A Study on the Teaching of Practical English Writing in Senior High School From the Perspective of Memetics
Bibliographic record
Abstract
Nowadays, foreign researches on memes mainly focus on linguistics and pragmatics, while China has gradually begun to explore the relationship between memetics and teaching, but it has not yet attracted too much attention. Therefore, this study will explore the internal relationship between memetics and English writing teaching. The main content of memetics is memes, which refers to “something to be imitated” (Dawkins, 1976). Its main feature is imitation and replication. In foreign language teaching and learning, imitation and reproduction are inevitable. In other words, language learning is to learn from imitation and to accumulate materials in order to gradually reach the level of innovation. The study was carried out in senior high school in Nanchang, Jiangxi Province. There are 106 students in it regraded as participants in this experiment sustaining four months. The research is designed to solve these two issues: (1) Can English practical writing based on memetics improve senior high school students’ writing performance? If so, to what extent? (2) Have students’ attitudes towards English writing changed before and after the memetics based on English practical writing experiment? What are the specific aspects? On the basis of the collected data in this study, SPSS 22.0 will be applied to analyze the results of tests and questionnaires. In line with the results, some conclusions are reached as it follows: (1) By the analysis of the results of tests, English practical writing teaching with memetics can obviously improve students’ writing proficiency. (2) According to the answers of interview and the results of questionnaires, the conclusion can be drawn that after the four-month experiment, students’ attitudes changes a lot.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| 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".