Optimizing Online Teaching: Total Quality Management in Action for Quality Assurance Measures
Bibliographic record
Abstract
The large-scale online teaching amid the pandemic triggered increasing concern over online teaching management and quality assurance. Take the theory of Total Quality Management (TQM) as guidance, a Chinese higher education institution (CHEI) built a multi-level, multi-link, and multi-dimensional teaching quality monitoring system (Online Teaching Quality Assurance Measures) with full participation, whole process, and all-round development by innovating teaching quality management and monitoring mechanism, aiming to ensure the continuous improvement of talent training quality to realize the sustainable development of application-oriented undergraduate universities with quality improvement as the core. The effectiveness of online teaching quality was demonstrated through the Questionnaire of Student Evaluation of Online Teaching Faculty and students’ academic performance (GPA) before and after the implementation of Online Teaching Quality Assurance Measures, guided by the principles of Total Quality Management theory. The results indicated that Online Teaching Quality Assurance Measures have a series of positive effects on online teaching in CHEI, and systematically guide online instructors as evidenced by outstanding ratings and feedback in course evaluations and students’ academic performance. This study also revealed that CHEI’s online teaching is facing some challenges, especially in the effort to promote learning interaction and teaching cooperation. The study underscored the importance of continuous improvement and provided some interventions in enhancing online educational practices, aligning with TQM principles. The findings are expected to make an important contribution to the field of online teaching quality management in higher education.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".