Promoting perspective-taking in astronomy by casting images from a phone or tablet up unto a screen
Bibliographic record
Abstract
Astronomy is a spatial science that requires connecting and comparing different points of view on astronomical systems to understand their complex mechanisms. Textbooks’ illustrations often fail to provide such connections, whereas 3D models of astronomical systems that students can “manipulate” are more conducive to learning. But providing learners with different perspectives simultaneously on an astronomical model can be difficult. One way to achieve this goal is by using a smartphone’s or tablet’s camera to capture the geocentric point of view, and sending the image in real-time via a casting device on a TV monitor or projecting a video image on a screen for all students to see. This way, learners can easily switch from their own “space-based” (i.e., allocentric) perspective on the model to what an observer on Earth (i.e., the view captured by the camera) would see at the same time. In this Best practice paper, presented principally as a resource for educators, we review the relevant literature on teaching astronomy with concrete models and promote classroom activities that use cameras, casting devices and projectors to teach the diurnal cycle, the phases of the Moon and eclipses, the seasons, and planetary motion.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".