Research on the Construction and Application of a Blended Teaching Evaluation System for Public Finance under the “New Economics and Management” Strategy: Based on the TOE Analytical Framework
Bibliographic record
Abstract
Driven by the digital education strategy, the field of higher education is undergoing accelerated transformation. As a core course in the field of finance and economics, the teaching model of public finance urgently needs innovation to meet the requirements of the "New Economics and Management" (NEM) strategy for cultivating high-quality professional talents. However, the current evaluation system for public finance teaching in higher education institutions faces issues such as formalism and oversimplification, which fails to meet the demands of the digital age. This study takes the public finance course at Anhui University of Finance and Economics as the research object and constructs a blended teaching evaluation system integrating "technology empowerment, organizational collaboration, and environmental adaptation" based on the Technology-Organization-Environment (TOE) analytical framework. The system adopts a combination of multi-agent evaluation and process-oriented evaluation to comprehensively assess teaching effectiveness from three dimensions: technology, organization, and environment. The practical results show that this evaluation system can effectively enhance students' learning outcomes and the achievement of teaching objectives, providing a scientific basis for the continuous improvement of public finance teaching quality. In addition, the study proposes optimization strategies, including technological upgrades, teacher training, and policy support, aiming to offer theoretical and practical references for the teaching reform of finance and economics disciplines under the NEM strategy.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".