The Impact of Cooperative Learning on CHC Students’ Achievements and Its Changes over the Past Decade
Bibliographic record
Abstract
Informed by emergent learning theories and multiple evidenced benefits, cooperative learning has developed into a widely accepted organization mode of class in the Western context. For the same reason, cooperative learning is transferred, during the past decade, into classrooms of Confucian Heritage Culture (CHC) contexts. Concerns, however, are raised regarding the effectiveness of the transfer, for contextual factors have long been acknowledged as a powerful barrier to borrowed initiatives, especially those that are not compatible with the deep-rooted cultural values in the situated contexts.This paper is built on Thanh-Pham’s (2014) review of literature, which is on the impact of cooperative learning on the CHC students’ learning achievements and conducted during 1990 to 2006. This paper has expanded Thanh-Pham (2014) with a similar review on available literatures, which were published from 2007 up to 2016. This review of 39 publications shows up noticeable changes regarding the impact of cooperative learning in the CHC contexts. Specifically, the positive findings have risen from 47.2% to 86.9%, whereas negative and null change studies fall considerably. Influencing factors are analyzed via SPSS22.0 Software and verified with exemplars. Reasons for these changes point to the changing context and adaptive agency.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".