A Preliminary Study on the Teaching Model of College English Reading: Fragmented Reading
Bibliographic record
Abstract
English reading is of vital significance in English learning. In proficiency tests such as CET-4, CET-6, IELTS and TOFEL, extensive reading accounts for a large proportion in scores. Those who do well in reading tend to get higher scores. However, the current English teaching situation doesn’t show its significance: the in-class time is limited with only two classes each week, and it’s difficult to decide appropriate textbooks for teachers. As a result, students can’t figure out the significance of extensive reading course and think little of it. Since the Internet appears in every aspect of our daily life, we can combine extensive reading course with the Internet. By using our spare time to reading fragmented information in English from the Internet selected and instructed by the teacher in any place, we bring fragmented reading into extensive reading teaching. In this paper, the author aims to assume a new teaching model concerning fragmented reading in details. Through this new teaching model, students can not only read English materials in classroom, but also after class in any place with their spare time. With much more input of English both in and out of class, their reading ability will be improved and they are more ready to deal with proficiency tests of all kinds. Meanwhile, students’ interests will be aroused and their horizons will be broadened, which are also helpful for students to pass all kinds of proficiency tests.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".