Attitudes in Action: Examining the Impact of Chinese Language Learning Attitudes on Academic Success Among International Students in Sichuan
Bibliographic record
Abstract
Guided by Expectancy-Value Theory, this quantitative cross-sectional survey examined the relationship between learning attitudes, course satisfaction, and learning outcomes among non-degree international students (N=390, 97.5% response rate) from three Sichuan universities, predominantly from Southeast Asia. Measurement validity was established through confirmatory factor analysis. Regression analyses revealed that learning attitudes significantly predicted both learning outcomes (β = .59, p < .001) and course satisfaction (β = .33, p < .001). Course satisfaction also positively predicted learning outcomes (β = .41, p < .001). Mediation tests further showed that course satisfaction partially mediated the effect of learning attitudes on outcomes, increasing the explained variance from 36.3% to 41.1% (ΔR² = .048, ΔF significant). Findings highlight the importance of aligning Chinese language pedagogy with students’ motivational appraisals and satisfaction subscales. This study not only deepens the understanding of the effectiveness of Chinese language instruction but also provides practical guidance for the improvement and reform of Chinese language courses in universities, aiming to enhance international students' language learning experiences and their proficiency in Chinese.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".