A Convolutional Neural Network-based Interaction Behavior Analysis and Creativity Evaluation System for Programming Games for 0-6 Years Old Children in Educational Big Data Environment
Bibliographic record
Abstract
In this study, a multimodal analysis framework based on GCN is constructed to address the needs of interaction behavior analysis and creativity assessment of programming games for 0-6 years old children.A stack noise reduction self-coding neural network is used to recognize human gestures in images, and the feature representation of interaction behaviors is realized based on GCN, and the effectiveness of the method is proved by the experimental results on the two-player interaction behavior library.Construct a creativity evaluation system applicable to programming game scenarios for young children, and recruit 80 students aged 0-6 years old to carry out experiments.The students were classified using the GCN-based interaction behavior analysis model, and the weights of creativity evaluation indexes were determined by AHP.The fuzzy comprehensive evaluation method was used to evaluate and score the factors of creativity of the three categories of students, and the test results were verified with the help of the gray correlation method.The comprehensive evaluation scores of the three types of students are 2.006, 3.507 and 5.026, respectively, in which the creativity level of excellent learners is the highest and reaches the excellent grade.The normalized gray comprehensive correlation vector (0.3224, 0.3727, 0.3049) is close to the AHP weight vector (0.328, 0.357, 0.315) with a good assessment effect, and the research results provide a new technical path for behavior analysis and creativity development assessment in early childhood programming education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".