Optimization of College English Classroom Teaching Process from the Perspective of Educational Psychology Based on Multi-feature Blended Learning
Bibliographic record
Abstract
In the traditional English teaching process, instructors tend to conduct teacher-centered classroom activities. This is convenient for teachers to play a leading role in the teaching process, and effectively organize and control the entire classroom instructional process. The initiative of students is neglected and the creativity and imagination of students is limited, which cannot focus on student individual differences in accordance with their aptitude. As a result, the use of the computer-assisted learning should be paid more attention. Compared with traditional classroom learning, online learning not only has no time and space constraints, but more importantly, students can choose their own learning content and learning according to their own conditions during the online learning process, in order to give full play to the initiative of students in learning. This study combines the practical teaching of the English grammar course of educational psychology, focuses on the correlation between English classroom teaching and learning level, major, and gender. Data will be collected, organized and analysed by mathematical statistics. The optimization process is designed by mixing multiple features and multiple methods. The results indicate that only 26% of freshmen believe that educational psychology has played a significant role in English teaching, while 72% of seniors believe that educational psychology is very important for learning English. It can be seen that teaching English to students from the perspective of educational psychology is very important for students. Meanwhile, it can be said that the blended learning mode is a new and optimized learning method under the background of e-learning, which can effectively improve the current English classroom teaching effect at tertiary 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".