A descriptive analysis of the paradigm shift from real to reel classroom during Covid-19 Pandemic
Bibliographic record
Abstract
Medical educational institutes have begun offering online classes in preparation for the COVID-19 pandemic. The course work of MBBS phase-I was completed by an online mode of teaching, but the students' satisfaction feedback is still needed to improve online teaching. After receiving approval from the institutional ethical committee, the feedback from 250 Phase-I MBBS students was collected. The student participation was voluntary and 212 students respond about online-classes feedback on the google form. The data were collected and analyzed in Excel and SPSS software. p-value <.05 was considered significant. The majority of students (90 percent of males and 94 percent of females) use their smartphones to attend online classes, and the majority of students experience network problems often or sometimes; only 6% of students were rarely affected by network issues. Only a quarter of students were satisfied with the online mode of teaching, half were neutral and the remaining quarter were unsatisfied. The internet access issue (p-value=0.101) as well as satisfaction level (p-value =0.985) were not affected by the student residence (urban/rural). The majority of students (62%) prefer face-to-face learning in the classroom, whereas 1/4th prefer watching a live playback video of online lectures and only 1/10th choose live-online sessions. Only one-fourth of students were satisfied with online classes and the majority of students suffer from the quality of internet services. Students prefer face-to-face interactive classroom learning. Students acknowledge the benefits of online teaching with the need for further improvement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".