Enjoyment and Self-Efficacy in Oral Scientific Communication Are Positively Correlated to Postsecondary Students’ Oral Performance Skills
Bibliographic record
Abstract
Scientific oral communication is of major importance in democratic societies, but science students often dread giving oral presentations because of the stress they cause, and more generally, because of their attitude towards science communication. As attitude influences behavior, attitude towards science communication might have an impact on the performance students give during an oral presentation. This study was conducted with French-speaking postsecondary CEGEP (17–19 years old) science students in Montreal, Quebec, Canada. In this mixed-methods study, students’ attitude towards oral communication in science (n = 1295) was measured using a five-component model (perceived relevance, anxiety, enjoyment, self-efficacy (S-E) and context dependency). We then observed, by video, a sample of 26 students and measured their oral performance skills during a presentation on a scientific topic. The results suggest a strong correlation between oral performance in science and two components of attitude: the enjoyment of doing oral presentations and a specific aspect of S-E we called Showmanship S-E. In addition, although most students had a high perception of the relevance of oral communication in science, this did not correlate to their oral performance and most experienced anxiety about their oral communication.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".