The integration of wearables in education environments to enhance teaching and learning is an emerging area of research. However, many studies lack the rigor of formal research designs and results are inconclusive. The purpose of this meta-analysis was to examine the overall effect of wearable use on learning and motivation outcomes and describe the characteristics of the studies that comprise the body of quantitative wearables research. Searches for wearables research were conducted in three databases resulting in 144 results with duplicates removed. Coding based on specific inclusion criteria resulted in 12 studies with 20 effect sizes published between January 2016 and August 2019. The overall weighted mean effect size for 20 learning and motivation outcomes was .6373 (SE = .1622). It should be noted that while this result was statistically significant (z = 3.9292, p = .0001) with 95% CI [.3194, 9552], the heterogeneity was also statistically significant. Additional weighted mean effect sizes relating to study characteristics were significant while meeting the assumption of homogeneity. A discussion of the findings, implications, and limitations are provided.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
References included in the meta-analysis are marked with an asterisk.
Anderson, L. W., Krathwohl, D. R., Bloom, B. S., & Bloom, B. S. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York, NY: Longman.
*Brewer, Z. E., Fann, H. C., Ogden, W. D., Burdon, T. A., & Sheikh, A. Y. (2016). Inheriting the learner’s view: A Google glass-based wearable computing platform for improving surgical trainee performance. Journal of Surgical Education, 73(4), 682–688. https://doi.org/10.1016/j.jsurg.2016.02.005
*Byrne, J. R., O’Sullivan, K., & Sullivan, K. (2017). An IoT and wearable technology hackathon for promoting careers in computer science. IEEE Transactions on Education, 60(1), 50–58. https://doi.org/10.1109/te.2016.2626252
Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Boston, MA: Houghton Mifflin Company.
*Chaukos, D., Chad-Friedman, E., Mehta, D. H., Byerly, L., Celik, A., Mccoy, T. H., & Denninger, J. W. (2018). SMART-R: A prospective cohort study of a resilience curriculum for residents by residents. Academic Psychiatry, 42(1), 78–83. https://doi.org/10.1007/s40596-017-0808-z
Cohen, J. (1992). A power primer. Psychological Bulletin,112(1), 155–159.
Cooper, H. M., Hedges, L. V., & Valentine, J. C. (2019). The handbook of research synthesis and meta-analysis (3rd ed.). New York, NY: Russell Sage Foundation.
Dehzangi, O., & Farooq, M. (2018, January). Wearable brain computer interface (BCI) to assist communication in the intensive care unit (ICU). In Proceedings from the 2018 IEEE international conference on consumer electronics (ICCE), Las Vegas, NV (pp. 1–4). IEEE. https://doi.org/10.1109/ICCE.2018.8326188.
de la Guía, E., Camacho, V. L., Orozco-Barbosa, L., Luján, V. M. B., Penichet, V. M., & Pérez, M. L. (2016). Introducing IoT and wearable technologies into task-based language learning for young children. IEEE Transactions on Learning Technologies,9(4), 366–378. https://doi.org/10.1109/TLT.2016.2557333.
Díaz, A., Pérez, S., & López, D. M. (2019). Adaptation component based on wearable technology to support personalized tracking of physical activity in children. Studies in Health Technology and Informatics,261, 115–121.
Divis, K., Anderson-Bergman, C., Abbott, R., Newton, V., & Emmanuel-Aviña, G. (2018). Physiological and cognitive factors related to human performance during the Grand Canyon rim-to-rim hike. Journal of Human Performance in Extreme Environments,14(1), 5. https://doi.org/10.7771/2327-2937.1095.
Duval, S., & Tweedie, R. (2000). A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association,95(449), 89–98. https://doi.org/10.2307/2669529.
Fonseca, D., Navarro, I., Renteria, I. D., Moreira, F., Ferrer, Á., & Reina, O. D. (2017). Assessment of wearable virtual reality technology for visiting world heritage buildings: An educational approach. Journal of Educational Computing Research,56(6), 940–973. https://doi.org/10.1177/0735633117733995.
