The Research on Learning Motivation and Factors for Glass Art University Student in China
Bibliographic record
Abstract
This study investigates the learning motivation of glass art students in China, focusing on the unique factors that drive their academic engagement. The research employs a comprehensive literature review and surveys conducted with students from Chinese art academies. Two questionnaires were developed through item and factor analysis: one assessing learning motivation and another examining influencing factors. The learning motivation questionnaire identifies three core components: "Passion for Art," "Future Work and Life," and "Negative and Coping." The questionnaire on influencing factors also reveals three key components: "Interest and Hobbies," "Social Environmental Impact," and "Influence of Traditional Culture." Survey results showed high internal consistency and structural validity for both questionnaires. The findings highlight that passion for art is the highest motivational factor, followed by future work and life considerations. Social environment and personal interests significantly influence learning motivation. However, knowledge-seeking and goal-setting motivations were relatively low due to the subjective and experiential nature of art majors. This emphasizes the need to integrate humanities education and reinforce students' emotional connections with their art practice, aligning learning goals with practical applications and societal expectations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".