Virtual reality platforms for K-12 STEM education
Bibliographic record
Abstract
Providing K-12 students with proper science, technology, engineering, and math (STEM) education is important to ensuring an innovative and prosperous economy. A highly skilled STEM workforce can lead to increased productivity and competitiveness, which can lead to a host of new ideas being researched and developed. STEM workers make added-value products, build bridges and roads, and conduct lifesaving medical research, among other important activities. The use of virtual reality (VR) technology for both education and workforce training has grown in recent years. VR technology can accelerate these processes at maximum efficacy and minimum costs and can have a significant impact on productivity gains, earnings, new jobs, innovation through research and development, and high-growth industries. This paper presents the development of a series of VR modules using the Unity game engine, the HTC VIVE Pro VR headset, and the Hi5 VR glove for the purposes of K-12 STEM education. Specifically, these developed modules have been designed to instruct K-12 students on topics related to motion and heat, with future goals to expand the modules to cover topics related to light, magnetism, electricity, radioactivity, sound, and waves. This paper will cover the methodology and design considerations that went into developing these modules, with a focus on how these modules relate to various learning strategies as well as with existing research on the use of VR in K-12 education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".