Gauttier, S. (2018, July). Hospital 5.0: Enhancing nurses with the use of wearables. In Proceedings of the 32nd international BCS human computer interaction conference, Belfast, UK (pp. 1–5). Retrieved from https://ris.utwente.nl
Harrow, A. (1972). A taxonomy of psychomotor domain: A guide for developing behavioral objectives. New York, NY: Addison-Wesley Longman Ltd.
Havard, B., & Podsiad, M. (2017). Wearable computers. In T. Kidd & L. R. Morris (Eds.), Handbook of instructional systems and technology (pp. 356–365). Hershey, PA: IGI Global.
Havard, B., & Podsiad, M. (2019). Wearables for performance support and learning. International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics,9(2), 37–50. https://doi.org/10.4018/ijmdwtfe.2018070103.
Kalantari, M. (2017). Consumers’ adoption of wearable technologies: Literature review, synthesis, and future research agenda. International Journal of Technology Marketing,3, 274. https://doi.org/10.1504/ijtmkt.2017.10008634.
*Kerner, C., & Goodyear, V. A. (2017). The motivational impact of wearable healthy lifestyle technologies: A self-determination perspective on fitbits with adolescents. American Journal of Health Education, 48(5), 287–297. https://doi.org/10.1080/19325037.2017.1343161
Kirk, M. A., Amiri, M., Pirbaglou, M., & Ritvo, P. (2019). Wearable technology and physical activity behavior change in adults with chronic cardiometabolic disease: A systematic review and meta-analysis. American Journal of Health Promotion,33(5), 778–791. https://doi.org/10.1177/0890117118816278.
Klopfer, E., Yoon, S., & Rivas, L. (2004). Comparative analysis of palm and wearable computers for participatory simulations. Journal of Computer Assisted Learning,20(5), 347–359. https://doi.org/10.1111/j.1365-2729.2004.00094.x.
Knight, A., & Bidargaddi, N. (2018). Commonly available activity tracker apps and wearables as a mental health outcome indicator: A prospective observational cohort study among young adults with psychological distress. Journal of Affective Disorders,236, 31–36. https://doi.org/10.1016/j.jad.2018.04.099.
Kokotsaki, D., Menzies, V., & Wiggins, A. (2016). Project-based learning: A review of the literature. Improving Schools,19(3), 267–277. https://doi.org/10.1177/1365480216659733.
Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1964). Taxonomy of educational objectives: The classification of educational goals. Handbook II: Affective domain. New York, NY: David McKay Co., Inc.
*Kuhn, J., Lukowicz, P., Hirth, M., Poxrucker, A., Weppner, J., & Younas, J. (2016). gPhysics—Using smart glasses for head-centered, context-aware learning in physics experiments. IEEE Transactions on Learning Technologies, 9(4), 304–317. https://doi.org/10.1109/tlt.2016.2554115
Lee, U., Han, K., Cho, H., Chung, K.-M., Hong, H., Lee, S.-J., et al. (2019). Intelligent positive computing with mobile, wearable, and IoT devices: Literature review and research directions. Ad Hoc Networks,83, 8–24. https://doi.org/10.1016/j.adhoc.2018.08.021.
Lee, V. R., Drake, J., & Williamson, K. (2015). Let’s get physical: K-12 students using wearable devices to obtain and learn about data from physical activities. TechTrends,59(4), 46–53. https://doi.org/10.1007/s11528-015-0870-x.
*Lindberg, R., Seo, J., & Laine, T. H. (2016). Enhancing physical education with exergames and wearable technology. IEEE Transactions on Learning Technologies, 9(4), 328–341. https://doi.org/10.1109/tlt.2016.2556671
Lipsey, M., & Wilson, D. (2001). Practical meta-analysis. Thousand Oaks, CA: Sage.
Mann, S. (1996, November). ‘Smart Clothing’: Wearable multimedia computing and ‘personal imaging’ to restore the technological balance between people and their environments. ACM Multimedia, 96, 163–174. Retrieved from http://www.nomads.usp.br.
