Evaluation of Student Use of a “Virtual Bellringer” Smartphone App
Bibliographic record
Abstract
Introduction Online or smartphone‐based anatomy resources such as atlases and 3D models are becoming more popular study tools by students. Although some include the ability to rotate objects and use shadowing to simulate depth, nearly all of these lack stereopsis. Recent studies have shown that stereopsis may be crucial for effective education in anatomy (Remmele et al, 2018; Cui et al, 2017). We developed a smartphone application to allow students to practice for their final bellringer practical exam (OSPE; Objective Structured Practical Exam). The app included 67 stereoscopic pairs from the Stereoscopic Atlas of Human Anatomy (D.L. Bassett & W.B. Gruber, Stanford University) (1962) ( http://lane.stanford.edu/biomed‐resources/bassett/raw/index.html ) and 5 images taken in‐house. The app was created for smartphones and viewable using Google Cardboard. An improvement over the original Viewmaster format (1962) was the ability to organize images and to point to specific structures in 3D space using digital pins. The PURPOSE of our study was to develop an inexpensive and easily accessible phone app and to evaluate its use by students in a second year undergraduate anatomy and physiology course. We termed our app the Virtual Reality Bell Ringer (VRBR). Methods The smartphone application was developed using Unity so that it could be implemented on both iOS and Android platforms. The app consisted of at least 5 digitally pinned images in each of 12 labs of the two term A&P course. A gaze‐based control system was used to navigate the main menu to choose labs while a gesture‐based control system was used to circulate through images. 975 students were given a Google Cardboard VR device (I AM Cardboard V2®; $8/unit) that was included with the purchase of the mandatory custom courseware lab manual at the beginning of the semester. The questions and answers for the pinned structures was provided on the university course management system for the course. Students were surveyed regarding their use of the app at mid‐semester of the first term. Results Of the surveyed students, 19% of students described feeling nauseated when using the app, 8% of students had phones that were not compatible with the app and 20% found that the memory requirements of the app was a significant barrier to its use. 10% of students described the app as being easy to use, 11% described it as being well designed, and 14% of students enjoyed using the app to test themselves. However, the gaze‐based menu was described as being difficult to use by students who reported that they “rarely” used the app, while students who reported that they “sometimes” use the app found the menu as easy to navigate. Conclusion We developed a smartphone app that employs stereoscopic visualization of anatomical dissections. The use of digital pins enabled the app to be implemented as a virtual OSPE for self‐testing. While many students have not yet used the app to prepare for the final exam, those students who used the app identified the large size of the app, the gaze‐based menu system, and phone compatibility to be barriers to its use. We are currently addressing these concerns in its second version. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.021 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".