A Comparative Study of Distance Teaching in Elementary Schools Between China and the United States Under COVID-19
Bibliographic record
Abstract
The outbreak and quick spread of COVID-19 quickly shut down almost all educational institutions in different parts the world by mid-March, 2020, presenting severe challenges to educators all over the world. The paper investigated the teaching situation of elementary schools in China and the United States during the epidemic period. The results show that the common challenges faced by the two countries in distance education are: 1) Elementary school students are young and have poor self-control ability, which requires close supervision and cooperation from parents; 2) Teachers’ information technology level is limited, lack of network teaching ability. There are some problems in Distance Education in American elementary schools, such as parents can’t urge students to study at home, digital technology inequality, low online attendance rate and so on, which further exposed the unfair problems in education. The problem of distance learning in elementary schools in China is that the learning resources are mainly subject knowledge, and the ability and methods of teachers to serve students’ autonomous learning are insufficient. American distance learning experience is to make full use of the rich network learning resources and professional distance teaching platform, and pay attention to the cultivation of students’ network autonomous learning ability. China’s distance teaching experience is to give play to the advantages of centralized management, organize famous teachers to record high quality courses, establish the Air Classroom platform, and provide high-quality classroom teaching resources. Therefore, basic education institutions in both countries should strengthen the training of teachers’ distance learning ability, improve the information-based teaching environment, learn from the experiences of both sides, and explore an effective mode of integrating online and offline education resources, so as to meet the challenges of basic education in the post epidemic era.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".