Improving the Quality of Higher Education in Central Europe: Approach Based On GAP Analysis
Bibliographic record
Abstract
A growing social pressure on addressing the issues of quality in administration of educational institutions has resulted in various national and international initiatives focused on development of recommendations and procedures for assurance of quality of education. The topic is getting more urgent in the period of global recession when the impacts of the crisis are experienced even by school graduates who have difficulties to find their place on the labor market. The issue of quality and connection of education with practical requirements ranks among the central topics of a broad discussion in Europe and worldwide. The submitted paper presents results of an investigation of a potential use of a system approach, based on individual gaps identified in a GAP analysis and summarizes recommendations concerning assurance of the lowest possible differences between individual components of the methodology. It is based on decomposition of individual gaps - in the relations between educational facilities, practice and students - into three areas, specifically key questions, interpretation that can be used for university environment and a set of suitable tools for elimination of differences in the given gap. The approach has been adapted for the conditions of university education and the descriptions and explanations of the individual gaps were adapted to increase the potential of improvements based on the application of the GAP analysis.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".