Bibliographic record
Abstract
E-learning in Indonesia began in mid-nineties with the advent of internet preceded by information technology which was introduced in Indonesia in late 1970s and early19 80s. However, those elearning initiators hit hard by the economic and political crises which hit Indonesia in 1997s until early 21st century. Beginning the year 2000, many schools, especially senior high school, took the initiatives to conduct e-learning in their environments, in spite of the economic crises. Based on data available from the Department of national Education, a survey conducted toward high school Websites. A virtual visit and randomly selected physical visits to high schools situated in Jakarta, Yogyakarta (Central Java), Makassar (South Sulawesi) and Padang(West Sumatera) yielded result that those Websites mainly used for disseminating school profiles including name and address, principals and teaching staffs, facilities, extracurricular works etc, but none specially directed to e-learning materials. The research orientation changed to vocational high school with the assumption that the vocational high school graduates are geared toward working market hence the courses are directed to more practical application and subsequently can improved with e-learning activities. Based on data from National Library of Indonesia and Directorate of Vocational High School, purposive sampling was done. The criteria are (a) the school has conducted e-learning for at least five years; (b) agree to be interviewed; (c) has trained other schools on e-learning development; (d) own a school library (e) appointed by Directorate of Vocational High School as a pilot project (f) accessible economically from Jakarta so it is more convenient to visit. Using snowball method, from interview with Wikrama principal and teachers, yielded data on other vocational schools in many regions. Those vocational high schools are randomly selected, interviewed by volunteer researchers . The results are analysed and showed that e-learning in vocational high school is limited to facilities designed by the school such as commonly found in Intranet, the constraints mainly on physical infrastructure and e-learning spread because of lack of facilities from school library. The development of e-learning activities is separated from school library, a cause resulted from the wrong perception of the school principals. It is suggested that the development of e-learning should synchronised with school library because in the future, even right now, school library will developed into learning resource centres, in which e-learning is covered. However, it needs better understanding among school principals which in Indonesia who will decide the fate of school library, either like it or not.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".