An Online Physics Laboratory Delivered Through Live Broadcasting Media: A COVID-19 Teaching Experience
Bibliographic record
Abstract
The COVID-19 pandemic has constituted a sudden educational transformation around the world. It has disrupted instructors, including physics educators, forcing them to adjust to remote teaching. The hands-on laboratory, one of the components of physics instruction, has also had to rapidly go online in all branches of this science, including nuclear physics. In this study, live broadcasting media was designed to conduct a remote nuclear physics laboratory. We then evaluated the immediate impact of this new mode of lab instruction on students’ learning and attitude toward this type of instruction. Fifty-nine 3rd-year physics students at a public university in Indonesia participated in this study. The effectiveness of instruction was examined by analyzing both weekly reports and open-ended responses about students’ learning experiences. In summary, it was evident that live broadcasting media was an effective way to conduct an online nuclear physics laboratory. Accordingly, students’ attitudes demonstrated constructive behaviors about their remote laboratorial experiences. Our findings imply that online platforms are one way to offer the physics laboratory during unanticipated transitions such as the COVID-19 pandemic. Students’ preference for a hands-on laboratory and the technical issues reported during the broadcasting session should be further examined to help design a remote nuclear physics laboratory that is even more accessible and enjoyable.
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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 | 0.003 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".