Decision-making system for higher education university selection: comparison of priorities pre- and post-COVID-19
Bibliographic record
Abstract
Purpose University selection in higher education is a complex task for aspirants from a decision-making perspective. This study first aims to understand the essential parameters that affect potential students' choice of higher education institutions. It then aims to explore how these parameters or priorities have changed given the impact of the COVID-19 pandemic. Learning about the differences in priorities for university selection pre- and post-COVID-19 pandemic might help higher education institutions focus on relevant parameters in the post-pandemic era. Design/methodology/approach This study uses a mixed-method approach, with primary and secondary data (university parameters from the website and LinkedIn Insights). We developed a university selector system by scraping LinkedIn education data of various universities and their alumni records. The final decision-making tool was hosted on the web to collect potential students' responses (primary data). Response data were analyzed via a multicriteria decision-making (MCDM) model. Portal-based data collection was conducted twice to understand the differences in university selection priorities pre- and post-COVID-19 pandemic. A one-way MANOVA was performed to find the differences in priorities related to the university decision-making process pre- and post-COVID-19. Findings This study considered eight parameters of the university selection process. MANOVA demonstrated a significant change in decision-making priorities of potential students between the pre- and post-COVID-19 phases. Four out of eight parameters showed significant differences in ranking and priority. Respondents made significant changes in their selection criteria on four parameters: cost (went high), ranking (went low), presence of e-learning mode (went high) and student life (went low). Originality/value The current COVID-19 pandemic poses many uncertainties for educational institutions in terms of mode of delivery, student experience, campus life and others. The study sheds light on the differences in priorities resulting from the pandemic. It attempts to show how social priorities change over time and influence the choices students make.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".