Research on the Innovation of Computer Basic Education Curriculum in Fine Arts Colleges and Universities Based on Network Education Model
Why this work is in the frame
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Bibliographic record
Abstract
The development of time and technology has made the traditional basic computer teaching unable to meet the needs of current students, and the cultivation of computer thinking ability has become a hotspot of computer education concern. The article combines the flipped classroom model with the MOOC platform of network education, and establishes a MOOC flipped classroom teaching model applicable to computer basic education courses in art colleges. In this model, students’ assessment results data are collected, and the Apriori algorithm optimised by matrix optimisation and prior pruning strategy is used to mine the association relationship of the assessment results, which helps teachers to understand students’ computer knowledge mastery. T College of Fine Arts is used as a research example to illustrate the effectiveness of MOOC flipped classroom through the changes in students’ performance, competence, and satisfaction. The improved Apriori algorithm has an execution time of only 2.93s when its minimum support is 100%, which can be used to understand students’ computer application ability for different question types, majors and skill performances. The mean score of the final exam of the students in the experimental class was 82.79, which reached 111.79% of the score of the control class, and more than 80% of them were satisfied with the MOOC flipped classroom. The use of flipped classroom and network education model can achieve the innovative development of computer basic education courses in art colleges and help to enhance the teaching quality of computer basic education courses in art colleges.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it