Effects of 3DS MAX Software on Creative Development and Skills in Art Education at Guizhou University, China
Bibliographic record
Abstract
This study investigates the impact of 3DS MAX software on creative development and skills in art education at GUIZHOU UNIVERSITY, China. As digital technologies become increasingly integral to art education, understanding how 3D animation software influences students' creative capabilities and technical skills is crucial for developing practical pedagogical approaches. The research objectives were to (1) explore the role of 3DS MAX in enhancing students' creative abilities,2) evaluate the implementation of 3DS MAX in art curricula, and (3) analyze student feedback on software usage. The study employed a quantitative approach with pretest and post-test design, using a sample of 45 first-year digital media art design students selected through cluster random sampling. Research instruments included lesson plans covering five key areas (Introduction, Modeling, Texturing, Lighting, and Character Rigging), a 30-item multiple-choice test, and a 25-item questionnaire measuring five dimensions of creativity (flexibility, fluency, elaboration, problem-solving, and artistic expression). Statistical analysis utilized descriptive statistics and paired t-tests. Results showed 1) significant improvements in student performance, with mean scores increasing from 75 to 79 and standard deviation decreasing from 6 to 5, and 2) the results indicate that students' mean scores after learning were significantly higher. 3). Student feedback revealed moderate to positive responses (overall mean 2.99, SD=1.42), with the most significant improvements in problem-solving (M=3.09) and elaboration (M=3.26), while artistic expression showed room for enhancement (M=2.83).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".