Wearable Technologies: Possibilities in Bringing Innovative Learning Experiences to the Classroom

Wearable Technology-‐ Schmitz Weiss

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Wearable Technologies: Possibilities in Bringing Innovative Learning Experiences to the Classroom Unpublished manuscript January 31, 2015 By: Amy Schmitz Weiss, Ph.D., Associate Professor of Journalism | School of Journalism and Media Studies | San Diego State University Technology plays a crucial role in higher education today.1 Specifically,

ICTs or Information and Communication Technologies 2 (i.e. computers, the

Internet, etc.) are used in many classrooms nowadays as tools to aid in basic

instruction (e.g. lecturing, class activities, homework assignments, etc.).3

Specifically in journalism and mass communication programs, ICTs are also

being used as a tool to aid in teaching news gathering and reporting skills vis-à-

vis digital still cameras, video cameras, audio recorders, laptops, etc., and as a

tool to aid in teaching content production vis-à-vis image processing, video

editing, web publishing, etc., to prepare students for the 21st century newsroom.4

In fact, the 21st century newsroom now reflects a digital environment

where new forms of ICTs (e.g. smartphones, tablets, etc.) are rapidly being

adopted for use in news reporting and gathering tasks in the newsroom.5 Some

individuals have defined this moment in time as one of radical transformation for

the journalism profession and academy where ICTs are changing the idea of how

journalism is taught and practiced.6

For example, the latest ICT being explored with in the newsroom is the


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wearable device.7 Wearable devices can be considered “devices that can be

worn by users, taking the form of an accessory such as jewelry, sunglasses, a

backpack, or even actual items of clothing such as shoes or a jacket”.8 These

kinds of devices can track and hold data about an individual and their daily life

ranging from how many steps they took in a given day to how much sleep they

get at night. Google Glass and various smartwatches are now being used and

experimented with by some reporters for shooting video as well as capturing

audio.9

As it relates to the classroom, wearable technology devices are not

unknown to the educational sector. The 2013 and 2014 Horizon Report identified

that wearable technology will become one of the next big technologies to be

adopted in higher education within the next three to five years.10 Currently,

universities around the world as well as in the United States have started to

experiment with the use of wearable devices for classroom instruction.11 Some

U.S. journalism programs and faculty now have Google Glass and are

experimenting with its use in classes.12 In fact, Robert Hernandez, a journalism

professor at the University of Southern California has a Google Glass class

underway this fall with students in an aim to create glass-centric software for

journalists.13

As journalism programs begin experimenting with wearable devices in the

classroom, some preliminary questions about this ICT must be explored before

its future possible implementation in the classroom. In general, how is the device


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perceived and adopted by the greater population of young adults? How do young

adults perceive its general use in higher education?

These preliminary insights can give journalism educators a better

understanding of how this innovation is perceived by young adults, how it can be

used in general for learning from a student’s perspective, and the likelihood of

the adoption of such technology by young adults.

The insights from this type of study can contribute to previous work by

scholars who have studied ICT adoption from the perspective of the educator14

and/or the effectiveness of an ICT in a specific program or class.15 It must be

noted that few studies have been conducted to understand the factors that make

students adopt an ICT, thus this study can also help to provide more insight to

educators from the learner perspective.16

Using the Diffusion of Innovations theoretical framework, this study

examines how the wearable device is perceived and adopted among young

adults based on an online census survey (n=29,940) that was conducted at a

southwestern university in April 2014.

Theoretical Background

Understanding the factors to ICT adoption can be explored in a variety of

ways. Scholars over the years have used the Theory of Reasoned Action,17 the

Theory of Planned Behavior18 as well as the Theory of Technology Acceptance19

to explore the attitudes, beliefs, and behaviors of ICT adoption at the individual

level. However, the Diffusion of Innovations theory is another approach to

examining this. This theory helps to provide a descriptive model of adoption of an


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ICT by its attributes and characteristics. The attributes and characteristics of an

innovation must also be considered aside from the adopter’s own beliefs or

behaviors. Thus, this study uses the Diffusion of Innovations theory because it

seeks to describe how young adults are adopting the wearable device and what

specific attributes and characteristics are driving such adoption.

