A Comparison of Pre-service Teacher Educational Modes: Taking China, the United States, Canada and Singapore as Examples
Bibliographic record
Abstract
In globalization, pre-service teacher (PST) educational modes in different countries reflect unique cultures, educational traditions and social needs. In-depth comparisons and analyses of these modes can provide cross-cultural educational insights to help countries learn, borrow and innovate in teacher education. This study explores and compares PST educational modes in China, the United States, Canada and Singapore, with particular attention to the characteristics, strengths and limitations of these modes and how they are adapted to their respective cultural and social needs. A comparative analysis approach is adopted to analyze these countries' educational modes, cultural backgrounds and policy environments through systematic collection and collation of relevant literature and then to draw out the differences and commonalities of the different methods. It is found that the PST educational modes in each country are closely related to their cultural and educational traditions and reflect a shift in the role of teachers from traditional knowledge transmitters to facilitators of learning, and promoters of technology play a vital role in teacher education and pose challenges. Countries have recognized the importance of lifelong learning in developing the teaching profession. These findings emphasize the importance of cultural differences in educational practices, and educational policymakers must understand and respect these differences when designing teacher education policies and procedures. At the same time, there is a need to focus on the impact of technological advances on education and to improve teacher resilience and the quality of teaching through policies and practices that support lifelong learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".