Designing Model for Oral Presentation Instruction in Indonesian Tertiary Context
Bibliographic record
Abstract
The purpose of oral presentations is to prepare students with the necessary skills for the professional environment. However, it has been observed that the courses offered by universities in East Kalimantan, Indonesia are designed in general English, which may not cater to the specific needs of the learners. This research is intended to determine the optimal oral presentation; however, this study is required to analyze the demands and appropriate model of oral presentation education in tertiary context. Data was gathered through questionnaires and interviews. The questionnaire results show that students need more opportunities to practice logical arguments, pronunciation, collaboration, and regular use of mobile learning to improve their understanding and attitude towards it. Instructors recognize the importance of peer projects and mobile learning in facilitating discussions and sharing among learners and instructors, as it enhances their learning experience. They revealed that online mobile applications such as editing online documents and instant messaging can help to do great collaboration in preparation and implementation phase. According to the result of the need analysis, the authors propose Mobile Technology Enhanced Collaborative Learning (MCOP). It is an oral presentation learning model that emphasizes the importance of utilizing collaboration and technology aspects in learning. The development of the instructional model will provide teachers with a concise and transparent path for teaching oral presentations using mobile technology. This model offers students opportunities to develop their oral presentation skills. The students will have transformational experiences for both their personal and social development.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.008 | 0.002 |
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".