An innovative model program for teaching educational psychology based on the maximum information entropy model
Bibliographic record
Abstract
This paper carries out a research on the quantitative evaluation of classroom behavior based on the maximum information entropy model, explains the theory related to information entropy and analyzes the concept of information entropy.The improved iFIAS interactive analysis system was used as the main analysis tool, and S-T analysis and time series analysis were used as auxiliary analysis methods to analyze the classroom teaching behaviors.Classroom teaching behaviors were coded and sampled, from which classroom teaching behavior related data were obtained, through which teaching behavior information entropy, redundancy, interaction mode, teaching mode and behavior category frequency were analyzed.After analysis, the overall classroom teaching characteristics of the psychoeducational quality lesson examples are teacher behavior and student behavior as the main, psychoeducational and teaching materials as the secondary, and the teacher's behavior in the early part of the classroom accounts for a high proportion in order to drive the students into the classroom.The proportion of student behavior rises in the middle and late stages.The proportion of hybrid and dialogic teaching mode is 95.83%, which is dozens of times more than the proportion of lecture.It reveals the teaching mode of quality psychology classroom teachers, i.e., focusing on the interaction with students and replacing the pure teacher lecture student acceptance mode with interactive counseling student active learning.The teaching analysis of quality classroom teaching behaviors with the maximum information entropy model realizes the establishment of an innovative model of psychological teaching and clarifies the direction for the future development of psychoeducational classrooms.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".