The Role of Artificial Intelligence Algorithms in English Education in Colleges and Universities and Their Teaching Strategies
Bibliographic record
Abstract
The rapid development of arti icial intelligence algorithms makes them play an important auxiliary role in college English education.This paper deeply analyzes the application of arti icial intelligence algorithms in English education in colleges and universities, and constructs a method of analyzing students' behavior in college and university English classrooms with computer vision as the algorithmic representative, which assists teachers to understand the state of students in the whole classroom.YOLOv7 network carries out multi-target detection in the classroom and improves the network for the de iciencies in the classroom environment.The improved K-means algorithm is then introduced to improve the DeepSORT multi-target tracking algorithm.Obtain the surveillance video data in the English classroom of Q college and build the dataset by itself, and design different experiments to verify the effectiveness of this paper's algorithm respectively.Finally, the classroom behavior analysis method based on computer vision in this paper is applied to teaching practice to explore the practical application effect of the method.The results show that the improved method of this paper can signi icantly improve the performance of the target detection and tracking model, and the application of this paper's method to the classroom time can accurately capture the classroom state of different students, and assist teachers in formulating different teaching strategies according to different classroom stages.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".