The Development of CyberLearning in Dual-Mode: Higher Education Institutions in Taiwan
Bibliographic record
Abstract
Open and distance education in Taiwan has evolved into the third phase – cyberlearning – conceptualized as teaching and learning interactions mediated entirely through the application of state-of-the-art information and communications technologies (ICT), such as the Internet and World Wide Web (WWW). Socio-economic influences, the development of new technologies and a marked shift in learning paradigms have increased the utilization of ICT at all levels of the Taiwanese education system. Since the advent and provision of cyberlearning programs, well over half (56 percent) of the conventional universities and colleges have been upgraded to dual-mode higher educational institutions. They offer real-time multicast instructional systems using videoconferencing and cable TV technology, virtual classroom systems via network-based instructional management systems, and curriculum-on-demand systems utilizing video-on-demand technology. Critical success factors in the development of these cyber universities and the opportunities, challenges and implications inherent in these are analyzed. ICT and the provision of cyberlearning have gradually been changing the structure and vision of higher education institutions as well as the entire learning environment and educational systems. Because the Ministry of Education (MOE) has initiated a policy on credit-based degrees for cyberlearning courses/programs, the development of open and distance education is anticipated to hasten the transformation of Taiwan’s education system towards one which will create an ideal learning society in the 21st century.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".