Research on the Path of Enhancing the Effect of Digital Educational Resources Integration Strategy on Civic and Political Education of College Students Based on Multidimensional Data Fusion and Optimization Algorithm
Bibliographic record
Abstract
Influenced by the backward management methods and other factors, the integration and sharing of digital educational resources in colleges and universities have certain problems, and cannot give full play to the advantages of digital educational resources in colleges and universities.Based on this, this study proposes a targeted digital education resources integration strategy, using particle swarm algorithm to optimize the sorting of digital education resources, to obtain high-quality digital education resources, introducing fuzzy clustering algorithm and combining with the principle of decision tree, to accurately classify and integrate digital education resources.On the basis of realizing the integration and classification of digital educational resources, a digital educational resource sharing model is formed to promote the effective use of digital educational resources.The digital educational resources integration strategy proposed in this paper is adopted to carry out the application practice of digital educational resources integration and sharing in S colleges and universities.The mean values of the three dimensions of students' learning attitude, teachers' teaching, and teaching effect in S colleges and universities reached 3.48, 3.97, and 3.74, respectively, and this paper's digital educational resources integration strategy method has a positive positive impact on the dimensions of students' learning attitudes, teachers' teaching, and teaching effect in Civic and Political Education in S colleges and universities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".