Critical Success Factors Identified within Moroccan Public Higher Education Institutions
Bibliographic record
Abstract
Total Quality Management is a concept which any educational institution can attain through a variety of critical success factors. This concept is one of the steps necessary to maximize success in improving quality in higher education institutions. Hence, this qualitative study used a semi-structured interview, endeavors to identify and analyze some of the critical success factors which have significant impact in Moroccan public higher education institutions under the framework of Total Quality Management. As a result of in-depth semi-structured interviews with three experienced university professors in selected public universities in the economical capital city of Morocco, this study is contributing to the literature by empirically determining the critical success factors of the TQM mostly needed in the public higher education institutions. The findings of this study bring an understanding of how certain critical factors are perceived in the Moroccan public higher education context. In addition to expand the body of the current literature, the study findings also provide knowledge that can be used as a basis to address the management needs of quality in higher education institutions. In addition, this study is the first of its kind to be addressed in the Moroccan setting / North African region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".