Mann, S. (2014). Wearable computing. In The encyclopedia of human-computer interaction (2nd ed). Interaction Design Foundation. Retrieved from https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/wearable-computing.
Markvicka, E., Rich, S., Liao, J., Zaini, H., & Majidi, C. (2018, March). Low-cost wearable human-computer interface with conductive fabric for STEAM education. In Proceedings of the 8th IEEE integrated STEM education conference (ISEC), Princeton, NJ, New Jersey (pp. 161–166). IEEE. https://doi.org/10.1109/isecon.2018.8340469.
McCann, J., & Bryson, D. (Eds.). (2009). Smart clothes and wearable technology. Cambridge, England: Woodhead.
Menezes, P. (2017). An augmented reality u-academy module: From basic principles to connected subjects. International Journal of Interactive Mobile Technologies,11(5), 105–117. https://doi.org/10.3991/ijim.v11i5.7074.
*Merkouris, A., Chorianopoulos, K., & Kameas, A. (2017). Teaching programming in secondary education through embodied computing platforms. ACM Transactions on Computing Education, 17(2), 1–22. https://doi.org/10.1145/3025013
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine,6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097.
Murphy, J. (2003). Task-based learning: The interaction between tasks and learners. ELT Journal,57(4), 352–360. https://doi.org/10.1093/elt/57.4.352.
Nanjappan, V., Liang, H. N., Lau, K., Choi, J., & Kim, K. K. (2017, November). Clothing-based wearable sensors for unobtrusive interactions with mobile devices. In Proceedings of the 2017 international SoC design conference (ISOCC), Seoul, Korea (pp. 139–140). IEEE. https://doi.org/10.1109/ISOCC.2017.8368837.
*Nederveen, J. P., Thomas, A. C. Q., & Parise, G. (2019). Examining the first-person perspective as appropriate prelaboratory preparation. Advances in Physiology Education, 43(3), 317–323. https://doi.org/10.1152/advan.00213.2018
Ngai, G., Chan, S. C. F., Ng, V. T. Y., Cheung, J. C. Y., Choy, S. S. S., Lau, W. W. Y., & Tse, J. T. P. (2010, April). i*CATch: A scalable plug-n-play wearable computing framework for novices and children. In Proceedings of the SIGCHI conference on human factors in computing systems, Atlanta, GA (pp. 443–452). ACM. https://doi.org/10.1145/1753326.1753393.
*Oh, S., So, H.-J., & Gaydos, M. (2018). Hybrid augmented reality for participatory learning: The hidden efficacy of multi-user game-based simulation. IEEE Transactions on Learning Technologies, 11(1), 115–127. https://doi.org/10.1109/tlt.2017.2750673
Orwin, R. G. (1983). A fail-safe n for effect size in meta-analysis. Journal of Educational Statistics,8(2), 157–159. https://doi.org/10.2307/1164923.
Park, S., Locher, I., Savvides, A., Srivastava, M. B., Chen, A., Muntz, R., & Yuen, S. (2002, October). Design of a wearable sensor badge for smart kindergarten. In Proceedings of the sixth international symposium, Seattle, Washington (pp. 231–238). IEEE. https://doi.org/10.1109/ISWC.2002.1167252.
Peppler, K., Danish, J., Zaitlen, B., Glosson, D., Jacobs, A., & Phelps, D. (2010, June). BeeSim: Leveraging wearable computers in participatory simulations with young children. In Proceedings of the 9th international conference on interaction design and children, Barcelona, Spain (pp. 246–249). ACM. https://doi.org/10.1145/1810543.1810582.
Pintrich, P. R., & Schunk, D. H. (2002). Motivation in education: Theory, research, and applications (2nd ed.). Englewood Cliffs, NJ: Merrill, Prentice-Hall International.
Plass, J. L., Homer, B. D., & Kinzer, C. K. (2015). Foundations of game-based learning. Educational Psychologist,50(4), 258–283. https://doi.org/10.1080/00461520.2015.1122533.
Rogers, E. M. (1983). Diffusion of innovations. New York, NY: The Free Press.
Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin,86(3), 638–641. https://doi.org/10.1037/0033-2909.86.3.638.
