Higher education evaluation system based on AHP & EWM
Bibliographic record
Abstract
As the scope of mental work continues to expand and technology continues to develop, people are more and more concerned about the issue of higher education. In order to measure the health status of the higher education system and evaluate the effectiveness of the policy, we establish the Health Evaluation System of Higher Education and the Prediction Model. task 1 In order to better quantify the criteria of health evaluation, we divide the model into four layers through AHP algorithm, select and define 13 fourth-layer indicators, and use Entropy Weight Method (EWM) to objectively calculate the fourth-layer indicators. The Analytic Hierarchy Process (AHP) is used to calculate the third-level weight. According to the population of different countries, we use the complex method of AHP and EWM and scale the indicator to create a scoring mechanism. In task 2 We firstly apply the model to a number of countries to test its suitability, and the results are in good agreement with the education assessment lists published by the United Nations. And we find that India is a country where there is still room for improvement in the education system. task 3 We propose an attainable and reasonable vison for India’s system that supports a healthy and sustainable system of higher education. task 4 According to the scores of India in various indicators and the total score obtained in task 2, we judge that the score of India is unqualified. task 5 We select three of the lowest scores in India’s higher education system out of 13 indicators, and propose targeted policies and a implementation timeline that will support the migration from current state to your proposed state. task 6 We establish a prediction model based on ARIMA, and substitute time series data into the prediction model to obtain the prediction results. Then, we use AHP and EWM algorithms to calculate the first-level targets for comparison, so as to evaluate the effectiveness of the policies. task 7 We reference and analyze the various situations in India and the real world impact of the implementation plan, find that the change is very difficult, and analyze the feasibility of the policy.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".