Comprehensively Evaluating Higher Education in North America Based on a Weighted Hierarchical Indicator Model: Specific to Different Study Abroad Students
Bibliographic record
Abstract
Receiving higher education abroad has become a promising way for international students to increase competitiveness. Despite the global outbreak of COVID-19 in recent years, still a large number of international students are inclined to study abroad, especially regarding North America as their first choice. University rankings recommended by distinct institutions are commonly considered as a useful guide to evaluate the quality of higher education, which is critical for international students to determine the target university for their further study. However, major problems identified in existing university ranking systems include insufficient integration of potential facets, weak measurement and quantification, and lack of taking personal demands and preference into account. To tackle these challenges, this study proposed an integrated conceptual model based on a hierarchical index system for comprehensively evaluating higher education in North America. This model attempts to improve the current university ranking philosophy by incorporating both subjective and objective weights using statistical and geospatial techniques, providing a theoretical basis for comprehensive evaluating higher education in North America as well as a personalized guide of selecting universities for different international students. Finally, results were effectively visualized on an interactive web-based platform with users' personalized preference as the input weights.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".