Schaefer, S. E., Ching, C. C., Breen, H., & German, J. B. (2016). Wearing, thinking, and moving: Testing the feasibility of fitness tracking with urban youth. American Journal of Health Education,47(1), 8–16. https://doi.org/10.1080/19325037.2015.1111174.
Schaule, F., Johanssen, J. O., Bruegge, B., & Loftness, V. (2018, March). Employing consumer wearables to detect office workers’ cognitive load for interruption management. In Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, New York, NY (Vol. 2(1), p. 32). https://doi.org/10.1145/3191764.
Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S., Thilakarathna, K., et al. (2017). A survey of wearable devices and challenges. IEEE Communications Surveys & Tutorials,19(4), 2573–2620. https://doi.org/10.1109/COMST.2017.2731979.
*Shadiev, R., Hwang, W. Y., & Liu, T. Y. (2018). Investigating the effectiveness of a learning activity supported by a mobile multimedia learning system to enhance autonomous EFL learning in authentic contexts. Educational Technology Research & Development, 66(4), 893–912. https://doi.org/10.1007/s11423-018-9590-1
*Sobko, T., & Brown, G. (2019). Reflecting on personal data in a health course: Integrating wearable technology and ePortfolio for eHealth. Australasian Journal of Educational Technology, 35(3). https://doi.org/10.14742/ajet.4027
Soni, J., & Goodman, R. (2017). A mind at play: How Claude Shannon invented the information age. New York, NY: Simon & Schuster.
Tofel-Grehl, C., Fields, D., Searle, K., Maahs-Fladung, C., Feldon, D., Gu, G., et al. (2017). Electrifying engagement in middle school science class: Improving student interest through e-textiles. Journal of Science Education and Technology,26(4), 406–417. https://doi.org/10.1007/s10956-017-9688-y.
ul Amin, R., Inayat, I., & Shazad, B. (2015, April). Wearable learning technology: A smart way to teach elementary school students. In Proceedings of the 2015 12th learning and technology conference, Jeddah, Saudi Arabia (pp. 1–5). IEEE. https://doi.org/10.1109/LT.2015.7587221.
Valentine, J. C., Pigott, T. D., & Rothstein, H. R. (2010). How many studies do you need? A primer on statistical power for meta-analysis. Journal of Educational and Behavioral Statistics,2(2), 215–247.
Van Til, K., McInnis, M. G., & Cochran, A. (2019). A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder. Bipolar Disorders. https://doi.org/10.1111/bdi.12849.
Walker, A., & Leary, H. (2009). A problem based learning meta analysis: Differences across problem types, implementation types, disciplines, and assessment levels. Interdisciplinary Journal of Problem-Based Learning,3(1), 12–43. https://doi.org/10.7771/1541-5015.1061.
Wilson, D. B. (2006). Meta-analysis macros for SAS, SPSS, and Stata [Software]. http://mason.gmu.edu/~dwilsonb/ma.html.
Wolters, C., & Pintrich, P. R. (1998). Contextual differences in student motivation and self-regulated learning in mathematics, english, and social studies classrooms. Instructional Sciences,26(1–2), 27–47. https://doi.org/10.1023/A:1003035929216.
*Yang, X., Lin, L., Cheng, P. Y., Yang, X., Ren, Y., & Huang, Y. M. (2018). Examining creativity through a. virtual reality support system. Educational Technology Research and Development, 66(5), 1231–1254. https://doi.org/10.1007/s11423-018-9604-z
Yen, H.-Y., & Chiu, H.-L. (2019). The effectiveness of wearable technologies as physical activity interventions in weight control: A systematic review and meta-analysis of randomized controlled trials. Obesity Reviews,10, 1485. https://doi.org/10.1111/obr.12909.
Zhou, H., Lee, H., Lee, J., Schwenk, M., & Najafi, B. (2018). Motor planning error: Toward measuring cognitive frailty in older adults using wearables. Sensors,18(3), 926. https://doi.org/10.3390/s18030926.
Conflict of interest
The authors declare that they have no conflict of interest.
