Research on Blended Learning of Higher Vocational English Based on Cloud Platform
Bibliographic record
Abstract
With the rapid development of international science and technology, higher requirements are put forward for the professional quality of professional and technical personnel. At the same time, more and more skilled talents with high quality are attracted to enter the high-tech industry. These complex skilled workers have also become the talent targets for all walks of life, and the talent gap is increasing every year, so the demand for skilled talents in the industry is far from being met. Therefore, as the first position to cultivate high-quality skilled talents for today's society, vocational colleges must carry out a profound education reform to meet the current social needs, realize the function of vocational education to promote social development, and fully guarantee the development of science and technology. Under the impact of the network, traditional education has been unable to provide sufficient power for social development. The realization of information technology and network campus has a profound impact on vocational education, making vocational education rapidly transformg. MOOC platform of high-quality education resources, micro class teaching and blended teaching and so on, some new teaching methods combined with the Internet, more and more have been applied to vocational education. For these aspects of research, scholars have also been widely concerned. As a teacher who has been struggling for a long time in the front line of college English teaching in higher vocational school, the author urgently reshapes the teaching mode of hgher vocational English classroom with the help of the power of network. After browsing a large number of relevant literature, this paper summarizes the current development degree, problems and reasons of blended teaching in higher vocational colleges, and puts forward solutions according to their own teaching experience.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".