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Comparison of Virtual Anatomy Technologies for Completing Educational Tasks Using a Decision Matrix
Author(s) -
Eisenmann Jordan,
Johnson Laura,
Meyer Edgar,
Hoffmann Darren
Publication year - 2021
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2021.35.s1.03515
Subject(s) - laptop , suspect , quality (philosophy) , medical education , scale (ratio) , rating scale , psychology , computer science , medicine , cartography , developmental psychology , philosophy , criminology , epistemology , geography , operating system
IntroOne fundamental subject in medicine with a high density of information is anatomy. While exploring tools to support learning, students and educators will encounter dozens if not hundreds of applications (apps) available to them. While this may initially appear helpful, the vast quantity of options poses a new issue ‐ finding the right app for their needs.HypothesisWe hypothesize that students and educators will have distinct priorities in making decisions about purchasing educational technology and suspect that incorporating these priorities into a decision matrix will allow for discrimination of similar products in the app market to match each user's needs.MethodsWe asked 10 students and 5 educators to issue an importance rating to 19 characteristics found in anatomical learning apps on a 1‐7 scale, before completing 3 educational tasks each for 3 laptop‐based apps: 3D Organon Anatomy (3DOrg), Human Anatomy Atlas 2019 (HAA19), and Complete Anatomy 2020 (CA20). After the tasks, participants rated the quality (on a 1‐7 scale) of the same 19 characteristics for each app. The “importance” and “quality” ratings for each of the 19 characteristics were multiplied and then totaled for a final score for each app. The highest score among the 3 apps indicated the preferred app for each individual, and importance/quality data were directly compared by one way ANOVA and t‐tests.ResultsThe decision matrix results showed that 6 students preferred CA20, 3 preferred HAA19, and 1 preferred 3DOrg. Of the educators, 2 preferred CA20, 2 preferred HAA19, and 1 preferred 3DOrg. The characteristics ranked as most important across students were: productivity/efficiency (6.500±0.5270), clear highlighting of structures (6.500±0.7071), and ability to switch between male and female models (6.300± 0.8233), all of which were significantly higher than many other characteristics, p<0.05. The characteristics educators ranked as most important were: level of detail (6.600±0.5477), intuitive controls (6.400±0.8944), and productivity/efficiency (6.400±0.8944), which were also significantly higher than many other characteristics, p<0.05. In free response prompts, both groups found CA20 to be highly detailed with many tools, but disliked the app's required yearly subscription and high learning curve for effective use. 3DOrg had the opposite issue as it had a minor learning curve and was a one‐time purchase, but lacked detail and features. HAA19 fell between the two in terms of detail and learning curve but was also a one‐time purchase. ConclusionThis study is a first step in the development of specific recommendations for virtual anatomy apps and suggests that students and educators desire different qualities in their apps. Furthermore, individual taste appears to play a role in preference of their optimal app. These results could be useful in future studies where a decision matrix algorithm may be used to predict the optimal app for learners and test the reliability of that prediction by evaluating satisfaction and learning outcomes.