Deployment of a business intelligence model to evaluate Iranian national higher education
Bibliographic record
Abstract
Higher education plays an important role in the political and socio-economic development of countries. Developing countries experience many significant challenges when it comes to national higher education programs; issues such as financial insecurity, poor managerial practices, and system inefficiencies are some of the obstacles that developing countries have yet to overcome. Resource allocation, technical efficiency, and managerial effectiveness are some of the significant objectives of government national higher education programs for developing countries-including those in the Middle East. The distribution of relevant data sources and the complexity of dynamism in higher education systems allows for an integrated intelligent system with a multi-dimensional view of the current situation to be built. This study proposes a business intelligence-based model to support the monitoring of higher education indicators and enable the forecasting of future trends through the integration of heterogeneous internal and external data sources. In the case study on Iranian higher education indicators, a prototype system was designed and implemented to evaluate the proposed model and its efficiency in practice. After monitoring the indicators using online analytical processing, several indicators were used to forecast trends by time series analysis models. The developed system attempts to provide an integrated view of the Iranian higher education system in comparison with other neighboring countries. The results emphasize that while higher education in Iran, particularly in the area of science and engineering, is a benchmark in the scientific community, the intense level of brain drain is increasing at an alarming rate.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".