Using Of Teleconference As A Medium To Establish An “E-Global-Learning-System”: An Experience Of 1000guru-Association On Facilitates Open And Distance Learning Activities With Schools In Indonesia
Bibliographic record
Abstract
Information communication and technology (ICT) has been used in various fields. The use of teleconference for teaching and learning activities is currently not a new topic in global world. In Indonesia, through IMHERE Program from Directorate of Higher Education, some universities have been connected with a network of teleconference as a medium of disseminate knowledge. However, when compare to university, the use of teleconference is not yet well-known in schools in Indonesia. This paper aims at presenting the experiences on the use of Teleconference as a medium of teaching secondary school pupils new information on different subjects they discovered in international learning environment. This activity is carried out by Indonesian students who study abroad e.g. students who study in Germany, Japan, Canada, Australia and USA. This paper will also analyse the feedback from the beneficiaries of the teleconference program, to gather opinion about the prospect, challenges in the administration, organisation, and the pedagogy implication of the use of information communication and technology in schools in Indonesia. The author will also give suggestions or recommendations on the best strategies of improving the use of information technology in schools. This is important to further promote and establish the e-Global-learning-system in Indonesia.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".