Development Approach for Quality Assurance in Higher Education on Area-Based and Community based on the Concept of Management of Educational Institution as the World of New Learning to Promote Students’ Living Valuable Lives
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Bibliographic record
Abstract
The misalignment between educational standards and the needs of students and the labor market often results in education that is underutilized. Thus, enhancing quality assurance in education to achieve learning outcomes that improve the value of life is essential for students to lead meaningful lives and attain true success. This study aims to: 1) examine the current and desirable states and priority needs, and 2) proposed development approach for quality assurance in higher education according to Area-Based and Community based on the concept of management of educational institution as the world of new learning to promote students’ living valuable lives. The respondents were 291 participants from 36 institutions. The results indicated that the overall current state is at a moderate level, the overall desirable state is at a high level, and the overall priority needs index (PNIModified=0.236). The proposed approaches are: 1) establishing policies and standards for data in various disciplines, considering societal trends, economy, environment, and other factors, with a focus on goal-orientation and regular updates both quantitatively and qualitatively; 1.1) developing a comprehensive database with national and international interlinks, 1.2) advancing technologies for teaching and learning management with an emphasis on outcomes, self-adaptation, discovery, and creativity, 1.3) fostering knowledge construction with innovative data; 2) formulating pedagogical policies and strategies that cater to students' potential and interests: 2.1) creating integrated trans-disciplinary courses that reflect learning outcomes, 2.2) developing modern learning resources, 2.3) establishing data collection methods for evaluating outcomes, including the progress and success of both teachers and students; 3) setting policies for supervision and monitoring of data management linked to instructional management and research, with a focus on academic integrity and national context: 3.1) developing an integrated data system for knowledge, innovation, and research that highlights the creation of new ideas for career development, 3.2) ensuring data access for discovering methods to improve instructional management, foster creative innovations, and develop positive career attitudes and societal security.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it