The Application of Cohesion Theory in English Cloze Teaching in Senior High School—A Case Study of Ganzhou Middle School
Bibliographic record
Abstract
Cloze test, a standard language test, can comprehensively reflect one’s language proficiency. However, many students report that cloze filling is more complicated than other question types. Comparing the College Entrance Examination (CEE) in the past decade, discourse analysis ability and understanding of cohesion theory are highly stressed. However, in normal teaching activities, the application of the cohesion theory is unsatisfactory for various reasons. Therefore, appropriate integration of cohesion theory into English cloze teaching in high school has become a significant problem that needs to be solved. This thesis primarily utilizes questionnaires, interviews, and teaching experiments, discussing how to apply cohesion theory in cloze test teaching by analyzing the data using SPSS. The author draws the following conclusions: Most senior high school students lack a sense of cohesion and discourse analysis when solving problems. The application of cohesion theory does effectively strengthen students’ ability to answer cloze filling. This article also provides the following enlightenment for teachers: they should consciously integrate cohesion theory with practice, and guide students to apply theoretical knowledge to solve problems. Moreover, teachers should transform teaching philosophy and methods by keeping up with the times.
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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 | 0.004 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".