Self-Efficacy and Self-Regulated Learning Strategies for English Language Learners: Comparison between Chinese and German College Students
Bibliographic record
Abstract
Two hundred Chinese and 160 German college students completed two surveys about their self-efficacy beliefs and self-regulated learning (SRL) strategies learning English in China and Germany. All participants took an English language test to measure their English proficiency. Cronbach’s alpha was used to check the internal consistency of the surveys, and confirmatory factor analysis was employed for the construct validity. Factorial invariance of the self-efficacy survey was tested between Chinese and German students. Both surveys turned out to be reliable and valid with satisfactory goodness-of-fit indices. The self-efficacy survey was found to have the same latent structure between Chinese and German students. The SRL strategy survey, however, was found to have a different latent structure. Mean differences between Chinese and German students were compared with multivariate analysis of variance, and relationships between self-efficacy, SRL strategies, and English proficiency were examined with structural equation modeling. Chinese students reported a lower level of self-efficacy beliefs but their English proficiency was not significantly different in comparison to German students. Their use of SRL strategies was not significantly different, either. Statistically significant relationships were noted between self-efficacy, use of SRL strategies, and English language test scores. Female students reported higher levels of self-efficacy beliefs but gained lower scores on the English language test in both countries. Implications of the results are discussed for English language instruction in both countries. Future studies are recommended to use the same English language test in two countries and to use a balanced sample between male and female students.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".