Wearable Technology Possibilities for Education

This  paper was written as part of my participation in the graduate course Educational Hardware Systems at the George Washington University. It is one of the required courses for the Education Technology Leadership concentration in the Master of Arts in Education and Human Development.

Wearable Technology Possibilities for Education


Networked computing is radically changing how and what we learn. Social constructivism and connectivism emerged in the early 1990 simultaneously with consumer internet as systems (mobile technology) and processes (constructivism) found a point of synergy.

Wearable technology is poised to see increasing consumer adoption (ABI Research, 2014), and with it, new forms of engagement with people and places, and new opportunities to better understand our selves and the world around us.

Considering the landscape and social impact of wearable technology, this paper seeks to identify the distinguishing characteristics and affordances of wearable technology, the potential impact on human interactions and our relationship with technology in order to inform development of educational applications.


Understanding Wearables

Wearable technologies are digital devices with communication and interaction capabilities embedded or integrated into something that can be comfortably worn on the body that performs tasks independently and intelligently based on user profiles and sensed information (Tehrani & Michael, 2014). Wearables come in many forms including jewelry, watches, patches, glasses, conventional garments, and hats (ABI Research, 2014). Table 1 shows Baber’s (2001) continuum of wearable device functionality with full featured computing devices at one end, sensing clothing with limited processing power at the other and interactive but narrowly functioning information appliances in the middle.


Table 1: Baber (2001) describes a continuum of device functionality.

Sensors Information Appliances Wearables
Limited processing power Interactive but narrowly functioning Fully featured computing devices


Ding & Lin (2009) describe several user motivations for engaging with mobile devices which involve receiving information from the device in response to a need; wearables as interfaces to sensors and information appliances, though, can also be set up to anticipate and independently respond to needs perceived by the device.

Google Glass and the as-yet unreleased Apple Watch are two examples on the full-featured end of the functionality continuum. These networked, interactive devices have spatial and locational awareness, are always on, can operate autonomously and offer broad functionality while remaining unobtrusive, largely hands-free, and intuitive to use (Apple, 2014; Parslow, 2014). Unless indicated, this paper focuses primarily on Google’s Glass and occasionally Apple’s Watch as the fully featured wearables.

Wearable devices are accessible at a glance whereas mobile devices are often in purses or pockets requiring the users’ full attention and tactile interactions to operate. Some wearables afford natural means of interaction like voice or subtle body movements allowing the user hands-free engagement with the technology while others function in the background without the need for human interaction at all. Accessing a mobile device may only take a few seconds longer than a wearable, yet it is reasonable to anticipate minor savings could contribute to greater attention and reduced workflow interruptions.

Wearable Affordances

Roach-Higgins & Eicher (as cited in Johnson & Lennon, 2014) suggest traditional worn clothing communicates between wearer and viewer, extends abilities, and influences how we experience, and are experienced by others. Similarly, worn technology becomes part of the user, communicating, extending physical and cognitive abilities, augmenting our experiences through information search, capture, storage, and retrieval, and enhancing physical and biological sensing thus blurring the line between human and machine (Benditt, 1999).

Physical Enhancement

Wearable technologies can enhance fully functioning senses or mitigate lost or disabled senses. Audio, video, and vibrational signals from mobile devices are common interface forms though the device must be hand held, or in contact with the body to be effective. Glass, on the other hand, simulates a large display using small near-the-eye projections and bone conduction in the eyeglass arms that discretely deliver audio to the user. Further, vibrotactile feedback and haptic response systems embedded in garments can simulate physical contact and pressure increasing the user’s tactile sensitivity.

Cognitive Enhancement

Studies demonstrate how wearables enhance memory by effective storage, retrieval, and delivery of information to the user on demand, or as a form of performance support (Lee, 2014; Ockerman & Pritchett, 2004; Shankland, 2012). Users are increasingly transferring the burden of memory from the brain to the device in a kind of merger of biological and digital processes into a hybrid memory (Clowes, 2012). Consequently, wearables become part of the user performing as memory assistants and are likely to evoke a psychological sense of unity with the device and its’ capabilities.

