Exploring Intersections and Integrations: Advancing Equity in Educational HR Analytics
Bibliographic record
Abstract
In the realm of education, fostering equitable systems that promote student success necessitates preparing HR MBA students, administrators, and faculty to engage effectively with data and analytics. This research examines the intersection of HR analytics and educational equity, focusing on identifying and addressing gaps in the practical application of People Analytical Platforms (PAPs) within educational settings. Central to this inquiry is the concept of 'multimodal inclusiveness,' which emphasizes the adoption of practices that recognize and accommodate diverse modes of communication and interaction inherent in educational contexts. By fostering equitable engagement with analytical tools, this approach seeks to empower stakeholders to collaborate effectively and inclusively. This study employed a primarily survey‑based approach to explore how HR professionals engage with data and analytics in educational settings. An online questionnaire was completed by 192 participants—148 university administrators (mostly from Canadian and U.S. HR MBA programs), 18 alumni/current students, and 26 faculty and lecturers. Using survey analysis and thematic exploration, the study investigates how HR education programs prepare students to leverage analytics for fostering inclusivity, promoting ethical practices, and challenging inequities in education. By bridging the gap between theoretical knowledge and practical application, this research aims to equip future HR professionals with the tools and competencies needed to drive systemic change, enhance organizational accountability, and prioritize inclusivity. Ultimately, the findings underscore the transformative potential of HR analytics in shaping equitable, ethical, and inclusive practices within educational institutions, advancing both student well-being and institutional success.
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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 | 0.052 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.030 | 0.050 |
| Open science | 0.002 | 0.045 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".