Reducing Pre-service Teachers’ Public Speaking Anxiety through a Virtual Reality Intervention
Bibliographic record
Abstract
It is well documented that teachers experience a plethora of emotions in their work including anxiety. While most research has focused on anxiety in regard to curricular areas, teachers may also experience public speaking anxiety. Unlike curricular anxiety, public speaking anxiety permeates all aspects of a teacher’s work, thereby potentially exerting negative consequences for their own well-being and their students’ learning. This multi-method pre-post single-group pilot study aimed to explore sources of pre-service teachers’ public speaking anxiety and to test the utility of a virtual reality (VR) intervention to reduce public speaking and social appearance anxiety. Participants (n = 7) described three sources of public speaking anxiety: communication difficulties, expectations of others, and judgment of others. Participants delivered three lessons over a period of about 5 weeks to a VR classroom simulated by the Virtual Orator © program. There were 22 student avatars representing various genders and ethnicities. Avatars were programmed to be informal in their actions and generally friendly, but they could not interact with the participant. The results of paired-samples t-tests showed that public speaking and social appearance anxiety were significantly reduced following the VR intervention. Participants described the VR as realistic and useful for real life. We discuss the results from the perspective of the control-value theory of emotions with implications for teacher education programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".