Physiological Understanding

Conductive threads make it possible to embedded sensors and circuitry into a garment’s fabric (Palomo-Lovinski, 2008; Tao, 2005) allowing for constant monitoring of an individual’s vital signs. Motion sensors strategically placed on the body for effective movement readings (Guo, He, & Gao, 2012) and maximising inter-device communication (Vallejo, Recas, del Valle, & Ayala, 2013) combined with sensed information about the environment and user context (Jin et al., 2014) can be used to translate discrete contexts and movements into a gestalt of user intention (Leutheuser, Schuldhaus, & Eskofier, 2013).

Contextual Understanding

Mobile devices are capable of sensing environmental position and conditions relative to the users’ physical context and deliver location-relevant notifications reflecting their interests and social relationships (Humphreys, 2013). As a portal to social media engagement with a space, users can experience rich overlays of digital content relevant to their location (Humphreys, 2013; Schwartz & Hochman, 2014). From a research and learning perspective, user contributed data tagged with contextual information can be harvested and filtered to better understand how spaces are used (Schwartz & Hochman, 2014).

While both mobile and wearable devices employ a wide array of sensors, wearables have the added advantage of flexible positioning contributing to greater quality and accuracy. The wearable offers fast and easy access to content with minimal interruptions to work flow. Additionally, push notifications can be targeted with increasing precision and focus (Salz, 2014) connecting user-submitted data with social media profiles.

Human Relationships

Like mobile devices, wearables offer quick and easy connections to other people. Accessing a wearable in social situations is less obvious and therefore more socially acceptable because it does not appear to occupy the user’s full attention like a hand-held mobile device. On the other hand, tension may arise where not all social participants have or understand the wearable technology in use (Garfinkel, 2014).

Wearables also provide a unique first person points of view using a head-mounted hands-free camera and will soon be able to provide hands-free video conferencing with a face view of the wearer (Kimura & Horikoshi, 2014). By choice or through automation, information from worn sensors and information appliances can be shared socially allowing the wearer to share data without active participation.

Handheld mobile technology, Beloff (2008) suggests, is akin to carrying in one’s pocket a portal to virtual private spaces even in when one is physically moving in public spaces. Always on, always accessible, and intuitively operated, wearable technology merges public and private spaces even more seamlessly than hand-held devices as wearables and their users continually feed and receive data to and from online social spaces (Hjorth & Lim, 2012).

Education Applications

Performance Support

Wearable technology emerged from military applications as field-based performance support for equipment service and maintenance in the field (Chinnock, 1998). Studies revealed a shift in how people performed following instructions from a wearable. Experienced workers on their own addressed peripheral tasks while performing the target task. Receiving performance support from a wearable, the same workers were less likely to stray from the prescribed actions performing only those tasks that were issued by the computer (Baber, 2001). This finding raises an important concern for wearables in education. Learners may demonstrate a high degree of compliance, but show little initiative to explore beyond given directions. In the context of experiential and connectivist learning approaches, it is important to implement learning support in a way that mitigates this effect while encouraging inquiry and initiative.

While wearables offer much the same functionality as mobile devices, there are unique affordances that offer engaging and effective learning experiences.

Multi-sensory Interfaces

Because wearables are in constant physical contact with the user as functional garments, vibrotactile feedback has wider utility. Such wearables perform as feedback devices and are used to develop specific motor skills (Lieberman & Breazeal, 2007), enhance tactile sensitivity (Ying et al., 2012) and provide navigational support (Zelek, Bromley, Asmar, & Thompson, 2003). Full body suits could sense environmental conditions and provide haptic output using embedded vibration motors to compensate for lost or disabled senses (Profita, 2014). Enabled garments could also interact with applications to simulate pressure creating the opportunity for immersive sensory learning experiences.

Non-invasive neural computer interfaces are particularly interesting for education. If we are to create a fully wearable independent individualised learning experiences we need also to understand how the brain receives, stores, processes, connects, retrieves, and delivers information and then how to perceive, measure, and possibly influence those processes (Bahr, 2001; Coates, 2008; Liao et al., 2012). Commercial electroencephalography (EEG) is still expensive and has limited utility with available applications focusing only on learning to control EEG waves (Emotiv Inc., 2014). An available free software development kit may encourage developers to explore the possibilities and affordances of such devices for control and sensing.


The wearable industry is dominated by healthcare, military, and industrial applications (Walker, 2013) where they play a significant role in training and performance support. Walker also mentions widespread adoption in the fitness industry of personal training sensors and health monitoring appliances. Individuals can use such devices to learn about their own personal health and physicians may employ such monitors for patient education. Collected data can motivate patients to make positive lifestyle changes (Shuger et al., 2011).

