Use of augmented reality (AR) to aid bioscience education and enrich student experience
In recent years, development of new technologies designed to enhance user experience have accelerated, often being used in modern media such as in films and games. Specifically, immersive experiences, such as virtual reality (VR) and augmented reality (AR), have redefined how digital media can be delivered, encouraging us to interact with and explore our environment. Reciprocally, as the power of these technologies has advanced, the associated costs to implement them have decreased, making them more cost-effective and feasible to deliver in a variety of settings. Despite the cost reduction, several issues remain with accessibility due to the knowledge base required to generate, optimise and deliver three-dimensional (3D)-digital content in both AR and VR. Here, we sought to integrate an AR-based experience into a level-4 biochemistry module in order to support the delivery of university lectures on protein structure and function. Traditionally, this topic would comprise two-dimensional still images of complex 3D structures. By combining a breadth of subject-specific and technological expertise from across the university, we developed an AR-enhanced learning experience hosted on the Zapworks AR platform. AR enabled full illustration of the complexity of these 3D structures, while promoting collaboration through a shared user experience. Assessing the impact of the AR experience via a formative test and survey revealed that despite only a modest increase in test performance, students overwhelmingly reported positively on the engaging nature and interactivity of AR. Critically, expanding our repertoire of content delivery formats will support the forward-thinking blended learning environments adopted across the higher education sector.
Berisio, R., et al., (2002) ‘Crystal structure of the collagen triple helix model [(Pro-Pro-Gly)10]3’, Protein Science, vol. 11, no. 2, pp. 262–270. doi: 10.1110/ps.32602
Bernardo, A. (2017) ‘Virtual reality and simulation in neurosurgical training’, World Neurosurgery, vol. 106, pp. 1015–1029. doi: 10.1016/j.wneu.2017.06.140
Berryman, D. R. (2012) ‘Augmented reality: a review’, Medical Reference Services Quarterly, vol. 31, no. 2, pp. 212–218. doi: 10.1080/02763869.2012.670604
Bower, M., et al., (2014) ‘Augmented reality in education – cases, places and potentials’, Educational Media International, vol. 51, no. 1, pp. 1–15. doi: 10.1080/09523987.2014.889400
Carmigniani, J., et al., (2011) ‘Augmented reality technologies, systems and applications’, Multimedia Tools and Applications, vol. 51, no. 1, pp. 341–377. doi: 10.1007/s11042-010-0660-6.
Chen, Y. (2006) A Study of Comparing the Use of Augmented Reality and Physical Models in Chemistry Education, ACM, p. 369. New York, NY, USA.
Da Veiga Beltrame, E., et al., (2017) ‘3D printing of biomolecular models for research and pedagogy’, Journal of Visualized Experiments, no. 121. doi: 10.3791/55427
EE. (2020) EE 5G brings you a live AR performance across the UK (2020). Directed by EE [Television advertisement]. Screened 13/01/2020.
García-Carrión, R., et al., (2020) ‘Implications for social impact of dialogic teaching and learning’, Frontiers in psychology, vol. 11, pp. 140. doi: 10.3389/fpsyg.2020.00140
Gil-Garcia, M., et al., (2018) ‘Combining structural aggregation propensity and stability predictions to redesign protein solubility’, Molecular Pharmaceutics, vol. 15, no. 9, pp. 3846–3859. doi: 10.1021/acs.molpharmaceut.8b00341
Gillet, A., et al., (2004) Augmented Reality with Tangible Auto-Fabricated Models for Molecular Biology Applications, IEEE Computer Society, p. 235. Austin, TX, USA.
Gulbis, J. M., et al., (1996) ‘Structure of the C-terminal region of p21(WAF1/CIP1) complexed with human PCNA’, Cell (Cambridge), vol. 87, no. 2, p. 297. doi: 10.1016/s0092-8674(00)81347-1
Hannan, L., Duhs, R. & Chatterjee, H. J. (2013) ‘Object based learning: a powerful pedagogy for higher education’, in Museums and Higher Education Working Together: Challenges and Opportunities, eds A. Boddington, J. Boys & C. Speight, Ashgate Publishing, Farnham, pp. 159–168.
Hu, M. & Liu, B. (2004) Mining and Summarizing Customer Reviews, ACM, p. 168. New York, NY, USA.
Khan, S. & Vihinen, M. (2007) ‘Spectrum of disease-causing mutations in protein secondary structures’, BMC Structural Biology, vol. 7, no. 1, p. 56. doi: 10.1186/1472-6807-7-56
Kolb, D. A. (2015) Experiential Learning, 2nd edn, Pearson Education, Upper Saddle River, NJ.
Light, G., Cox, R. & Calkins, S. (2009) Learning and Teaching in Higher Education, 2nd edn, Sage, London.
Nelson, D. & Cox, M. (2017) Lehninger Principles of Biochemistry, 7th edn, W.H. Freeman and Company. New York, NY, USA.
Paavilainen, J., et al., (2017) The Pokémon GO Experience, ACM, p. 2493. New York, NY, USA.
Pettersen, E. F., et al., (2004) ‘UCSF Chimera – a visualization system for exploratory research and analysis’, Journal of Computational Chemistry, vol. 25, no. 13, pp. 1605–1612. doi: 10.1002/jcc.20084
Phon, D. N. E., Ali, M. B. & Halim, N. D. A. (2014) Collaborative Augmented Reality in Education: A Review, IEEE, p. 78. Kuching, Malaysia.
Schrodinger, L. (2015) Pymol. The Molecular Graphics System, Schrodinger, New York, NY, USA.
Silge, J. & Robinson, D. (2016) ‘tidytext: text mining and analysis using tidy data principles in R’, Journal of Open Source Software, vol. 1, no. 3, p. 37. doi: 10.21105/joss.00037
Smith, D. P. (2016) ‘Active learning in the lecture theatre using 3D printed objects [version 2; peer review: 2 approved]’, F1000Research, vol. 5, p. 61. doi: 10.12688/f1000research.7632.2
Surade, S. & Blundell, T. (2012) ‘Structural biology and drug discovery of difficult targets: the limits of ligandability’, Chemistry & Biology, vol. 19, no. 1, pp. 42–50. doi: 10.1016/j.chembiol.2011.12.013
Thomas, R. G., William John, N. & Delieu, J. M. (2010) ‘Augmented reality for anatomical education’, Journal of Visual Communication in Medicine, vol. 33, no. 1, pp. 6–15. doi: 10.3109/17453050903557359
Xiao, Y., et al., (2015) ‘Aβ(1–42) fibril structure illuminates self-recognition and replication of amyloid in Alzheimer’s disease’, Nature Structural & Molecular Biology, vol. 22, no. 6, pp. 499–505. doi: 10.1038/nsmb.2991
Zappar Ltd. (2020) Zapworks, [online] Available at: https://zap.works/
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