Using 3D holographic technology (HoloLens) for asthma education in health sciences and medicine.
Bibliographic record
Abstract
Medical and health sciences education requires constant updating and refining to best prepare students for the expectations of working in the modern healthcare professional environment. Learning about a range of diseases is a core component within these courses, and a fundamental part of many curricula and assessment-focussed outcomes. One common example is asthma, a widespread and prevalent respiratory condition estimated to affect up to 339 million people globally. However, learning about the management of asthma requires students to integrate knowledge of physiology, anatomy, pathology, immunology, pharmacology and more. It also necessitates a need for an understanding of the lungs and its associated structures in 3D space. This can be difficult when studying from a textbook or lecture notes alone. In recent years, there has been a shift towards technology-enhanced learning to deliver content in an engaging manner. Emerging technology, such as the Microsoft HoloLens, is of great interest as it can provide 3D representations of the human body, while also encouraging interactivity with any presented organs or systems. Though never employed for the specific use of teaching asthma, the HoloLens shows potential as a way to effectively explain the mechanisms underlying asthma, and its associated multidisciplinary concepts. The aim of this honours research project will be to assess whether a textbook-style written delivery, or a three-dimensional (3D) augmented reality HoloLens resource, is more effective for learning. This will be a randomised-control trial utilising pre- and post-testing with first year health sciences and medical students. Lessons will be set up with an instructional module explaining the epidemiology, anatomy, physiology, pathophysiology, immunology and pharmacology of the respiratory system and asthma. The control group are to be provided with a printed textbook- style version of the lesson, with 2-dimensional diagrams, while the HoloLens intervention group viewing the models in 3D, with the text read out as an audio transcript. Though data collection will commence shortly, it is hypothesised that learning through augmented reality using the HoloLens device will provide a better overall learning experience and improve test performance for health sciences and medical students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".