Practical Exploration of University English Teaching Reform in the Information Technology Era Based on Cognitive Mapping Construction
Bibliographic record
Abstract
In the era of information technology in education, accurate analysis of individual characteristics becomes the key to personalized learning and tailored teaching, which is of positive significance to the exploration of teaching reform paths.This paper constructs a cognitive map of college English courses under the guidance of cognitive theory, and establishes a reform model of college English teaching in combination with the cognitive map, so as to realize students' self-knowledge and cognitive construction in the teaching process.The idea of fuzzy set theory is used to quantitatively analyze the knowledge ability level of college students, and then the Logistic model and Bernoulli distribution function are used to calculate the students' cognitive level of mastering each knowledge point and their scores of answering the questions in the college English course.The analysis of the effect of the teaching model after practice found that the students' mastery and cognitive level of subjective and objective knowledge points in the college English course were significantly improved and higher than the ideal reference value.The correct rate of answering composition questions in subjective questions increased by 30.44% compared with that before the teaching mode was carried out.The informatization teaching mode proposed in this paper lays a foundation for the teaching reform of college English and provides an effective path for students to improve their knowledge mastery and cognitive level.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".