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.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Field parameters for codebook
|Study ID||Uniquely identifies a study; first author’s last name and year|
|Coder||The individual who coded the entry|
|Publication type||Publication type “1 Journal article, 2 Book chapter, 3 Dissertation, 4 Conference proceeding, 5 Technical report, 6 Other, 7 Unknown”|
|Published date||Publication date “1 2016, 2 2017, 3 2018, 4 2019”|
|Continent||The continent where study was conducted “1 North America, 2 South America, 3 Asia, 4 Africa, 5 Australia, 6 Europe, 7 Antarctica”|
|Study environment||Study environment “1 K-12, 2 Higher Education, 3 Business/Industry/Military, 4 Healthcare, 5 Other (type in), 6 More than one (type in)”|
|Participant age||Age(s) of participants “1 Adults 25 + , 2 Adults 18–24, 3 Minors < 18, 4 Adults 65 + , 5 More than one (type in”|
|Participant gender||Gender of participants “1 Mixed, 2 Male, 3 Female”|
|Pedagogical strategies||The pedagogical strategies used within the study conducted “1 Problem-based, 2 Project-based/Collaborative, 3 Game-based, 4 Task-based, 5 None, 6 Other (Type in)”|
|Wearables||Type of wearable(s) used in the study “1 Implantables, 2 Smartwatches, 3 Smart Jewelry, 4 Fitness Trackers, 5 Smart Clothing, 6 Head-Mounted Displays, 7 Health Related Devices, 8 Sensors, 9 Other (type in), 10 More than one (type in)”|
|Selection bias||Systematic differences between groups at baseline “1 Low risk, 2 High risk, 3 Unclear risk”|
|Performance bias||Something other than the intervention affects groups differently (blinding of participants) “1 Low risk, 2 High risk, 3 Unclear risk”|
|Attrition bias||Participant loss affects initial group comparability “1 Low risk, 2 High risk, 3 Unclear risk”|
|Detection bias||Method of outcome assessment affects group comparisons (blinding of data collectors) “1 Low risk, 2 High risk, 3 Unclear risk”|
|Reporting bias||Selective reporting of outcomes “1 Low risk, 2 High risk, 3 Unclear risk”|
|Reliability provided||Was reliability assessed? “1 Yes, 2 No”|
|Validity provided||Was validity assessed? “1 Yes, 2 No”|
|Research design||Study design “1 Two-group pretest–posttest, 2 Two-group posttest, 3 One-group pretest–posttest, 4 One-group posttest, 5 Unclear, 6 Other (type in)”|
|Time relative to treatment||Time after treatment was measured “1 Immediately, 2 Days, 3 Over one week”|
|Sample assignment||Type of sample assignment “1 Individual, 2 Group, 3 Program area, 4 Unclear”|
|Sample design||Type of sampling “1 Random, 2 Matching, 3 Convenience, 4 Quota, 5 Other (type in)”|
|Total sample size||Total reported sample size “type in”|
|Treatment sample size||Total reported treatment sample size “type in”|
|Control sample size||Total reported control sample size “type in”|
|Outcome measured||The outcome measured in the study “1 Cognitive, 2 Affective, 3 Psychomotor, 4 Motivation, 5 Engagement, 6 Support/Performance, 7 Functionality/Design Evolution, 8 Evaluation, 9 Applications, 10 Other (type in)”|
|Construct||The construct(s) used in this study “type in”|
|Measure scale||The measurement scale of the outcome “1 Continuous, 2 Discrete, 3 Ordinal, 4 Nominal, 5 Other (type in)”|
|Effect size||The effect size of outcome, if reported “type in”|
|Reason for exclusion||The reason the publication is excluded from this meta-analysis “1 Insufficient data to calculate effect size, 2 High risk of bias, 3 Wearable application unclear, 4 Other (type in)”|
About this article
Cite this article
Havard, B., Podsiad, M. A meta-analysis of wearables research in educational settings published 2016–2019. Education Tech Research Dev (2020). https://doi.org/10.1007/s11423-020-09789-y
- Learning outcomes
- Technology integration