Cross Cultural Comparison of Rural Education Practice in China, Taiwan, and the United States.
Bibliographic record
Abstract
The purpose of this research is to compare the rural education practices of China, Taiwan, Canada and the United States. International comparisons of mathematics achievement find that students in Asian countries outperform those from the USA. Excluded from these studies, however, are students from rural areas in China. This study compares the math abilities of 272 selectively chosen 5 grade students from rural, central China, 361 students from rural, northern Taiwan and 95 students from rural, central Pennsylvania. The test instrument was the same as used in previous China vs. USA comparisons and focused on four subtopics: computation, number concepts, geometry and problem solving. The results showed that rural Chinese and Taiwanese students outperformed similar American students in the area of mathematics achievement. The rural Chinese and Taiwanese students were also found to be more focused on school and academics and less on social aspects of school life. Their parents held higher expectations for them to do well in school. However, these cultural differences were not able to explain away the overall math achievement gap among the Chinese, Taiwanese and American students. It is recommended that further study be conducted to explore possible factors that contribute to different math achievement among the countries by interviewing students, teachers, and the parents. A. Purpose of the Research The purpose of this research is to compare the rural education practices of China, Taiwan, Canada and the United States. Although many cross cultural studies comparing student achievement exist, none of them seem to focus on rural education. Thus, there is a lack of understanding of how education systems deal with children who come from farming, and often low-education family backgrounds. Some studies have shown that students from the United States do not perform as well in math and science as compared to students from around the world. Especially notable is how Japanese and Singaporean students outperform US students (Beaton et al. 1996a 1996b; Schmidt et al). Chinese students also display high levels of achievement. However the principal studies showing Chinese versus US performance do not include students who live in rural parts of China. Thus, this research project focuses on comparing rural education practices and outcomes in Asian and the western countries. This study first, attempts to evaluate which students perform better. Then it seeks to uncover the practices within the family and/or school that lead to superior performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".