Introduction to Wearable Enhanced Learning (WELL): Trends, Opportunities, and Challenges

  • Ilona BuchemEmail author
  • Ralf Klamma
  • Fridolin Wild


Wearable enhanced learning (WELL) is an emerging area of interest for researchers, practitioners in educational institutions, and companies. Also many grassroots movements are providing new sensors, devices, prototypical concepts, and learning solutions for WELL. Deeply rooted in the traditions of technology enhanced learning (TEL), such as self-regulated learning and mobile learning, WELL has been generating new challenges and opportunities in the field. Fragmentation, scalability, and data aggregation and resulting pedagogical approaches are among the key challenges and opportunities. The authors of this chapter explore drivers and affordances of wearable enhanced learning, outline the development of WELL as part of the evolution of technology enhanced learning, describe the key stakeholders in WELL (business, vocational training, higher education, and maker communities), and inspect some of the key domains in WELL, such as gaming and entertainment, health and sports, business and industries, and some technology trends, such as e-textiles, smart accessories, and head-mounted displays. This chapter broadens current perspectives on learning with wearables and learning about wearables and integrates insights from related fields including philosophy of technology, sociology, and design.


Wearables Learning Drivers Affordances Trends Fragmentation Scalability Data aggregation Diffused landscape Experimental field 


  1. Abt, C. C. (1970). Serious games. New York: Viking Press.Google Scholar
  2. Aldowah, H., Rehman, S., Ghazal, S., & Umar, I. (2017). Internet of things in higher education: A study on future learning. Journal of Physics Conference Series, 892(1), 012017.CrossRefGoogle Scholar
  3. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376.CrossRefGoogle Scholar
  4. Annapurna, S., Teja, P. K. V. S., & Murty, S. (2016). A comparative study on Mobile platforms (android vs. IOS). International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 5(3), 547–553.Google Scholar
  5. Attallah, B., & Ilagure, Z. (2018). Wearable technology: Facilitating or Complexing education? International Journal of Information and Education Technology, 8(6), 433–436.CrossRefGoogle Scholar
  6. Atzori, L., Lera, A., & Morabito, G. (2014). From “smart objects” to “social objects”: The next evolutionary step of the internet of things. Communications Magazine, IEEE, 52(1), 97–105.CrossRefGoogle Scholar
  7. Avella, J. T., Kanai, T., & Kebritchi, M. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning, 20(2), 13–29.Google Scholar
  8. Awolusi, I., Marks, E., & Hallowell, M. (2018). Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices. Automation in Construction, 85, 96–106.CrossRefGoogle Scholar
  9. Bower, M., & Sturman, D. (2015). What are the educational affordances of wearable technologies? Computers & Education, 88, 343–353.CrossRefGoogle Scholar
  10. Bower, M., Sturman, D., Alvarez, V. (2016). Perceived utility and feasibility of wearable technologies in higher education,” Proceedings of 15th World Conference on Mobile and Contextual Learning, mLearn, October 2016, Sydney.Google Scholar
  11. Buchem, I., Attwell, G., & Tur, G. (Eds.). (2013). The PLE conference 2013. Learning and diversity in the cities of the future. 4th international conference on personal learning environments (Beuth research report 2013). Berlin: Logos Verlag.Google Scholar
  12. Buchem, I., Merceron, A., Kreutel, J., Haesner, M. and Steinert, A. (2015). Designing for user engagement in wearable-technology enhanced learning for healthy ageing. iLRN conference 2015, workshop proceedings of the 11th international conference on intelligent environments.Google Scholar
