The Path of Building Curriculum Resources of Adult Colleges and Universities Based on MOOC in the Intelligent Era
Bibliographic record
Abstract
Curriculum resources are the basis for ensuring the implementation of the curriculum, theirs suitability and richness affect the achievement of the curriculum teaching goals. They are an important guarantee for achieving the curriculum teaching goals. The application of artificial intelligence technology in the field of education has triggered profound changes in teaching and learning. In the intelligent era, how to build a massive adult education learning resource library that can meet personalized needs has become an important topic and development direction of learning resource construction. The construction of adult colleges and universities curriculum resources based on MOOC is an effective means of promotion for teaching and learning. This paper analyzes the defects in the construction of traditional curriculum digital learning resources in adult colleges and universities, the advantages of the construction of curriculum resources based on MOOC in adult colleges and universities, the principles and paths of the construction of curriculum resources based on MOOC, in order to quickly create the exclusive curriculum resources suitable for adult colleges and universities and in line with the characteristics of adults, make better use of its supplementary teaching and supplementary learning, effectively improve the quantity and quality of adult education resources construction, and promote the development of adult education and teaching.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".