As the mobile and wearable environment is changing the way we engage with content, so too is the way content itself is stored, retrieved, and delivered. Granular pieces of information are stored as discrete elements, tagged with content and purpose descriptions, and then linked with logical identifiers. As application interfaces and content sequencing algorithms are developed utilizing information architectures like SCORM, it will be possible for a learner’s device to sense location and activity, identify learning opportunities using current data relevant to the learner’s needs, deliver content and assess responses in situ.

A big benefit of mobile wearable technology is the opportunity to automate many communication processes to benefit not just one’s self, but the greater human population and physical environment. As walking sensor-laden transmitters, humans can gather environmental, climatic, infrastructural, biological, and social data through the course of the day without any additional effort beyond donning the sensors. From an educational perspective, this offers two benefits. First for the user who benefits from enhanced cognitive and physical abilities, and physiological and environmental understandings. Secondly, aggregated raw data from worn sensors can inform research, decision-making, and public policy.


Early experiences with technology in education focused on integration: how to use a computer for teaching and learning. Discussions about technology integration still treat computing devices as add-ons to what happens in the classroom reflecting the separation of the person from the computing device. Wearables challenge the perceived separation of people and computers causing us to rephrase the question, “What can I do with computers in the classroom?” to “What can I do with learners who have computers?” The distinction is subtle, but one that shifts the focus away from the device and on to the individual.

K12 and higher education experience with wearables is limited and Walker’s (2013) Market Assessment makes no mention at all of educational applications. Google’s Glass Explorer Program now has some wearables in teachers’ hands and early implementations, as with most new technologies, reflect old teaching strategies such as performing web searches, recording lectures (Sivakumar, 2014), providing feedback on written work, and sharing print materials (Hall, 2014).

Some reports, however, describe learning experiences that capitalize on the wearable’s unique capabilities such as inquiry based, location specific, contextual, interactive learning experiences (Suarez, Ternier, Kalz, & Specht, 2014), language learning using speech recognition and synthesis in augmented reality applications (Nooriafshar, 2013), and capitalizing on the ease with which records can be taken and information shared to digital workspaces (Hall, 2014).

Augmented Experiences

Humphreys (2013) suggests that awareness of others’ experiences and opinions about physical spaces affects the user’s perceptions of that same physical space. Positive reports may enhance a users’ experience of a space while negative reports may predispose a user to a negative experience. Experiential and connectivist learning approaches emphasize the importance of learner experiences and discovery in creating knowledge. Wearable contextual learning systems will have to balance delivery of known content with opportunities for discovery.

A New Kind of Network

The growing prevalence of wearables creates the potential for an enormous mobile sensor array as humans themselves become part of the technology. Cisco (2014) reports that 22 million wearable devices in 2013 were generating significant network activity across the planet, a number predicted to increase more than eightfold within five years.

A world full of interconnected sensors, information appliances, wearable, and mobile devices all sharing user-interpreted and raw data on social media creates what is variously called the “Semantic Web” or “Transcendent Web” (Michael & Michael, 2013; Sabbagh, Karam, Acker, & Rahbani, 2011). Here, user-entered data gives context, provides details, makes connections, overlays opinion, emotion, and experience to events and locations. This adds dimensionality to raw sensor data extending the utility of the data beyond the wearer.

Demographically and contextually rich open data has countless possibilities for research and education. Wearable users may be motivated by applications to behave in a desired way (Khaled, Barr, Noble, & Biddle, 2006; Vassileva, 2012) resulting in gathering of targeted data. For example, a researcher could create a gamified application that prompts user to visit certain locations, or enter certain information that they might not otherwise. The user achieves a gaming goal and the researcher acquires needed data.

Additionally, as users create logical links between discrete information items they lay the foundation for computer intelligence along the lines of a digital neural network.

Challenges within the field

Privacy and Social Acceptance

Hong (2013) recalls that many new technologies, including the camera, met with resistance and fear. He notes that experiences with new technologies are limited and early expectations often differ from the reality that emerges. These early expectations, he observes, are also bound to change over time as the technology’s affordances are discovered and adopted.

