Leading institutional policy implementation: Negotiating the complexities of policy implementation in higher education in the UAE.
Bibliographic record
Abstract
While it is widely understood that assessment policy and its implementation by actors profoundly affect the quality of student learning in higher education, there is a dearth of research highlighting the institutional factors that influence policy implementation in today’s globalized world. Although leadership is an often-cited factor influencing policy implementation, it is not well understood in the United Arab Emirates (UAE) and the Middle East. This paper discusses a qualitative case study that explored how leadership negotiates institutional factors and influences actors’ implementation of assessment in a Health Sciences department in an institution in the UAE. Adopting Hans Bresser’s (2004) Contextual Interaction Theory (CIT) as an empirically-based conceptual framework, the case study examined how institutional factors and leadership influence motivation, cognition, and power/capacity in a UAE institution. Data were collected from semi-structured interviews with key informants in a Health Sciences department and internal and external policy documentation. Findings indicated that the policy design and the institution’s top-down approach to governance influenced leaders’ implementation of assessment policy in particular ways. In addition, the institutional culture of change and the sizable multi-campus structure impacted the department's policy and leaders’ assessment implementation. Finally, there were findings on the nature of leadership and the nuances of supporting and influencing policy implementation that was contextualized in UAE society. The study results offer policymakers, institutional leaders, and department-level leaders (department and program leaders) a deeper understanding of how system-level influences impact policy implementation.
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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 | 0.040 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.025 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".