“We're going on a virtual trip!”: a switching-replications experiment of 360-degree videos as a physical field trip alternative in primary education
Bibliographic record
Abstract
Field trips are steadily declining due to limited funding, time constraints, safety concerns, and other logistical issues. Many schools are resorting to a virtual field trip (VFT), especially when education is disrupted due to public health concerns, natural disasters, or other unforeseen significant events. Virtual reality as a common form of VFT is likely not an option for many schools due to cost and other barriers. The purpose of our study was to explore the potential of going in a VFT using 360-degree (360°) videos as an alternative to a physical field trip in primary education. We recruited third-grade pupils (aged 8-9) from two private elementary schools to experience VFTs using 360° videos (360V) and regular videos (REGV). Using a switching-replications experimental design, we compared their content recall (assessment tests) and VFT experience (attitude, perceived usefulness, involvement, inquiry, video engagement, and virtual guide) across four-time points. Our results show that the increase in content recall scores of 360V groups after VFTs was consistently higher compared to REGV groups at all time points, although it was only significant in one quarter. We also found pupils' video engagement, involvement, and attitude as significant factors in their VFT experience. These results call attention to a possible implementation of VFTs and continue the long-standing tradition that has been acknowledged as a student-centered, interactive instructional method.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".