Exploring Fundamental Aspects and Needs for Developing an Instructional Model to Enhance Attitude and Achievement in Ideological and Political Education: A Mixed-Methods Study
Bibliographic record
Abstract
This study aimed to survey and explore the basic aspects and needs for developing an instructional model to enhance attitude and achievement in ideological and political education(IPE). According to the experimental data results, the current status of attitude and achievement and the factors affecting attitudes are mainly analyzed, and recommendations were made for the reform of the teaching model of IPE education in vocational colleges. The sample consisted of 286 students in the first semester of the 2024-2025 academic year at Sichuan Vocational College of Health and Rehabilitation. A total of 246 valid questionnaires were collected, and seven faculty members and seven student representatives were interviewed. The instruments were College students' attitude scale and interview outlines. The study used both quantitative and qualitative methods. The results indicate that the overall attitude to learning of students in Chinese vocational colleges is “moderate”, the achievement base is in mid-to-lower range, and although the awareness of the meaning of learning has increased, the classroom mood is significantly deficient. Based on the results, it suggests that the traditional instructional model is no longer suitable for IPE. When designing the model, we should combine with constructivism and other theories of suitability to explore the whole process of IPE, adopting a group problem-driven approach and making the classroom return to “student-centered”.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".