Managing constraints in teaching and learning in higher education in Oman: understanding market orientation and quality service delivery
Bibliographic record
Abstract
The main thrust of this research is to contribute to theory building in the Theory of Constraints (TOC) in logistics management and its applicability in Oman Higher Education Institutions (referred to as HEI right through), with focus on colleges and universities. One of the two major theories of higher education said a blend of theory and practice was the only way to get quality graduates in HEI. The research will explore gaps in knowledge regarding these theories as a contribution to knowledge. Logistics will be looked at in this research as a support function for marketing strategy. Market orientation is a company philosophy focused on discovering and meeting the needs and desires of company or organizational customers through its products mix, and in this instance HEI will be expected to meet foremost the needs of industry and government as employers of graduated students, then the needs of students and society. Axtell quoting Lombardi argued that the quality of university research drove the quality, breadth, and depth of the undergraduate curriculum and that teaching delivered the state of current knowledge while research pursued knowledge at the boundaries of our current understanding, (Axtell, 2016:3510). Field research would shed light on research-teaching nexus in Oman compared to the GCC and the world at large. This will be an intensive in-depth single case study, with Oman being the case. This research paper focused the management of constraints in higher education teaching and learning in Oman and understanding market orientation and quality service delivery. This paper is constructed from the researcher’s PhD thesis as a way of disseminating critical new knowledge on higher education in Oman and globally, for the betterment and benefit of academia and society.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".