Influence of Acculturation in Yunnan’s Ethnic Minority College Students on Their Academic Achievement: The Moderating Role of Learning Motivation
Bibliographic record
Abstract
This study examined the effect of Yunnan’s ethnic minority college students’ acculturation on their academic achievement under the risk of the Matthew effect. Additionally, the role played by learning motivation in the relationship between ethnic minority college students’ acculturation and academic achievement was explored. A total of 403 valid questionnaires were collected from four areas in Yunnan province, China. Consequently, the Acculturation Scale, Academic Achievement Scale, and Learning Motivation Scale were used for measurement materials. These items of scales were evaluated on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). SPSS (statistical package for the social sciences) and AMOS (analysis of a moment structures) softwares were used for data analyses. In addition, items were analyzed through item analysis, confirmatory factor analysis, reliability analysis and regression analysis. These results indicated that ethnic minority college students with low acculturation and learning motivation or high acculturation and low learning motivation can become objects of the Matthew effect. However, this study also observed that in certain students, high acculturation and high learning motivation can prevent the Matthew effect. Thus, high acculturation is crucial for improving academic achievement in ethnic minority college students. A level of high learning motivation is a powerful moderator promoting the academic achievement of students with high acculturation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".