  13. Buckingham S. (2012). Learning analytics. UNESCO policy brief. Retrieved from Accessed 9 Nov 2018.
  14. Cecchinato, M. E., & Cox, A. L. (2017). Smartwatches: Digital handcuffs or magic bracelets? Computer, 50(4), 106–109.CrossRefGoogle Scholar
  15. Chan, T., Sharples, M., Vavoula, G., & Lonsdale, P. (2004). Educational metadata for Mobile learning. In Proceedings of the The 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education (WMTE’04).Google Scholar
  16. Clemons, E. K., & Madhani, N. (2010). Regulation of digital businesses with natural monopolies or third-party payment business models: Antitrust lessons from the analysis of Google. Journal of Management Information Systems, 27(3), 43–80.CrossRefGoogle Scholar
  17. Colpani, R., & Homem, M. R. P. (2015). An innovative augmented reality educational framework with gamification to assist the learning process of children with intellectual disabilities. In 6th international conference on information, intelligence, systems and applications (pp. 1–6). Corfu: IISA 2015. IISA.Google Scholar
  18. Cordeil, M., Cunningham, A., Dwyer, T., Thomas, B. H., & Marriott, K. (2017). ImAxes: Immersive axes as embodied affordances for interactive multivariate data visualisation. In Proceedings of the 30th annual ACM symposium on user Interface software and technology (UIST '17) (pp. 71–83). New York: ACM.Google Scholar
  19. David, M. (2015). The correspondence theory of truth, Stanford Encyclopedia of Philosophy. Accessed 9 Nov 2018.
  20. Duarte, B. N. (2013). The body Hacktivism movement: A talk about the body. PsychNology Journal, 11(1), 21–42.Google Scholar
  21. Dunleavy, M., Dede, C., & Mitchell, R. (2009). Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology, 18(1), 7–22.CrossRefGoogle Scholar
  22. Eisenhauer, M., Rosengren, P., & Antolin, P. A. (2009). Development platform for integrating wireless devices and sensors into ambient intelligence systems. In 6th annual IEEE communications society conference on sensor, Mesh and Ad Hoc communications and networks workshops (pp. 1–3).Google Scholar
  23. Elyamany, H. F. and Alkhairi A. H. (2015). IoT-academia architecture: A profound approach, Proceedings of the 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Takamatsu, 1–5.Google Scholar
  24. Engeström, Y. (2005). Developmental work research. Expanding activity theory in practice (International cultural-historical human sciences). Berlin: Lehmanns Media.Google Scholar
  25. Ernst, C.-P. H. (2016). The drivers of wearable device usage. In Practice and perspectives. Switzerland: Springer International Publishing.Google Scholar
  26. Ezenwoke, A., & Ezenwoke, O. (2016). Wearable technology: Opportunities and challenges for teaching and learning in higher education in developing countries. In INTED 2016. Barcelona.Google Scholar
  27. Freitas de, S. & Levene, M. (2003). Evaluating the development of wearable devices, personal data assistants and the use of other mobile devices in further and higher education institutions. JISC Technology and Standards Watch Report (TSW030), 1–21.Google Scholar
  28. de Freitas, S., & Liarokapis, F. (2011). Serious games: A new paradigm for education? In M. Ma, A. Oikonomou, & L. C. Jain (Eds.), Serious games and edutainment applications (pp. 9–23). London: Springer.CrossRefGoogle Scholar
  29. Foreman, J. (2004). Game-based learning: How to delight and instruct in the 21st century. In: Educause Review, 39(5), 50–66.Google Scholar
  30. Gilliland. S., Komor. N., Starner, T. & Zeagler, C. (2010). The textile interface swatchbook: Creating graphical user interface-like widgets with conductive embroidery. In International Symposium on Wearable Computers ISWC, 18–25, Seoul, South Korea, IEEE.Google Scholar
  31. Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin Harcourt (HMH).Google Scholar
  32. Guinard, D., & Vlad, T. (2015). Building the web of things. Shelter Island: Manning.Google Scholar
  33. Hayward, J. (2018). Wearable technology 2018-2028: Markets, players, forecasts. Resource document. IDTechEx. Accessed 1 Nov 2018.
