Implementation of Problem-based Learning in Reading among Primary School English Teachers in Neijiang City, Sichuan Province, China
Bibliographic record
Abstract
Problem-based Learning (PBL) is a student-centered teaching method with teachers as facilitators. In order to promote the mastery and implementation of PBL by primary school English teachers, this study is conducted to determine the knowledge and implementation level of the PBL, and compare the knowledge and implementation level of the PBL between English major and non-English major teachers. A total of 150 English teachers from public primary schools in urban areas of Neijiang City, Sichuan Province, China was involved in the study. The instrument used was an online questionnaire. The result from descriptive statistics and T-test showed that the knowledge and implementation level of PBL among primary school English teachers is high. Besides, there is no significant difference between English major and non-English major primary school English teachers in knowledge and implementation level of PBL. It was found that English major background has no effect on teachers’ knowledge and implementation level of PBL in primary school English reading class. Overall, this study explored the implementation of PBL among primary school English teachers in reading and promoted teachers’ attention to PBL, improved teachers’ ability to apply PBL, enhanced the English reading ability of primary school students, and promoted the reform and development of teaching methods.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".