D2L Learning Management System in America: It’s Character and It’s Inspiration Towards Online Education of China
Bibliographic record
Abstract
After 20th century 90s, online education becomes popular all around the world. Along with the development of information technology, online education starts to affect traditional education and learning methods. In this era which is new media frequently used while interaction and participation, it deeply changed the learning activity essence and form. America, as one the example of mature online education, used D2L learning management system in higher education not only to broaden and deepen the students knowledge, but also to provide students with multi-skill training opportunity. It emphasize the research and course teaching interaction as well as its parallel development,improved students learning quality in higher education. This article, through character analysis of D2L learning management system in America, try to inspire China’s online education with D2L’s successful experience. Also try to propose a feasible suggestion to improve China’s higher education online environment, form a new model of China’s online education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".