Junior Middle School Students’ Perceptions of Mathematics Classroom Learning Environments and Their Approaches to Learning Mathematics in China
Bibliographic record
Abstract
This study investigated Chinese junior middle school students’ perceptions of mathematics classroom learning environments and approaches to learning mathematics, among 1,640 students from 62 junior middle school classrooms in eight provinces in China. A Chinese-language version of the Constructivist Learning Environment Survey (CLES) and Approach to Learning Mathematics (ALM) were used in this study and were proved reliable and valid in the Chinese context. Factor analysis, CFA, descriptive statistics, Independent-Samples T Test, and Bivariate Correlation were used to analyze data from the questionnaire survey. The results of this study indicate that Chinese students failed to perceive their classroom learning environment as relatively positive, and tended to use deep learning approach and surface motive in mathematics learning. In addition, significant urban-rural differences were identified in both perceptions of classroom learning environment, and approaches to learning. The findings reveal that deep approaches were positively associated with Chinese students’ perceptions of mathematics classroom learning environments (Personal Relevance, Uncertainty, Shared Control, and Student Negotiation).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".