Enhancing Orbital Physics Learning Performance through a Hands-on Kinect Game
Bibliographic record
Abstract
Practicing is very important in the process of learning physics. Experiencing physics laws and observing the phenomenon in the experiments and labs help students learn. However, some contexts like the law of orbits in physics cannot be practiced directly and students can only learn it from animation or drawings. We have designed a Kinect game for students to experience orbital physics and conducted a pilot in a summer camp of Athabasca University's science outreach program to verify the hypotheses include whether the students' attitudes toward computer/video games will affect their perceptions toward the developed Kinect game or not, and whether their performance in the game will be influenced by the lack of prior knowledge of the law of orbits or not. The quantitative analysis results showed that there was a positive correlation between students' gaming performances and what they knew about the relevant physics knowledge. Also, it shows that the students' attitudes toward computer/video games do not affect their perceptions toward the developed Kinect game in terms of its usability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".