Design Artificial Intelligence Convergence Teaching and Learning Model CP3 and Evaluations
Bibliographic record
Abstract
In this paper, CP3 model (Converged model of Problem recognition, Plan and Play) is developed to perform the artificial intelligence convergence education as a teaching and learning model for elementary school students. The convergence education was applied to actual classes with five subjects: data collection and analysis, understanding sorting algorithms, understanding sequential structures, understanding repetitive structures, and procedural thinking. When the class was conducted using the CP3 model, the overall score is improved by 41.6% compared to the general classes. There were improvements of 53% of male students and 33% of female students, and male students in the lower grades participates more actively in Artificial Intelligence convergence classes. When the satisfaction of the class with CP3 model is analyzed, the interest level is improved by 83%, the problem-solving ability is improved by 70%, the satisfaction level is improved by 68.5%, the understanding level is improved by 64%, and the expectation level is improved by 68%. The overall satisfaction to the class is very high when the subjects and objects closely familiar in daily life are used due to the characteristics of the lower grade students, and the result is more effective when playable elements are applied. However, for low-grade students, they are still experiencing a little difficulties in classes with complex classes like CP3. Considering the characteristics of low-grade students, simple algorithms with a topic closely related to daily life would make a better result.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".