The Effect of COVID-19 CORONA VIRUS on Sustainable Teaching and Learning in Architecture Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
On 11 March, the World Health Organization (WHO) announced that the COVID-19 outbreak became a global pandemic. The governments have been implementing measures to limit the number of people congregating in public places. Therefore, the Ministry of Education stated that all educational institutes should complete the 2019-2020-2 semester using online video conferences and virtual classes. The aim of this research is to study the effect of COVID-19 on teaching and learning during the last three months of lockdown after shifting to virtual classes. The research study the procedures applied by the College of Architecture Engineering in Dar Al Uloom University. The Adding value is improving the E-Learning process for the upcoming semesters and solving the negative points for a better education. To achieve this objective the researcher, distribute a survey to the students to scale their experience and record the positive points, and to find a solution to the negative points to solve these problems. The outcome of the research showed a good experience and many recommendations to be applied in the coming future.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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