Educational Evaluation in China and the U.S.: A Literature-Based Inquiry into Its Impact on High School Students
Bibliographic record
Abstract
This study conducts a literature-based comparative analysis of educational evaluation systems in China and the United States, with a focus on their impact on high school students’ academic and social-emotional development. Grounded in Social Emotional Learning (SEL) Theory, the research systematically reviews 32 peer-reviewed studies published between 2010 and 2025, exploring how educational assessments—ranging from China’s high-stakes Gaokao to the U.S.’s formative and summative evaluations—shape student outcomes beyond academic performance. Findings reveal a fragmented body of literature: nearly half the studies examine one national context in isolation, while few delve into the psychological, career, or equity-related implications of assessment practices. Chinese evaluations, deeply rooted in Confucian meritocratic traditions, tend to emphasize collective achievement and social mobility, whereas U.S. assessments prioritize individual expression and holistic growth. However, both systems often neglect the social and emotional dimensions critical to student well-being. This paper highlights the need for more integrative, cross-cultural research and calls for educational policies that move beyond cognitive metrics to support students’ holistic development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".