Quality in Higher Education: Defining the Conceptual Contents and their Relative Predominance
Bibliographic record
Abstract
The purpose of this study is to investigate the conceptual content of the term “quality” in higher education, as it emerges from the descriptions and discussions of authors, researchers, and experts in 22 scientific publications. The analysis of the qualitative data is based on the methodology of grounded theory. From the analysis, 21 major dimensions or characteristics of quality in higher education emerged due to their high frequency of occurrence, were subdivided into five broader categories: “learning environment”, “learning content”, “processes”, “students”, and “teachers”. According to the main findings, from the "learning environment" category, the dimension concerning psychosocial elements predominated in the literature. From the category "learning content", two dimensions prevailed (student-centred teaching and learning) and the dimension concerning taking an interest in and caring about students. From the "processes" category, the dimension concerning assessment prevailed. In the category "students", the dimension of improved learning outcomes was the most frequently observed, and finally, from the "teachers" category, two dimensions prevailed over the others, one concerning pedagogical skills and the other termed skills: emotional, management, reflection.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".