A Sage on a Stage, to Express and Impress: TED Talks for Improving Oral Presentation Skills, Vocabulary Retention and Its Impact on Reducing Speaking Anxiety in ESP Settings
Bibliographic record
Abstract
This study explores the impact of using TED Talks on improving oral presentation skills of Business English students and vocabulary uptake/retention. It also assesses the impact of improving such hard cognitive skills on increasing Business majors’ speaking anxiety level. Sequential explanatory mixed method was used, which includes both quantitative and qualitative data collection and analyses. Business students’ oral presentation skills were assessed through Oral Presentation Skills Sheet (OPSS), vocabulary retention was assessed through Vocabulary Uptake/Retention Test (VURT), and speaking anxiety level was assessed through Personal Report of Public Speaking Anxiety (PRPSA) (Mörtberg, Jansson-Fröjmark, Pettersson, & Hennlid-Oredsson, 2018). Participants in the study consist 49 students, who were divided into two groups; experimental group consisting of 24 students, and control group that includes 25 Business English majors. Findings of the study revealed that oral presentation skills and vocabulary uptake/retention levels were improved due to the use of TED talks as an ICT tool. Also, it was revealed that Business majors in the experimental group are more enthusiastic, energetic and motivated to give killer presentations as they became more confident and free of anxiety and tension.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".