Google’s Glass raised significant alarm when announced. People were uncomfortable about the ease with which users could record anything they observed (Gross, 2014). Surreptitious recordings are also possible with a mobile device but the concern disappears when the device is put away. The face-worn device, though not always recording nor even powered on, is always pointed at whomever the user is looking at. From another point of view, some people view Google’s Glass with great curiosity and have surreptitiously taken photos of the wearer without the wearer’s consent (Swan, 2014). Interestingly, Apple’s watch has not encountered the same public push back likely because the form factor is not, so to speak, as in your face as Google’s Glass.

A mobile device can facilitate connections with others, but it can also hinder face-to-face interactions (Przybylski & Weinstein, 2012). While a mobile can be tucked away in a purse or pocket, there may be more resistance to removing a wearable because of the ties to appearance and identity (Johnson & Lennon, 2014).

Ease of sharing and making data public, though, is also an element of concern. Tunick (2013) explores notions of privacy in light of the counterargument that if you have nothing to hide, you have nothing to fear. He describes non-legal forms of punishment that can occur when out-of-context data emerges and is linked to an individual who, as a result, experiences harsh treatment. Such artifacts can exist online indefinitely causing serious and long-term damage to an individual’s reputation and well-being. Some such issues can be mitigated through public policy and during product development itself (Rubenstein & Good, 2013).

Hjorth and Lim (2012) described mobile devices as a portal to private and intimate spaces within public spaces pointing to the emerging intersection of public and private experiences. The nature of wearables, like Glass, make it challenging to monitor a child’s activity, to provide responsible supervision online, and to share digital experiences (O’Keeffe & Clarke-Pearson, 2011). Glass is, by its nature, a very personal and private device with a short line to a very public forum.


Not having a common form factor amongst wearable devices, standardization and interoperability present design and implementation challenges. Sensors must transmit gathered data to another device and some information appliances rely on a mobile interface for full functionality. Additionally, situations employing multiple sensors present a wide variety of communication challenges.

Ongoing research seeks to optimize wearable sensor efficacy by determining best sensor placement (Guo et al., 2012; Haar, Fees, Trost, Crowe, & Murray, 2013), creating algorithms that accurately translate sensed movement into human behaviours (Leutheuser et al., 2013), tuning strategies to compensate for signal attenuation on inter-connected body-mounted sensor networks (Vallejo et al., 2013), developing a means for reliable inter-device communication (Cubo, Nieto, & Pimentel, 2014), creating functional flexible silicon circuitry embedded in fabrics (Healy, Donnelly, O’Neill, Alderman, & Mathewson, 2006; Palomo-Lovinski, 2008), and balancing form and function factors to create comfortable wearables (Haar et al., 2013; Simone & Kamper, 2005).

As research tools, wearables will provide increasingly detailed and accurate data about virtually all aspects of nature and society. The quantity of data is likely to put pressure on research science further opening the door to so-called citizen science and crowd-sourced data analysis. Such data could provide working material for the wearable users’ learning activities reflecting real practical applications of the skills in question.

Power needs

Always on electronic devices have power requirements that must accommodate mobility and provide extended operation. Research continue into how best to meet mobile energy needs. Solutions range from solar (Pool, 2008) to new form batteries (Wooliey et al., 2004) to minimising power requirements using new technologies (Pearson, Buchanan, & Thimbleby, 2014) outsourcing computation to cloud computers (Ivan & Popa, 2014), and kinetic energy generated by the user (Palomo-Lovinski, 2008; Xu, Yang, Zhou, & Liu, 2013). Until then, users must carry cables and seek out electric power points to charge their devices.

Conclusion & Future Research

Much is said about the outdated industrial factory model of education still in practice (Pinar, 1992; Serafini, 2002). Computers introduced robust and accessible multimedia into classrooms about the same time as hypertext challenged linear-sequential approaches to content opening the door for self-directed study. Mobile technology untethered the learner from the desktop bringing computing power into new spaces and with it location-aware delivery of contextually relevant content. Wearable information systems will also have a tremendous impact on education changing the way we teach and learn. Wearable technology could untether learners not only from bricks and mortar classrooms, but from devices themselves as they become extensions of our selves offering  learning networks, and dynamic, relevant, contextual, meaningful, and timely learning experiences that reflect engaging and learner-driven inquiry (Füllsack, 2013).

As wearables still have very low adoption in educational enterprises, future research should focus on how military, industrial, and healthcare industries employ wearables for training purposes. Existing educational deployments should be studied to document best practice in terms of contributions to student learning and engagement over the long term. Additionally, a study of success factors in independent self-guided learning using wearable and mobile technology will inform a transition away from traditional industrial learning models to a model that better employs available technology to maximise learning potential.

