Quest for a New Instrument for Measuring Academic Program Quality
Bibliographic record
Abstract
This research explores and confirms a new way of measuring the quality aspect of an academic program based on hospitality education. In our opinion, there is a growing demand for specialists in hospitality education in India. The fact that the number of hospitality education institutes is increasing doesn’t go hand in hand with the care for the quality of education. Hence, we present one set an alternative trajectory by offering a new instrument APQUAL for measurement of quality of hospitality program offered by an educational institute. A critical review of the literature on the major instrument for measuring higher education quality has been done. The empirical part of the paper presents the developed in eight steps APQUAL construct that is an effective instrument to analyze the academic program quality. This work explored a number of facets of program quality by employing EFA (exploratory factor analysis) and CFA (confirmatory factor analysis) to fit the first order nonrecursive model and calculated the reliability and validity of our proposed instrument. This research provides a valid measure of academic program quality, which can be also applicable at the micro-level.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".