Research on the Integration and Evaluation Method of Intelligent Interaction Design and Visual Communication Design Education Content Based on Fuzzy Logic Reasoning Algorithm
Bibliographic record
Abstract
The effective integration of intelligent interaction design and visual communication design education is an important attempt to improve the educational effect.This paper firstly constructs the evaluation system for the teaching effect of intelligent interaction design and visual communication design courses, and then establishes a set of evaluation models based on fuzzy logic inference algorithm.In the example application part, the G1-entropy weighting method designed in this paper is used to measure the weights of each influence index, followed by an empirical study using School A as an example, and finally the multiple linear regression analysis is used to make further exploration on the influencing factors of the teaching effect of the course.The study concludes that in the subjective weight calculation experiment, it is found that the weight of external influences accounts for the highest proportion of 0.277, that is, experts believe that the overall planning has a strong influence on the course effect.Further, the regression modeling yields that learning interest, curriculum, faculty, teaching content, and practical activities have significant positive correlation with teaching effectiveness.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".