Effects of Sustained Impromptu Speaking and Goal Setting on Public Speaking Competency Development: A Case Study of EFL College Students in Morocco
Bibliographic record
Abstract
Research on impact of sustained impromptu speaking on public speaking competency development is scarce and lacking. The researcher investigated Moroccan college students’ public speaking competency development through extemporaneous (i.e. carefully prepared and rehearsed) speech performance, after implementation of a teaching strategy involving treatment through weekly impromptu (i.e., involving little or no preparation) speaking sessions combined with individual goal-setting strategy (teacher feedback). For this purpose, the researcher assessed 64 extemporaneous speeches delivered over the course of a semester using the public speaking competence rubric (PSCR), and observed the students’ public speaking progress through 90 impromptu speaking activities using a weekly goal-setting strategy. Results revealed that a combination of sustained impromptu speaking and goal-setting contributed significantly and effectively to public speaking skills development over the course of the semester. They also clearly showed that the teacher’s weekly goal-setting strategy played a major role in building speakers’ confidence and overall improvement. Considering the linguistic and cultural background of the students involved, together with the speech genres and the instructor’s task requirements, new public speaking competency dimensions and sub-dimensions have been identified.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".