Bibliographic record
Abstract
Open and distance education has been playing an important role in China’s development of higher education and lifelong learning. In 2012, the Chinese government approved six large-scale radio and television universities (RTVUs) to become open universities (OUs), including the Open University of China (OUC), Beijing Open University (BJOU), Shanghai Open University (SHOU), Guangdong Open University (GDOU), Jiangsu Open University (JSOU), and Yunnan Open University (YNOU). The purpose of this study is to provide a descriptive analysis of the transition from RVTUs to OUs, and the current state and challenges of open universities in China after five years’ reform. Five topics are explored in this paper, including: the new positioning of open universities in China’s vast and differentiated higher education system; award bearing and non-award bearing program offerings; implementation of the online teaching and learning modes; the use of Open Education Resources (OER) and online mini-courses; and the development and use of a credit bank system. A summary of these topics follows a discussion of four issues of open university reform, including key performance indicators (KPIs) for open universities, cohesion and resource sharing between the national and provincial open universities, quality assurance for award bearing programs, and planning to transform China’s existing 39 provincial RTVUs into OUs. It is expected that the results of this study would contribute to knowledge about institutional differentiation in the world’s largest higher education system, and on the merits of open and distance education in the human resource development in China. This paper may also provide insight for other countries that are engaged in institutional differentiation of higher education systems punctuated by the essential role of open universities in such planning and implementation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".