Innovation of College English Literature Education Model under the Internet Background
Bibliographic record
Abstract
In the mode of college English education, literature education has not been regarded as one of the priorities. Because literature education has high requirements for college students' English level, but the practical application effect is not obvious, so most colleges do not pay much attention to English literature education. However, literature education plays a pivotal role in English teaching. In university English teaching, consideration should be given to the development of students' ability to use the language, as well as to the improvement of their cultural literacy. With the increasing comprehensive national strength of the country and the rapid development of information technology, the application of information technology to college English literature education from the perspective of "Internet +" can fully reflect the teaching ideas of the new curriculum standards. It can effectively implement the dominant position of students and teachers, realize the sharing of educational and teaching resources, enrich the teaching content, and stimulate students' interest in English reading through a variety of teaching methods. This paper briefly discussed the new model of English literature teaching in the network environment. From the perspective of "Internet +", it explored a new learning method suitable for contemporary college students, so as to promote the rapid development of English literature teaching. Regarding the innovation of college English literature education model, more than 86% of teachers believe that innovation can be made in teaching content, teaching methods, teaching concepts, and teaching subjects, and students can obtain relevant teaching videos, audios, pictures and other materials through the Internet platform.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".