Finally, given the predicted merger of biological and digital capabilities in humans, questions of self-identity are likely to arise. Understanding the impact of digital enhancement on perceptions of self will begin to address important issues of well-being and psychological adjustment in the coming age.


ABI Research. (2014). 90M Wearable computing devices will be shipped in 2014. Microwave Journal, 57(4), 66.

Apple. (2014). Apple Watch - Features. Retrieved October 13, 2014, from http://www.apple.com/watch/features/

Baber, C. (2001). Wearable computers: A human factors review. International Journal of Human-Computer Interaction, 13(2), 123–145. doi:10.1207/S15327590IJHC1302_3

Bahr, M. (2001). A brave new world of information. Social Alternatives, 20(1), 41–46.

Beloff, L. (2008). The curious apparel : Wearables and the Hybronaut. Social Fabrics, 8(1).

Benditt, J. (1999). Humachines. Technology Review, 102(3), 8.

Chinnock, C. (1998). DARPA describes vision of wearable computing. Military & Aerospace Electronics, 9(10), 1–6.

Cisco. (2014). Cisco visual networking index: Global mobile data traffic forecast update, 2013–2018. Retrieved from http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white_paper_c11-520862.html

Clowes, R. W. (2012). Hybrid memory, cognitive technology and self. In Proceedings of the 5th AISB Symposium on Computing and Philosophy (pp. 2–12).

Coates, S. D. (2008). Neural interfacing: Forging the human-machine connection. In Synthesis Lectures on Biomedical Engineering (Vol. 3, pp. 1–112). doi:10.2200/S00148ED1V01Y200809BME022

Cubo, J., Nieto, A., & Pimentel, E. (2014). A cloud-based Internet of Things platform for ambient assisted living. Sensors. doi:10.3390/s140814070

Ding, W., & Lin, X. (2009). Information architecture: The design and integration of information spaces. In Synthesis Lectures on Information Concepts, Retrieval, and Services (Vol. 1, pp. 1–169). doi:10.2200/S00214ED1V01Y200910ICR008

Emotiv Inc. (2014). Applications. Retrieved from http://www.emotiv.com/store/app/

Füllsack, M. (2013). Constructivism and computation: Can computer-based modeling add to the case for constructivism?. Constructivist Foundations, 9(1), 7–16. Retrieved from http://proxygw.wrlc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=93648527&site=ehost-live

Garfinkel, S. (2014). Glass, Darkly. MIT Technology Review, 117(2), 70–77.

Gross, A. (2014, March 4). What’s the problem with Google Glass. The New Yorker. Retrieved from http://www.newyorker.com/business/currency/whats-the-problem-with-google-glass

Guo, Y., He, W., & Gao, C. (2012). Human activity recognition by fusing multiple sensor nodes in the wearable sensor systems. Journal of Mechanics in Medicine and Biology, 12(05), 1250084. doi:10.1142/S0219519412500844

Haar, S., Fees, B., Trost, S., Crowe, L. K., & Murray, A. (2013). Design of a garment for data collection of toddler language and physical activity. Clothing and Textiles Research Journal, 31(2), 125–140. doi:10.1177/0887302X13478161

Hall, R. P. (2014). Teaching using Google Glass and apps. The Journal of Interactive Technology & Pedagogy. Retrieved from http://jitp.commons.gc.cuny.edu/teaching-using-google-glass-and-apps/

Healy, T., Donnelly, J., O’Neill, B., Alderman, J., & Mathewson, A. (2006). Silicon fibre technology development for wearable and ambient electronics applications. International Journal of High Speed Electronics and Systems, 16(2), 713–722.

Hjorth, L., & Lim, S. S. (2012). Mobile intimacy in an age of affective mobile media. Feminist Media Studies, 12(4), 477–484. Retrieved from http://proxygw.wrlc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=84923916&site=ehost-live

Hong, J. (2013). Considering privacy issues in the context of Google Glass. Communications of the ACM, 56(11), 10–11. doi:10.1145/2524713.2524717

Humphreys, L. (2013). Foursquare and the parochialization of public space. Retrieved October 08, 2014, from http://spir.aoir.org/index.php/spir/article/viewFile/784/pdf

Ivan, C., & Popa, R. (2014). Cloud based cross platform mobile applications. Advances in Computer Science: An International Journal, 3(2), 69–77.

