Research on the Optimization of Civics Content and Practical Path of Western Economics Course in Applied Colleges and Universities Based on Machine Learning
Bibliographic record
Abstract
The combination of the content of Civics and professional courses in colleges and universities is one of the important contents of general education in colleges and universities in recent years.The article introduces machine learning algorithms into this field to explore the optimization path of western economics course civics in colleges and universities.After developing the resources of western economics course civics, the content generation model of western economics course civics is constructed by using the content generation algorithm based on pre-training model and keywordawareness, respectively.Then the text generation performance of the proposed content generation model is examined.The results of the teaching experiments of the experimental group and the control group are compared to explore the effectiveness of this paper's machine-learning-based content optimization and practice path of western economics course civics on improving students' performance.The F1 values of this paper's content generation model on the ROUGE-1, ROUGE-2, and ROUGE-L indicators are 39.06%, 24.79%, and 36.65%,respectively, which is the optimal performance among all models.The students in the experimental group and the control group had the same level of Civics in Western Economics course before the experiment.After the experiment, the two groups produced a score difference of about 5 points on the 8 content dimensions, and the p-values were all less than 0.05.The experimental group's postexperimental performance in course civics were all significantly improved (p<0.05), while the control group basically remained at a level similar to that of the preexperiment (p>0.05).The content optimization and practice path of western economics course Civics based on machine learning can significantly improve the learning effect of students.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".