A Proposed Measurement Instruments for Total Quality Management Practices in Higher Education Institutions
Bibliographic record
Abstract
The purpose of this paper is to provide a comprehensive Total Quality Management (TQM) practices measurement instruments in Higher Education Institutions (HEIs) based on previous studies. HEIs just like other industries are facing challenges in order to survive. Today, quality has become important and a must for every marketable product or service due to the business world becomes more and more complex and competitive. In this context as a management process, TQM has been accepted to cope with the changes in market environment and to focus on continuous quality improvement. Many authors believed that the principles of TQM can contribute to the continuous improvement of HEIs. This paper provides a measurement instruments for TQM practices that emphasis on continuous improvements for quality measurement in HEIs. This instrument is based on a comprehensive study of previous studies of TQM practice measurement in education. Analysis focuses on customer orientation, continuous improvement, and employee engagement at all levels. This paper proposed nine dimensional measurement instruments that can be used as self-assessment in HEI. These nine dimensions are: leadership or top management commitment; strategic planning; customer focus and satisfaction; measurement, analysis, and knowledge management; human resources management; system and management processes; course delivery; campus facilities; and benchmarking and partnership. Measurement instruments were selected based on number of dimensions used from previous study and customers’ perception on dimensions of quality, their rating of importance and their overall evaluation of the service provider.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".