A Systematic Review of Self-Coping Strategies Used by University Students to Cope with Public Speaking Anxiety
Bibliographic record
Abstract
Despite a growing body of research on instructor techniques and treatments to mitigate public speaking anxiety, this issue remains prominent, especially among university students. An alternative to mitigating such anxiety is to identify authentic coping strategies that university students could practice in actual situations. Numerous studies have attempted to explore students’ personal and social factors with the objective of suggesting suitable coping strategies to reduce the fear of public speaking. This paper reviews the existing evidence to understand the complexities of strategies that university students use to reduce their fear of public speaking. Nine peer-reviewed studies published between 2015 and 2020 were selected for this review from Science Direct and Google Scholar, using search terms such as “public speaking anxiety” and “coping strategies.” The analysis revealed that university students who (a) had an intermediate level of English language proficiency and a high level of speaking anxiety adopted both compensation and metacognitive strategies; (b) had a high level of English language proficiency and speaking anxiety adopted the affective strategy; and (c) had a high level of speaking anxiety and were exposed to full English medium instruction contexts adopted both social and memory strategies. This review, therefore, provides a better understanding of how university students cope with public speaking anxiety and at the same time urges educators to refine their pedagogical methods to lower the psychological barrier of speaking.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".