Constructing an Equilibrium Optimization Model of Teaching Strategies between English Teachers and Students Based on Game Theory
Bibliographic record
Abstract
Teachers and students will form a variety of dependent behaviors and interactions centered on teaching activities in the teaching process, thus, the teaching process can be regarded as a typical game process.This paper invokes game theory, takes teacher-student behavioral interaction as the research object, constructs a game model of teacher-student behavior in the process of English teaching, and proposes a teaching optimization strategy for English flipped classroom.At the same time, numerical simulation of the teacher-student game model is carried out to explore the dynamic game equilibrium under the cooperative behavior of teachers and students.The simulation results show that in the teacher-student game network, the strategy choices of teachers and students change over time, and different benefit-loss parameter , additional gain parameter 0 , and cost-saving parameter have a greater impact on the replication of the strategy choice behaviors of the game parties.In addition, the increase of the parameters of the gain PT obtained by the instructor's conscientious instruction, the gain PS obtained by the student's conscientious learning, and the loss KS of the punishment that the student receives for not learning conscientiously are conducive to the promotion of the instructor and the student's strategy evolution towards cooperation (conscientious instruction, conscientious learning), while the increase of the instructional cost CT of the instructor's conscientious instruction and the learning cost CT paid by the student's conscientious learning are not conducive to the promotion of the two parties' cooperation,.And when the proportion of instructors and students initially choosing cooperation is larger, the likelihood of both parties evolving toward cooperation is greater.This paper provides theoretical support for the optimization of English teaching process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".