can positively impact L2 fluency, comprehensibility, and accentedness—three frequently discussed dimensions of L2 speech. Following a pretest–posttest design, the researchers obtained speech samples from 24 adolescent Brazilian EFL learners before and after their participation in a 4-month drama-based English language program. The development of oral skills by this group was compared with that of a parallel group of learners who received 4 months of instruction in a traditional communicative EFL classroom. Thirty untrained Canadian native English speaker raters evaluated randomized recorded L2 speech samples and provided impressionistic scalar judgments of fluency, comprehensibility, and accentedness. Results indicate that drama-based instruction can lead to significantly larger gains in L2 English oral fluency relative to more traditional communicative EFL instruction; comprehensibility scores also appear to be impacted, but with a much smaller effect; accentedness scores do not seem to benefit from one type of instruction over the other. The authors discuss implications for teaching practice.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".