According to Rogers, an innovation can be defined as “an idea, practice,

or object that is perceived as new by an individual or other unit of adoption.”20 It

can be defined as technology, which Rogers defines as “ a design for

instrumental action that reduces the uncertainty in the cause-effect relationships

involved in achieving a desired outcome.”21

Wearable devices can be considered a technology and innovation similar

to the desktop computer or smartphone. Wearable technology is a concept that

can be tied back to the early 1990s with the development of Ubiquitous

Computing where it was predicted that computers would weave themselves

seamlessly into daily life.22 The first scientist to experiment with wearable

devices was Steve Mann who created wearable computer experiments in the late

1990s that were somewhat big in size, slow in processing power and limited in

physical use.23 Since then, wearable technologies have become smaller in size,

run faster because of sophisticated microchips and processors, and can

withstand a variety of elements (e.g. rain, sunshine, etc.).24

According to research market firm, SSI, wearable technologies will

become popular in the mainstream market within the next two to three years.25 In

fact, a recent survey conducted by Harris Interactive of 2,577 U.S. adults, about


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46% said they would be interested in owning a wearable technology device.26

Another survey conducted late last year,27 identified that among U.S. adults

interested in purchasing a wearable technology device, 33% said they would be

interested in wearing a smartwatch, 22% said wearing the device in their clothing

and 19% said wearing glasses. Of those who already own a wearable technology

device, eight in 10 said that it has enhanced their life. Specifically, 61% felt more

informed, another 61% said it helped with their personal efficiency, 47% felt more

intelligent, and 37% said it helped with their career development.28

These statistics can be placed within the context of the natural adoption

curve that occurs with a new technology. According to Rogers, innovations will

go through an adoption curve in the market that consists of innovators at 2.5%,

then the early adopters at 13.5%, the early majority that will then represent

another 34% over time, the late majority at 34%, and laggards representing the

end of the adoption curve at 16%.29 Based on current market trends, wearable

technology can be considered at the early stage of Rogers’ adoption curve based

on the number of people who are currently adopting wearable devices.

Furthermore, within the Diffusion of Innovations theoretical framework,

Rogers states there are attributes of innovations that speed or impede diffusion

and how an individual may adopt an innovation. These include relative

advantage, compatibility, complexity, trialability and observability. Relative

advantage is that the innovation is perceived better than the idea that

supersedes it. This can be determined by economic profitability, social prestige

or other benefits. Compatibility is that an innovation is perceived consistent with


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the values, experiences and the needs of the adopter. Complexity is how the

innovation is perceived as relatively difficult to understand or use. Trialability is

that the innovation may be experimented with on a limited basis. Observability is

how the innovation is easily visible to others. 30

These attributes as Rogers has defined them have been adapted and

studied for decades in different contexts and theoretical models in organizational

behavior, social psychology and information science fields.31

As it applies to this study, several scholars in education and educational

technology32 have studied how students have adopted ICTs (e.g. wikis,

smartphones, blogs, social networking sites, e-learning systems, etc.) and their

attributes in the classroom setting. Several of these studies have shown how the

attributes of observability, compatibility, relative advantage and trialability of the

ICT can play a factor into a student’s likelihood to adopt and use a technology.

For example, observabilty can play a factor for students in how they

decide to adopt a technology based on what is happening in the social circle

around them. For example, Lai, Weng and Lei found in their research that

students’ adoption of technology (e.g.. social networking sites, video games,

wikis, blogs, smartphones, etc.) for learning purposes was connected to how

much their peers and teachers supported the technology in addition to their own

attitudes toward technology. Perceived usefulness and general ICT literacy skills

influenced adoption but had less predictive power.33

Another study identified that students will perceive and in turn, adopt new

media production technologies (e.g. web-ready media via audio, photo and


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video) for classroom and learning use based on the influence of significant others

