The Research on the Differences Between Male and Female Middle School Students in English Learning: A Case Study in Xiangning No.2 Middle School
Bibliographic record
Abstract
This article mainly focuses on the differences between male and female middle school students in English learning. In order to clarify whether there are gender differences in the middle school student’s English learning and seek out in what ways the concrete differences are showed, the author adopts two methods to investigate and analyze the differences: analysis of students’ exam results and questionnaire analysis. Taking Xiangning No.2 Middle School as an example, first, the author compares of students’ English Graduation Exam results and other subjects Graduation Exam results from 2015 to 2017; then the author investigates and analyzes the results of students’ English Entrance Exam and English Graduation Exam from 2015 to 2017. According to the test scores, the author confirms the fact that there are significant differences between male and female middle school students in English learning. What’s more, the author carries on a questionnaire survey among 180 students of Xiangning No.2 Middle School. From the analysis of questionnaire results, the author finds that the concrete differences are showed in five aspects: learning motivation, learning attitude, learning interest, the attitude towards exam and learning methods. Finally, the author puts forward some effective learning methods and strategies for male and female students to improve their English ability. Gender differences have a great effect on middle school students’ English learning, so English teachers should teach students in accordance with gender differences. Thus, male and female students will have a better development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 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) | 0.004 | 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".