Research on the Development of Innovation of Teacher Education in University under the Era of All Media
Bibliographic record
Abstract
Today, we have already entered an unprecedented all media era in which the network media is highly developed and social we media are dazzling. Under this background, university teachers and students can not only obtain the information they need in a more efficient way than before, but also express their views and demands in a way of rapid and extensive dissemination. However, such a convenient all media also brought a lot of negative effects to the higher education. By analyzing the differences between the communication and social information dissemination in the era of all media and the past, this study on the one hand explores how universities innovate the incentive mechanism for the development of teacher education, and how university teachers themselves seize the opportunity of this era to improve themselves; on the other hand, it explores how to make better use of all media that students are keen on in the teaching process to carry out teaching work, maximize the benefits of high-speed dissemination of knowledge and information and minimize the disadvantages of students' addiction to the Internet, and by analyzing the effect of online teaching on the Novel coronavirus pneumonia epidemic situation, exploring the innovative path of future teacher education in universities, so as to better promote the development of higher education.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".