FORMATIVE ELECTRONIC ASSESSMENTS DURING COVID-19 LOCKDOWN IN SECOND PHASE MEDICAL UNDERGRADUATES
Bibliographic record
Abstract
Objective: Electronic learning and assessment was embraced in medical education worldwide following the COVID-19 pandemic. This study was done to determine the perceptions of medical undergraduates on formative electronic assessments conducted during COVID-19 lockdown and to estimate the mean marks scored in these assessments. Methods: This was a descriptive study done for a period of 3 months on online platform. Six online formative assessments were conducted on Google Forms or Kahoot. A questionnaire was administered as Google Form to collect the perceptions of the participants on perceived ease of use of platform, attitude, and practice adopted in online assessments. Data were analyzed using SPSS 16. Results: The response rate was 97.7%. Kahoot was perceived to be easier with lesser technical glitches and privacy concerns as compared to Google, while it was equivocal in terms of access assessment links, output storage, display clarity, overall user interface, network issues, need for computer literacy, and eyestrain caused. The participants had a positive attitude regarding the usefulness of online assessments however majority liked the traditional assessments more than the online assessments. While less than one-third (22%) had copy pasted some answers, more than half (54.4%) had referred to internet and more than three quarter (79.6%) had referred to textbooks/power points/notes while attending online assessments. Conclusion: The participants felt that Google Forms and Kahoot were comparable online assessment tools except for difficulty in filling, privacy concerns, and technical issues on Google Forms. The usefulness of online assessments was embraced by the participants but they felt that the traditional assessments were to be continued, while attending online assessments some students had referred to the internet or study materials.
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How this classification was reachedexpand
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".