Assessment for the Evaluation of E-Learning and Teaching by the Quality Matter Standards Post COVID-19
Bibliographic record
Abstract
On 18 March, the WHO announced that COVID-19 was a global pandemic, and the Ministry of Health instituted a COVID-19 lockdown. After the period of restrictions ended, blended learning was initiated at the universities. The research aims to evaluate the responses of students, teachers, and quality units in the teaching and learning process by Quality Matter standards. The survey was constructed using a Google form. The research recorded positive data in most of the Quality Matter Standards (QM) in Architecture Engineering Program applied by 95%. Some weakness points were identified 5% and are discussed in this paper. The results by (QM) St1 (2.7/3), St2 (2.6/3), St3 (2.6/3), St4 (2.6/3), St5 (2.6/3), St6 (2.5/3), St7 (2.6/3), St8 (2.7/3). General conclusions are added for teachers to be applied to e-learning education.
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 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 | 0.046 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".