Research on Multi-objective Optimization Algorithm for Optimal Design of Art Education Curriculum Based on Emotional Learning Model
Bibliographic record
Abstract
Teaching curriculum design is centered around the three dimensions of affective attitudes and values, processes and methods, and knowledge and skills, which fit with the affective learning model composed of emotion, learning and cognition.This paper brings affective analysis into art curriculum design and proposes a learner affective model for teaching art courses driven by multiple teaching objectives.Through multi-objective optimization, we give an interactive decision-making method based on a hierarchical affective cognitive model to simulate learners' affective decision-making under multi-objective-driven teaching.Analyze the teaching process of incorporating affective learning strategies in an art course, and examine the interrelationship between affective engagement and learners' knowledge construction in three rounds of learning activities.To analyze the impact of affective learning strategies on students' learning outcomes.The experimental group (affective learning strategy group) significantly outperformed the corresponding creativity abilities of students in the control group in the three components of surprise, originality and challenge after the teaching of the art course, and the affective learning strategy succeeded in stimulating students' creativity.The combination of affective learning model and curriculum design can enhance the effectiveness of art education.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".