Enhancing the Quality Assurance of Fashion Technology Courses in India: A Comparative Study between Educators and Industry Professional
Bibliographic record
Abstract
Higher education institutions are entrusted with the herculean task of ensuring quality in teaching and learning process of students. It is important that these institutions establish and maintain a proper quality assurance system so that they can offer quality services to their key stakeholders. Fashion Technology courses in India are undergoing drastic transformations in the recent years to keep pace with developments in industry and market conditions. The expectations from the stakeholders has increased manifold and it is the responsibility of the education leaders related with Fashion Technology courses to ensure higher quality in educational offerings. The purpose of this work is to evaluate the importance of different factors in ensuring quality in fashion technology courses. From the extensive review of literature on quality assurance parameters in higher education, ten unique factors were identified. Data were collected from 330 faculty members and 280 industry professionals from fashion design technology industry. Quality assurance parameters like Resources (Students, Faculty, Infrastructure), Education Management, Instructional Design and Delivery, Assessment and Evaluation, Student learning outcomes, Learning Experiences, Professional Attributes, and Skill Sets were considered in this study. Statistical measures like relative importance index (RII), t-test, correlation analysis, etc. were used to compare the perception of educators and industry professionals. The study highlights the importance of different factors in promoting quality assurance in fashion technology courses. The findings has several implications for educators to focus on enhancing quality assurance in higher education in general and fashion technology courses in particular.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".