(e.g. other students and instructors) who adopt new media production

technologies. Furthermore, the study identified that students acknowledged that

technology was important to their future careers and thus, were likely to adopt

new media production technologies as a result of seeing the value of such skills

for professional competency.34

Compatibility can also play a factor for technology adoption. Based on a

survey of students’ perceptions of an e-learning system and their intentions to

use the technology, findings showed that students accepted and adopted a

technology when it matched their learning expectations and when it was

compatible to their learning lifestyle.35

Relative advantage and understanding the uses and benefits of

technology can also play a factor into a student’s adoption of ICTs. A group of

researchers surveyed and interviewed university students from two different

disciplines (i.e. engineering and social work) and found that students adopted

general technologies such as websites, course learning management systems,

smartphone apps for learning purposes but didn’t adopt technologies such as

wikis, YouTube, blogs or social media as readily. To examine this difference, the

researchers identified in interviews with the students that they had a limited

understanding of knowing the potential benefits of using such technologies for

classroom use and thus only adopted those technologies they saw a direct

benefit and use from. Several students also stated that they looked to their peers

or instructors for guidance in technology adoption. The researchers posited that


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students might be using certain technologies in a formal learning context based

on what their instructors are doing with such technologies in the classroom.36

It can be concluded that the attributes of compatibility, relative advantage,

and observability as well as attitudes toward such ICTs (e.g. smartphone apps,

wikis, social networks, websites, etc.) can play a factor in technology adoption

among students as it relates to the classroom and learning experience.37

Research Questions

Research has shown that young adults are adopting ICTs for classroom

use and they are open to adopting new ICTs when their peers and teachers

adopt or perceive its usefulness as well as its compatibility in context to the

learning environment.38 Based on the past ICT scholarship, how are young adults

adopting the latest ICT - the wearable device? What attributes drive adoption or

not? How is this technology perceived for use in the educational setting? These

issues are addressed in the following research questions.

RQ1: What innovation attributes (e.g. relative advantage, compatibility,

complexity, trialability and observability) speed or impede diffusion of wearable

technology by young adults?

RQ2: What innovation attributes (e.g. relative advantage, compatibility,

complexity, trialability and observability) speed or impede diffusion of wearable

technology by young adults for educational purposes?

RQ3: What ways could young adults perceive wearable technology

devices being used in educational settings?


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Method

An online census survey39 was administered to college students 18 years

and older attending a southwestern university in April 2014 (n=29,940).

Responses were obtained from 454 young adults.40 Respondents were recruited

through an email invitation and sent by the campus registrar office to the entire

study body at the university.41 This study focuses on young adults because they

are the fastest adoption group of wearable technology devices than any other

age segment.42 The university population and not just journalism and mass

communication students were surveyed for this study in order to provide a

comprehensive and full picture about how young adults are adopting wearable

technologies. The insights garnered from this census survey can provide

journalism educators with preliminary information of the possible uses of this ICT

in general in the classroom.

Measurement. Respondents were asked about their device ownership

(e.g. smartphones, game consoles, laptops, etc.), their Internet usage,

perceptions and uses of wearable technologies. Questions in the survey were

based on existing survey instruments on innovation adoption of information

communication technologies (ICTs) and wearable technologies.43 Existing scales

from Moore and Benbasat44 were used for this study to measure Rogers’

innovation attributes of relative advantage, compatibility, complexity/ease of use

and trialability to determine perceptions for wearable technology adoption (see

Table 1).45 All scales had reliability above .80, which is considered very good

according to DeVellis.46 To make this study as replicable as possible, the


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observability scale was excluded from this study as Moore and Benbasat had

construct issues that resulted in low reliability and validity.47 The researcher used

another method by which to measure observability among the respondents.48

Table 1 Innovation Characteristics Scale Items by Moore and Benbasat Relative Advantage

• Using a wearable tech device would enable me to accomplish tasks more quickly. • Using a wearable tech device would improve the quality of the work I do. • The disadvantages of using a wearable tech device would far outweigh the advantages. * • Using a wearable tech device would improve my performance at school. • Using a wearable tech device would give me greater control over my work at school. • Using a wearable tech device would increase my productivity.

