The superiority of three-dimensional physical models to two-dimensional computer presentations in anatomy learning Academic Article uri icon

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abstract

  • BACKGROUND: Although several studies (Anat Sci Educ, 8 [6], 525, 2015) have shown that computer-based anatomy programs (three-dimensional visualisation technology [3DVT]) are inferior to ordinary physical models (PMs), the mechanism is not clear. In this study, we explored three mechanisms: haptic feedback, transfer-appropriate processing and stereoscopic vision. METHODS: The test of these hypotheses required nine groups of 20 students: two from a previous study (Anat Sci Educ, 6 [4], 211, 2013) and seven new groups. (i) To explore haptic feedback from physical models, participants in one group were allowed to touch the model during learning; in the other group, they could not; (ii) to test 'transfer-appropriate processing' (TAP), learning ( PM or 3DVT) was crossed with testing (cadaver or two-dimensional display of cadaver); (iii) finally, to examine the role of stereo vision, we tested groups who had the non-dominant eye covered during learning and testing, during learning, or not at all, on both PM and 3DVT. The test was a 15-item short-answer test requiring naming structures on a cadaver pelvis. A list of names was provided. RESULTS: The test of haptic feedback showed a large advantage of the PM over 3DVT regardless of whether or not participants had haptic feedback: 67% correct for the PM with haptic feedback, 69% for PM without haptic feedback, versus 41% for 3DVT (p < 0.0001). In the study of TAP, the PM had an average score of 74% versus 43% for 3DVT (p < 0.0001) regardless of two-dimensional versus three-dimensional test outcome. The third study showed that the large advantage of the PM over 3DVT (28%) with binocular vision nearly disappeared (5%) when the non-dominant eye was covered for both learning and testing. CONCLUSIONS: A physical model is superior to a computer projection, primarily as a consequence of stereoscopic vision with the PM. The results have implications for the use of digital technology in spatial learning.

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publication date

  • November 2018