Jin, M., Zou, H., Weekly, K., Jia, R., Bayen, A. M., & Spanos, C. J. (2014). Environmental sensing by wearable device for indoor activity and location estimation. In 40th Annual Conference of the IEEE Industrial Electronics Society (IECON). Retrieved from http://arxiv.org/ftp/arxiv/papers/1406/1406.5765.pdf

Johnson, K. K. P., & Lennon, S. (2014). The social psychology of dress. Retrieved October 06, 2014, from http://www.bergfashionlibrary.com/page/The$0020Social$0020Psychology$0020of$0020Dress/the-social-psychology-of-dress

Khaled, R., Barr, P., Noble, J., & Biddle, R. (2006). Investigating Social Software as Persuasive Technology. In Persuasive Techology (pp. 104–107). Retrieved from http://www.researchgate.net/publication/225105886_Investigating_Social_Software_as_Persuasive_Technology/file/60b7d529c30292931a.pdf

Kimura, S., & Horikoshi, T. (2014). Prototype glasses-type device with videophone. NTT DOCOMO Technical Journal, 15(3), 10–16.

Lee, C. T. S. (2014). Hands-free performance support. T&D, (April), 108–110.

Leutheuser, H., Schuldhaus, D., & Eskofier, B. M. (2013). Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PloS One, 8(10), e75196. doi:10.1371/journal.pone.0075196

Liao, L.-D., Chen, C.-Y., Wang, I.-J., Chen, S.-F., Li, S.-Y., Chen, B.-W., … Lin, C.-T. (2012). Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. Journal of Neuroengineering and Rehabilitation, 9(January), 5. doi:10.1186/1743-0003-9-5

Lieberman, J., & Breazeal, C. (2007). Development of a wearable vibrotactile feedback suit for accelerated human motor learning. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 4001–4006).

Michael, K., & Michael, M. (2013). No limits to watching? Communications of the ACM, 56(11), 26–28. doi:10.1145/2527187

Nooriafshar, M. (2013). Google Glass and its potential uses in digital education. In Research Colloquia and Showcase 2013 (pp. 9–14). Springfield, QLD: University of Southern Queensland.

O’Keeffe, G. S., & Clarke-Pearson, K. (2011). The impact of social media on children, adolescents, and families. Pediatrics, 127(4), 800–4. doi:10.1542/peds.2011-0054

Ockerman, J. J., & Pritchett, A. R. (2004). Improving performance on procedural tasks through presentation of locational procedure context: An empirical evaluation. Behaviour & Information Technology, 23(1), 11–20. doi:10.1080/01449290310001641284

Palomo-Lovinski, N. (2008). Extensible dress: The future of digital clothing. Clothing and Textiles Research Journal, 26(2), 119–130. doi:10.1177/0887302X07310078

Parslow, G. R. (2014). Commentary: Google Glass: A head-up display to facilitate teaching and learning. Biochemistry and Molecular Biology Education, 42(1), 91–92. doi:10.1002/bmb.20751

Pearson, J., Buchanan, G., & Thimbleby, H. (2014). Designing for digital reading. Synthesis Lectures on Information Concepts, Retrieval, and Services. doi:10.2200/S00539ED1V01Y201310ICR029

Pinar, W. F. (1992). “Dreamt Into Existence by Others”: Curriculum Theory and School Reform. Theory into Practice, 92(31), 228–235.

Pool, R. (2008). Nanotech tonic. Engineering & Technology, 25 October, 62–66.

Profita, H. P. (2014). Smart garments: An on-body interface for sensory augmentation and substitution. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct Publication (pp. 331–336). New York, New York, USA: ACM Press. doi:10.1145/2638728.2638840

Przybylski, A. K., & Weinstein, N. (2012). Can you connect with me now? How the presence of mobile communication technology influences face-to-face conversation quality. Journal of Social and Personal Relationships, 30(3), 237–246. doi:10.1177/0265407512453827

Rubenstein, I. S., & Good, N. (2013). Privacy by design: A counterfactual analysis of Google and Facebook privacy incidents. Berkeley Technical Law Journal, 28(2), 1133–1412. Retrieved from http://scholarship.law.berkeley.edu/btlj/vol28/iss2/6

Sabbagh, K., Karam, D., Acker, O., & Rahbani, J. (2011). Designing the transcendent web: The power of Web 3.0.

