More than access: MOOCs and changes in Chinese higher education
Bibliographic record
Abstract
This paper presents an analysis of the conceptualization of massive open online courses (MOOCs) by major influencers in Chinese higher education. Using critical discourse analysis, predominantly from university resources, a map of the discursive construction of MOOCs is presented and interpreted. The centralized orientation of decision making in Chinese higher education is reflected in how MOOCs have been introduced, envisioned, and utilized in China. With the increase of Chinese MOOCs, elite universities are able to capitalize on their comparative advantages, which may be counter to the true intent of MOOCs, which is to raise teaching standards across sectors. This paper serves to illuminate how MOOCs may reinforce the status of elite universities, thereby having the opposite effect to their real intention of democratizing higher education for the masses. The strategy of using MOOCs to improve teaching quality and augment the worldwide reputation of Chinese institutions is central to China’s reinvigorated focus on higher education, which counters the widely held perception, and intention, that MOOCs are vehicles for widening access.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".