  34. Hajo, A., & Galinsky, A. D. (2012). Enclothed cognition. Journal of Experimental Social Psychology, 48(4), 918–925.CrossRefGoogle Scholar
  35. Hensen, B., Koren, I., Klamma, R., Herrler, A. (2018). An augmented reality framework for gamified learning. In: G. Hancke, M. Spaniol, K. Osathanunkul, S. Unankard und R. Klamma (Hg.): Advances in web-based learning – ICWL 2018. Cham: Springer International Publishing (11007).Google Scholar
  36. Hunn, N. (2015). The market for smart wearable technology. A Consumer Centric Approach. WiFore Consulting. Resource document. Accessed 9 Nov 2018.
  37. Irizarry, J., Gheisari, M., & Walker, B. (2012). Usability assessment of drone technology as safety inspection tools. Journal of information technology in construction (ITcon), 17(12), 194–212.Google Scholar
  38. Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Ludgate, H. (2013). NMC horizon report: 2013 higher (Education Edition). Austin: The New Media Consortium.Google Scholar
  39. Kahneman, D. (1973). Attention and effort. Englewood Cliffs: Prentice-Hall (Prentice-Hall series in experimental psychology).Google Scholar
  40. Kapp, K. M. (2012). The gamification of learning and instruction. Game-based methods and strategies for training and education. Hoboken: Wiley.Google Scholar
  41. Kiourti, A., Lee, C. W. L., Chae, J., & Volakis, J. L. (2016). A wireless fully passive neural recording device for unobtrusive neuropotential monitoring. IEEE Transactions on Biomedical Engineering, 63(1), 131–137.CrossRefGoogle Scholar
  42. Koren, I., & Klamma, R. (2017). Community learning analytics with industry 4.0 and wearable sensor data. In D. Beck, C. Allison, L. Morgado, J. Pirker, F. Khosmood, J. Richter, & C. Gütl (Eds.), Immersive learning research network. Third international conference, iLRN 2017, Coimbra, Portugal, June 26–29, 2017. Proceedings iLRN 2017. Coimbra, Portugal, June 26–29, 2017 (Vol. 725, pp. 142–151). Cham: Springer International Publishing.Google Scholar
  43. Kreft, S., Gausemeier, J., Matysczok, C. (2009). Towards wearable augmented reality in automotive assembly training. In: ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2, 1537–1547: 29th computers and information in engineering conference, San Diego, CA, August 30–September 2, 2009.Google Scholar
  44. Kukulska-Hulme, A. (2010). Mobile learning as a catalyst for change. Open Learning: The Journal of Open and Distance Learning, 25(3), 181–185.CrossRefGoogle Scholar
  45. Kurzweil, R. (1999). The age of spiritual machines: When computers exceed human intelligence. New York: Viking Penguin.Google Scholar
  46. Lanier, J. (2000). One-half of a manifesto. Edge, Resource document Accessed 28 Oct 2018.
  47. Lanier, J. (2010). Who owns the future? New York: Simon & Schuster.Google Scholar
  48. Latour, B. (1993). We have never been modern. Cambridge, MA: Harvard University Press.Google Scholar
  49. Law, J., & Hassard, J. (1999). Actor-network theory and after. Oxford: Blackwell Publishers/The Sociological Review.Google Scholar
  50. Limbu, B., Jarodzka, H., Klemke, R., Wild, F., & Specht, M. (2018). From AR to expertise: A user study of an augmented reality training to support expertise development. Journal of Universal Computer Science, 24(2), 108–128.Google Scholar
  51. Lingley, A. R., Ali, M., Liao, Y., Mirjalili, R., Klonner, M., Sopanen, M., Suihkonen, S., Shen, T., Otis, B. P., Lipsanen, H., & Parviz, B. A. (2011). A single-pixel wireless contact lens display. Journal of Micromechanics and Microengineering, 21(12), 125014.CrossRefGoogle Scholar
  52. Luhmann, N. (1997). Die Gesellschaft der Gesellschaft. Suhrkamp: Frankfurt a. M.Google Scholar
  53. Luhmann, N. (2000). The reality of the mass media. Stanford: Stanford, CA.Google Scholar
  54. Mann, S. (2013). Veillance and reciprocal transparency: Surveillance versus sousveillance, AR glass, lifeglogging, and wearable computing. IEEE ISTAS, 2013, 1–12.Google Scholar
  55. Manovich, L. (2001). The language of new media. Cambridge, MA: MIT press.Google Scholar
  56. Mann, S., Fung, J., & Lo, R. (2006). Cyborglogging with camera phones. In K. Nahrstedt, M. Turk, Y. Rui, W. Klas, & K. Mayer-Patel (Eds.), Proceedings of the 14th annual ACM international conference on Multimedia - MULTIMEDIA '06. The 14th annual ACM international conference. Santa Barbara, CA, USA, 23.10.2006–27.10.2006 (p. 177). New York: ACM Press.Google Scholar
  57. Maurtua, I. (2009). Wearable technology in automotive industry: From training to real production. In A. Takashima & Y. Tanaka (Eds.), Sharing and composing video viewing experience. London: INTECH Open Access Publisher.Google Scholar
  58. McEwen, A., & Cassimally, H. (2013). Designing the internet of things. Hoboken: Wiley.Google Scholar
  59. Nagtegaal, F. Verzijl, D. Dervojeda, K.; Probst, L.; Frideres, L., Pedersen, B (2015). Internet of things. Wearable technology. Business innovation observatory. European Union, February 2015.Google Scholar
  60. Neely, R. M., Piech, D. K., Santacruz, S. R., Maharbiz, M. M., & Carmena, J. M. (2018). Recent advances in neural dust: Towards a neural interface platform. Current Opinion in Neurobiology, 50, 64–71.CrossRefGoogle Scholar
  61. Norman, D. A. (1988). The psychology of everyday things. New York: Basic Books.Google Scholar
  62. Noura, A. & Renaud, K. (2016). Privacy of the internet of things (2016). A systematic literature review (extended discussion).Google Scholar
  63. Orlikowski, W. J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization Science, 3(3), 398–427.CrossRefGoogle Scholar
  64. Parsons, D., Thomas, H., & Wishart, J. (2016). Exploring mobile affordances in the digital classroom. In I. Arnedillo-Sanchez & P. Isias (Eds.), Proceedings of the 12th international conference on mobile learning 2016 (pp. 43–50). Lisboa: IADIS Press.Google Scholar
  65. Pejoska, J. (2016). Kinemata - motion memory enhanced. AALTO University. Learning environments research group. Resource document Accessed 15 Nov 2018.
  66. Ras, E., Wild, F., Stahl, C., & Baudet, A. (2017). Bridging the skills gap of workers in industry 4.0 by human performance augmentation tools. In Proceedings of the 10th International Conference on Pervasive Technologies Related to Assistive Environments - PETRA '17. the 10th International Conference. Island of Rhodes, Greece, 21.06.2017–23.06.2017 (pp. 428–432). New York: ACM Press.Google Scholar
  67. Rehring, K., Hoffmann, D., Ahlemann, F. (2018). Put your glasses on: Conceptualizing affordances of mixed and virtual reality for enterprise architecture management. Multikonferenz Wirtschaftsinformatik 2018. Resource document Accessed 8 Nov 2018.
  68. Rheingans, F., Cikit, B., & Ernst, C.-P. H. (2016). The potential influence of privacy risk on activity tracker usage: A study. In C.-P. H. Ernst (Ed.), The drivers of wearable device usage. Practice and perspectives. Switzerland: Springer International Publishing.Google Scholar
  69. Rheingold, H. (2002). Smart mobs: The next social revolution. Cambridge, MA: Basic Books.Google Scholar
  70. Rizwan, M., & Qureshi, J. (2013). IMS-based mobile learning system. Life Science Journal, 10(4), 2121–2126.Google Scholar
  71. Sandall, B. K. (2016). Wearable technology and schools: Where are we and where do we go from Here? Journal of Curriculum, Teaching, Learning and Leadership in Education, 1(1), 9.Google Scholar
  72. Sharon, T., & Zandbergen, D. (2016). From data fetishism to quantifying selves: Self-tracking practices and the other values of data. New Media & Society, 9(11), 1695–1709.CrossRefGoogle Scholar
  73. Shin, D. D. H. (2017). Empathy and embodied experience in virtual environment: To what extent can virtual reality stimulate empathy and embodied experience? Computers in Human Behavior, 78, 64–73.CrossRefGoogle Scholar
  74. Steinert, A., Buchem, I., Merceron, A., Kreutel, J., & Haesner, M. (2018). A wearable-enhanced fitness program for older adults, combining fitness trackers and gamification elements: The pilot study fMOOC@Home. Sport Sciences for Health, 14(8), 275.CrossRefGoogle Scholar
  75. Stoppa, M., & Chiolerio, A. (2014). Wearable electronics and smart textiles: A critical review. Sensors (Basel, Switzerland), 14(7), 11957–11992.CrossRefGoogle Scholar
  76. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.CrossRefGoogle Scholar
  77. Van Der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704.CrossRefGoogle Scholar
  78. Van Merriënboer, J. J., Clark, R. E., & De Croock, M. B. (2002). Blueprints for complex learning: The 4C/ID-model. Educational Technology Research and Development, 50(2), 39–61.CrossRefGoogle Scholar
  79. Várkuti, B., Guan, C., Pan, Y., Phua, K. S., Ang, K. K., Kuah, C. W. K., et al. (2013). Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabilitation and Neural Repair, 27, 53–62.CrossRefGoogle Scholar
  80. Vinge, V. (1993). The coming technological singularity: How to survive in the post-human era, originally in Vision-21: Interdisciplinary science and engineering in the era of cyberspace, G. A. Landis (Ed.), NASA technical report CP-10129, 11–22. Houston: NASGoogle Scholar
  81. Wang, J., O'Kane, A. A., Newhouse, N., Sethu-Jones, G. R., & Barbaro, K. (2017). Quantified baby: Parenting and the use of a baby wearable in the Wild. Proceedings of the ACM on Human-Computer Interaction, 1, 1–19.Google Scholar
  82. Waldrop, M. M. (2016). The chips are down for Moore’s law. The semiconductor industry will soon abandon its pursuit of Moore's law. Now things could get a lot more interesting. Nature. International Weekly Journal of Science, 530(7589), 144--147.Google Scholar
  83. Warneke, B., Last, M., Liebowitz, B., & Pister, K. S. J. (2001). Smart dust: Communicating with a cubic-millimeter computer. Computer, 34(1), 44–51.CrossRefGoogle Scholar
  84. Wenger, E. (1998). Communities of practice. learning, meaning, and identity. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  85. Weiser, M. (1991). The computer for the 21st century. Scientific American, 265, 94–104.CrossRefGoogle Scholar
  86. Windmiller, J. R., & Wang, J. (2013). Wearable electrochemical sensors and biosensors: A review. Electroanalysis, 25(1), 29–46.CrossRefGoogle Scholar
  87. Wild, F., Moedritscher, F. Sigurdarson, S. (2008). Designing for change: Mash-up personal learning environments, eLearning Papers 9 (2008), resource document: Accessed 8 Nov 2018.
  88. Witzner, D. H., & Qiang, J. (2010). In the eye of the beholder: A survey of models for eyes and gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 478–500.CrossRefGoogle Scholar
  89. Wong, L.-H., & Looi, C.-K. (2011). What seams do we remove in mobile assisted seamless learning? A critical review of the literature. Computers & Education, 57(4), 2364–2381.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Economics and Social SciencesBeuth University of Applied Sciences BerlinBerlinGermany
  2. 2.RWTH Aachen University, Informatik 5 (Information Systems and Databases)AachenGermany
  3. 3.Oxford Brookes University, Performance Augmentation LabOxfordUK

Personalised recommendations