PENINGKATAN KEMAMPUAN PUBLIC SPEAKING MAHASISWA MELALUI PELATIHAN INTERAKTIF BERBASIS PRAKTIK
Bibliographic record
Abstract
Public speaking is an essential skill for university students as future professionals across various fields. However, many students still face difficulties in expressing ideas clearly and convincingly, mainly due to a lack of confidence, experience, and understanding of effective communication techniques. This community service program aimed to provide structured training that not only introduced theoretical concepts but also offered hands-on practice through interactive activities. The methods included lectures, workshops, presentation simulations, and final evaluations to assess participants’ progress. Results showed that around 90 % of participants experienced significant improvement in confidence, content mastery, presentation structure, and appropriate use of body language. Participants also reported that the training helped them manage nervousness and enhance adaptability in diverse speaking situations. Overall, the program proved effective in equipping students with relevant and applicable public speaking skills suited to both academic and professional environments. Additionally, the program encouraged students to continue practicing independently, broaden their understanding of modern communication strategies, and build collaborative networks with peers who share similar interests in self-development and professional competence. This initiative is expected to create a supportive and sustainable learning environment for the future development of students’ speaking abilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".