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Facilitating Molecular Biology Teaching by Using Augmented Reality (AR) and Protein Data Bank (PDB)

  • Parviz Safadel
  • David White
Original Paper

Abstract

Spatial understanding of molecules in molecular biology provides a better understanding of molecules in isolation and relation to their next elements. Augmented reality (AR) has recently been developed as a computer interface that enables the users to see the real world with virtual objects superimposed on it. We report a method that shows the use of AR and data provided from protein data bank (PDB) to facilitate the teaching of macromolecules in biology. Users can easily convert the molecules structures obtained from PDB to a 3D format and use it with an AR application to study the molecules from different perspectives. A sample of 60 college students was assigned randomly to one of two conditions namely 2D and AR. At the end of the experiment, participants completed a comprehensive test and then a satisfaction questionnaire. The results of the study showed a significant difference between 2D and AR in satisfaction, the media usability, perception, and apprehension.

Keywords

AR Augmented reality PDB Biology 3D visualization DNA structure Media satisfaction Usability PyMol Blender PMV 

Notes

Compliance with Ethical Standards

Conflict of Interest

I have no conflict of interest.

Ethical Approval

This article does not contain any studies with animals performed by any of the authors.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Azuma, R. T. (1997). A survey of augmented reality. Presence, 6(4), 355–385.CrossRefGoogle Scholar
  2. Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84(2), 191.CrossRefGoogle Scholar
  3. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs: Prentice Hall.Google Scholar
  4. Cheng, K. H., & Tsai, C. C. (2016). The interaction of child–parent shared reading with an augmented reality (AR) picture book and parents' conceptions of AR learning. British Journal of Educational Technology, 47(1), 203–222.CrossRefGoogle Scholar
  5. Dawley, L., & Dede, C. (2014). Situated learning in virtual worlds and immersive simulations. In Handbook of research on educational communications and technology (pp. 723–734). New York: Springer.CrossRefGoogle Scholar
  6. Dede, C. (2008). Theoretical perspectives influencing the use of information technology in teaching and learning. In International handbook of information technology in primary and secondary education (pp. 43–62). Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-0-387-73315-9_3
  7. Dunleavy, M. (2014). Design principles for augmented reality learning. TechTrends, 58(1), 28–34.CrossRefGoogle Scholar
  8. Dunleavy, M., & Dede, C. (2014). Augmented reality teaching and learning. In Handbook of research on educational communications and technology (pp. 735–745). New York: Springer.CrossRefGoogle Scholar
  9. Gallagher, A. G., & O’Sullivan, G. C. (2011). Fundamentals of surgical simulation–principles and practices. Retrieved from http://archpsyc.jamanetwork.com/data/Journals/JAMA/22492/jbk0307_974_975.pdf CrossRefGoogle Scholar
  10. Galton, F. (1883). Inquiries into the human faculty & its development. JM Dent and Company. Retrieved from https://books.google.com/books?hl=en&lr=&id=V91CAQAAMAAJ&oi=fnd&pg=PA1&dq=Galton,+1883&ots=crKYQGAe1A&sig=hduPNx4tK2kAXnV2oMwOIeZlCiA
  11. Ganley, C. M., Vasilyeva, M., & Dulaney, A. (2014). Spatial ability mediates the gender difference in middle school students' science performance. Child Development, 85(4), 1419–1432.CrossRefGoogle Scholar
  12. Gillet, A., Sanner, M., Stoffler, D., & Olson, A. (2005). Tangible interfaces for structural molecular biology. Structure, 13(3), 483–491.CrossRefGoogle Scholar
  13. Guilford, J. P., & Lacey, J. I. (1947). Printed classification tests. DTIC Document. Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=AD0651781
  14. Holopainen, J., Mattila, O., Parvinen, P., Pöyry, E., & Seppälä, K. (2018). Employing mixed reality applications: Customer experience perspective. In Proceedings of the 51st Hawaii International Conference on System Sciences.Google Scholar
  15. Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2006). Mental imagery and the human brain. Progress in Psychological Science around the World, 1, 195–209.Google Scholar
  16. Malone, T. W. (1981). Toward a theory of intrinsically motivating instruction*. Cognitive Science, 5(4), 333–369.CrossRefGoogle Scholar
  17. Martín-Gutiérrez, J., Saorín, J. L., Contero, M., Alcañiz, M., Pérez-López, D. C., & Ortega, M. (2010). Design and validation of an augmented book for spatial abilities development in engineering students. Computers & Graphics, 34(1), 77–91.CrossRefGoogle Scholar
  18. North, M. M., & North, S. M. (2016). A comparative study of sense of presence of traditional virtual reality and immersive environments. Australasian Journal of Information Systems, 20.  https://doi.org/10.3127/ajis.v20i0.1168.
  19. Palincsar, A. S. (2005). 12 Social constructivist perspectives on teaching and learning. In An Introduction to Vygotsky (Vol. 285).Google Scholar
  20. Rodic, M., Zhou, X., Tikhomirova, T., Wei, W., Malykh, S., Ismatulina, V., ... & Kovas, Y. (2015). Cross-cultural investigation into cognitive underpinnings of individual differences in early arithmetic. Developmental Science, 18(1), 165–174.CrossRefGoogle Scholar
  21. Salancik, G. R., & Pfeffer, J. (1978). A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 23, 224–253.CrossRefGoogle Scholar
  22. Stieff, M., & Uttal, D. (2015). How much can spatial training improve STEM achievement? Educational Psychology Review, 27(4), 607–615.CrossRefGoogle Scholar
  23. Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: a structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124–142.CrossRefGoogle Scholar
  24. Towle, E., Mann, J., Kinsey, B., Brien, E. J., Bauer, C. F., & Champoux, R. (2005). Assessing the self efficacy and spatial ability of engineering students from multiple disciplines. In Frontiers in Education, 2005. FIE’05. Proceedings 35th Annual Conference (p. S2C–15). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1612216
  25. Treisman, A. M. (1964). Verbal cues, language, and meaning in selective attention. The American Journal of Psychology, 77, 206–219.CrossRefGoogle Scholar
  26. Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.CrossRefGoogle Scholar
  27. Wei, X., Weng, D., Liu, Y., & Wang, Y. (2015). Teaching based on augmented reality for a technical creative design course. Computers & Education, 81, 221–234.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications & Technology 2018

Authors and Affiliations

  1. 1.Texas Tech UniversityLubbockUSA

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