Coping with the challenges of open online education in Chinese societies in the mobile era: NTHU OCW as a case study
Bibliographic record
Abstract
In an era witnessing the rapid development of information technology, mobile devices have brought revolutionary changes to learning. A single conventional media platform is not enough for the various mobile devices. Technology-enriched educational environments supported by different devices are important research issues nowadays. To capture the rapid growth of mobile users in Chinese societies, OpenCourseWare (OCW) needs to move their learning models toward the mobile sphere. Therefore, this study reports the three years of empirical experience in implementing the upgraded National Tsing Hua University OCW platform and analyzes how users access the platform with various devices. The results indicate a responsive web design and cloud-computing provide great accessibility to meet the diversity of various mobile devices from Chinese users throughout the world, including 466,429 visits with 264 different mobile devices from 146 territories. Moreover, the proposed solutions make the workflow of OCW production more efficient. The study further discussed the importance of both tablets and smartphones. Moreover, to expand the reach of open educational resources (OER) in Chinese societies, the critical issues of fair use and sustainability of OER should be of concern. The findings of the study provide valuable references for web engineers and educators to explore cross-device online learning using PCs and mobile devices.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".