Online formative assessment coupled with synchronous online learning: Insight from an Indian medical college
Bibliographic record
Abstract
Objectives: During the coronavirus disease-19 pandemic, majority of the institutions have started distance education. Assessments are also being conducted online. Our question was about the interest of students in assessing their classroom learning by an online quiz. The aim of this study was to observe students’ participation pattern in online anonymous formative assessment immediately after synchronous 1-h online class. Material and Methods: We designed online quizzes with five questions related to the preceding class. In the last quarter of the 1-h class, we shared the quiz with the students. A total of 20 such classes were conducted. We recorded anonymous data on attendance, participation, time of participation, and obtained marks. The data were expressed in mean, standard deviation (SD), and percentage. Chi-square test, t-test, and ANOVA were used according to the data. Results: Among 100 1st-year medical students, average attendance in online classes (62.1 ± 13.5) was lower than the face-to-face 1-h lecture class (80.35 ± 13.01, t-test P < 0.001). Average 55.48% (34.45 ± 7.13) of the attendee participated in the online formative assessment. Approximately, students took 4¼ min to answer the online quiz (minimum 45, median 204, maximum 988, mean 255.76, and SD 154.96 sec). The quiz score was high among the students with 46.73% of the quiz participants scoring full marks. Conclusion: Nearly half of the students attending online classes opted for an anonymous, optional, and online self-assessment quiz. The online quiz is a quick method of formative assessment requiring only few minutes. Further, research should be conducted to find ways to increase participation among the students.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".