Exploring different communication modes for intravenous infusion training using mixed reality: A healthcare e-learning case study with student directed learning
Bibliographic record
Abstract
The COVID-19 pandemic presented challenges for healthcare students, necessitating the acquisition of clinical skills through online learning due to time constraints and limited access to medical supplies. In response to such major interruptions in the healthcare-related and other educational sectors, extended reality (XR) technologies have been swiftly emerging as a promising solution. Our case study here focuses on developing an augmented reality training platform in Unity, and compatible with Microsoft HoloLens. This platform often projects the virtual training components into the user’s surrounding real-world environment, reducing dependence on physical lab sessions and promoting Student Directed Learning (SDL). To illustrate the platform's effectiveness, we focused on teaching the assembly of an intravenous infusion (IV) pump. Four communication/mentoring modes (computer agent-text, agent-audio, human-text, and human-audio) have been enabled within the present XR-based SDL tool. A user study with 8 senior nursing students revealed that the agent-text mode (out of all four modes) was the most effective, considering trust, reliability, usefulness, satisfaction, and ease of use measures. • Developed an Extended Reality with different communication modes for IV infusion training. • Emphasized Student Directed Learning, fostering motivation and deeper engagement. • User study included trust, reliability, usefulness, satisfaction, and ease of use. • Computer agent instructor-text mode delivery was top-ranked communication option.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".