The Use of a Portable Virtual Reality System as a Supplementary Educational Tool in a First‐Year Human Anatomy Course
Bibliographic record
Abstract
Significant advancements in consumer‐grade virtual reality (VR) systems are providing educators with new tools to interact with students. Prior to 2016, VR systems were cost‐prohibitive, limited in software selection, and complex to use. The Oculus Rift , first introduced in 2013, revolutionized the VR field by bringing forth the first affordable six‐degrees of freedom (6‐DoF) commercialized VR system. A functional 6‐DoF VR system consists of the wearable headset, two hand controllers that allow users to interact with objects in the VR space naturally, external sensors to track movements, and requires a costly VR‐ready computer system to render the graphics. In the fall of 2017, a strike affecting all colleges in Ontario limited the ability to deliver onsite lectures for five weeks. During the strike, innovative ways of lecture delivery, such as VR lectures, were successfully implemented to meet the course objectives of a first‐year dissection‐based human anatomy course. However, a notable limitation of VR technology at the time was its need to be tethered to a computer, which confined its use to a small, pre‐defined work area. The limited accessibility of the technology led to a higher amount of time and effort required to create VR‐based educational content. Recent advancements in VR technology led to a new, all‐in‐one 6‐DoF VR system. The Oculus Quest , released in May 2019, incorporates an on‐board computer, built‐in camera sensors, and an on‐board battery. The combination of these features resulted in a powerful, affordable, and portable system that can be used anywhere, anytime, with ease. The Oculus Quest was incorporated into the Fall 2019 first‐year human anatomy course. Videos re‐enforcing course lectures and major anatomical concepts, such as abdominal blood supply, the brachial plexus, and blood supply to the upper limb, were created and distributed digitally to students. Additionally, new software features allow the instructor to export the 3D models created in the VR space as a 3D file, which allows students to view the 3D models on their own VR headsets or computer devices, and 3D print the structures created in the VR space. Preliminary data suggests that students had a better understanding of anatomical concepts, relationships, and depth when using the supplementary VR resources in conjunction with traditional resources. Moreover, VR video content attained a higher viewer retention rate and viewer satisfaction when compared to 2D videos.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".