Teachers’ Perceptions of the Impact of the Double Reduction Policy on the Design and Amount of English Homework in Primary Schools – An Empirical Study
Bibliographic record
Abstract
In compulsory education, homework is considered a key means to help students consolidate the knowledge acquired in class. The introduction of the Double Reduction Policy in 2021 sets new requirements for assigning homework to students in the compulsory education phase. This study investigates the impacts of the Double Reduction Policy on the design and amount of English homework in primary schools as well as students’ perceived willingness to do the homework before and after the implementation of the new policy in a third-tiered city in Guangdong Province, China. Through the method of questionnaire and the statistics generated from paired samples t-tests, the study found that there was significant difference in terms of English teachers’ weekly class hours, students’ average time on doing English homework, English teachers’ practice of assigning unified and stratified homework before and after the Double Reduction Policy was implemented. In other words, students do less homework after the Policy was introduced. However, there was no significant difference in students’ perceived willingness to do English homework before and after the Policy. These findings may have practical implications for the Double Reduction Policy to be truly effective in its implementation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".