Salz, P. A. (2014). The importance of “Push” in the app age. EContent, 37(7), 32–32.

Schwartz, R., & Hochman, N. (2014). The social media life of public spaces: Reading places through the lens of geo-tagged data. In R. Wilken & G. Goggin (Eds.), Locative Media (pp. 1–18). New York: Routledge. Retrieved from http://nadavhochman.net/wp-content/uploads/2014/08/Schwartz_TheSocialMediaLifeofUrbanSpaces.pdf

Serafini, F. W. (2002). DISMANTLING THE FACTORY MODEL OF ASSESSMENT. Reading & Writing Quarterly, 18(1), 67–85. doi:10.1080/105735602753386342

Shankland, S. (2012). How Google is becoming an extension of your mind. Retrieved September 24, 2014, from http://www.cnet.com/au/news/how-google-is-becoming-an-extension-of-your-mind/

Shuger, S. L., Barry, V. W., Sui, X., McClain, A., Hand, G. a, Wilcox, S., … Blair, S. N. (2011). Electronic feedback in a diet- and physical activity-based lifestyle intervention for weight loss: A randomized controlled trial. The International Journal of Behavioral Nutrition and Physical Activity, 8, 41. doi:10.1186/1479-5868-8-41

Simone, L. K., & Kamper, D. G. (2005). Design considerations for a wearable monitor to measure finger posture. Journal of Neuroengineering and Rehabilitation, 2(1), 5. doi:10.1186/1743-0003-2-5

Sivakumar, R. (2014). Google Glass in education. StudyMode.com. Retrieved from http://www.studymode.com/essays/Google-Glass-In-Education-54628722.html

Suarez, A., Ternier, S., Kalz, M., & Specht, M. (2014). GPIM : Google Glassware for inquiry-based learning. Open Learning and Teaching in Educational Communities, 8719, 530–533. doi:10.1007/978-3-319-11200-8_58

Swan, G. (2014). Glassholes at SXSW: The cultural dissonance of technology in a petri dish. Retrieved from http://glassalmanac.com/sxsw-glassholes/2851/sthash.r9pE9JKP.dpuf

Tao, X. (2005). Introduction. In X. Tao (Ed.), Wearable Electronics and Photonics (p. 256). Boca Raton: Woodhead Publishing Ltd. Retrieved from http://books.google.com/books?hl=en&lr=&id=SaKjAgAAQBAJ&pgis=1

Tehrani, K., & Michael, A. (2014). Wearable technology and wearable devices: Everything you need to know. Retrieved October 03, 2014, from http://www.wearabledevices.com/what-is-a-wearable-device/

Tunick, M. (2013). Privacy and punishment. Social Theory and Practice, 39(4), 643–668. doi:10.5840/soctheorpract201339436

Vallejo, M., Recas, J., del Valle, P. G., & Ayala, J. L. (2013). Accurate human tissue characterization for energy-efficient wireless on-body communications. Sensors (Basel, Switzerland), 13(6), 7546–69. doi:10.3390/s130607546

Vassileva, J. (2012). Motivating participation in social computing applications: A user modeling perspective. User Modeling and User-Adapted Interaction, 22(1-2), 177–201. doi:10.1007/s11257-011-9109-5

Walker, S. (2013). Wearable technology – Market assessment. Retrieved from http://www.ihs.com/pdfs/Wearable-Technology-sep-2013.pdf

Wooliey, S., Cross, J., Ro, S., Foster, R., Reynolds, G., Baber, C., … Schwirtz, A. (2004). Count three for wearable computers. IEE Electronics Systems and Software, (March).

Xu, G., Yang, Y., Zhou, Y., & Liu, J. (2013). Wearable thermal energy harvester powered by human foot. Frontiers in Energy, 7(1), 26–38.

Ying, M., Bonifas, A. P., Lu, N., Su, Y., Li, R., Cheng, H., … Rogers, J. A. (2012). Silicon nanomembranes for fingertip electronics. Nanotechnology, 23(34), 344004. doi:10.1088/0957-4484/23/34/344004

Zelek, J. S., Bromley, S., Asmar, D., & Thompson, D. (2003). A haptic glove as a tactile-vision sensory substitution for wayfinding. Journal of Visual Impairment & Blindness, October, 621–632.



What do you think? Share you thoughts below...

%d bloggers like this: