Strategies to Improve Higher Education Institutional Performance Through Predictive Analytics Implementation
Bibliographic record
Abstract
Higher education institutions face challenges in integrating predictive analytics due to data silos and resistance to adoption, limiting their ability to improve student outcomes and operational efficiency. This issue concerns institutional leaders, as ineffective data use can lead to declining retention rates, financial strain, and missed opportunities for growth. Grounded in the composite conceptual framework of the technology acceptance model and the diffusion of innovations theory, the purpose of this qualitative pragmatic inquiry study was to explore effective strategies to implement predictive analytics tools to reduce costs and improve student outcomes. The study included three higher education leaders in Ontario with experience using or managing predictive analytics. Data were collected using semistructured interviews and publicly available documents. Through thematic and template analysis, four themes were identified: (a) challenges in integration, (b) stakeholder alignment, (c) practical applications, and (d) institutional support. A key recommendation is for higher education leaders to implement targeted training programs that enhance stakeholder engagement, ensuring faculty and administrators can effectively utilize predictive analytics for informed decision-making. The implications for positive social change include the potential for higher education leaders to leverage data-driven insights to improve student retention and promote equitable access to educational resources, fostering more inclusive and effective learning environments.
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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 | 0.050 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".