Compatibility

• Using a wearable tech device would be compatible with all aspects of my life. • I think that using a wearable tech device would fit well with the way I like to work, play

and live. • Using a wearable tech device would fit into my lifestyle.

Complexity/Ease of Use

• I believe that wearable tech device would be cumbersome to use. • It would be easy for me to remember how to perform tasks using a wearable tech device. • Using a wearable tech device would require a lot of mental effort. • Using a wearable tech device would be frustrating. • Using a wearable tech device would be clear and understandable. • I believe that it would be easy to get a wearable tech device to do what I want it to do. • Overall, I believe that a wearable tech device would be easy to use. • Learning to operate a wearable tech device would be easy for me.

Trialability

• I’ve had a great deal of opportunity to try various wearable tech devices. • I know where I can go to satisfactorily try out various uses of wearable tech devices. • A wearable tech device was available to me to adequately test run its use. • I was permitted to use a wearable tech device on a trial basis long enough to see what it

could do.

Note. *Item removed from scale due to low reliability. Observability scale was not included due to construct issues from Moore and Benbasat. Observability was measured using another method. See article for details. The relative advantage scale consisted of 5 items with a reliability of α.=91. The compatibility scale consisted of 3 items with a reliability of α=.90. The complexity/ease of use scale consisted of 8 items with a reliability of α=.88. The triability scale consisted of 4 items with a reliability of α=.87.


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Sample Demographics. About 63.7 of respondents were female and 36.3%

were male. The majority of the students were undergraduates (74.7%), 24.5%

were graduate students and less than one percent other.

Over 80% of respondents were aware of wearable technology devices.

However, only 16% owned a wearable technology device in comparison to 84%

that didn’t. Of those who owned a wearable device, frequency of its use varied.

Thirty-four percent used the device several times a day, 20.6% used it 3-5 days a

week, 14.7% said they used it less often, 10.3% said they used it once a day,

and 7.4% said every few weeks. The majority stated they owned a health or

fitness monitor (N=64), followed by smart glasses (N=2), smart watches (N=2),

mobile life tracker (N=1) and a people tracker device (N=1).

Results

In order to answer RQ1, students were first segmented into adopters or

non-adopters of wearable technology devices based on their ownership of such

devices and ANOVA analyses were run according to aforementioned innovation

attributes scales.

Table 2 Innovation Attributes by Adopter Category _____________________________________________________________________________

Adopter Non-Adopter

Innovation Attributes Mean SD Mean SD F-ratio df Sig. _____________________________________________________________________________ Relative Advantage 2.55 .85 3.06 .77 23.480 1, 417 .001 Compatibility 2.29 .79 3.12 .92 46.610 1, 416 .001 Complexity/Ease of Use 2.17 .59 2.72 .65 38.878 1, 392 .001 Trialability 3.32 1.12 4.07 .83 38.928 1, 402 .001 Note: Scale from 1-5 strongly agree to strongly disagree


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As shown in Table 2, of the respondents who have adopted a wearable

technology device, agreed that the innovation attributes of relative advantage

(M=2.55, SD=.85) F(1, 417) = 23.480, p = .001, compatibility (M=2.29, SD=.79)

F(1, 416) = 46.610, p = .001, ease of use/complexity (M=2.17, SD=.59) F(1, 392)

= 38.878, p = .001 and trialability (M=3.32, SD =1.12) F(1, 402) = 38,928, p =

.001 significantly showed to aid their adoption and use of such device in

comparison to non-adopters. For the innovation attribute of observability, 92% of

adopters were knowledgeable and exposed to wearable technology devices in

comparison to 86% of non-adopters.

Table 3 Reasons for Adoption of Wearable Technology Devices ________________________________________________ Reason % _________________________________________________ Track physical activity 53.6 Usefulness 11.5 Trying out new gadgets 10.1 To be healthy 10.1 Other 10.1 For fun 4.3 (N) (69) _________________________________________________

A majority of respondents who did adopt a wearable technology device

stated their reason was to use it to track physical activity (53.6%), followed by its

usefulness (11.5%), trying out new gadgets (10.1%), to be healthy (10.1%) and

for fun (4.3%) (see Table 3).


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Table 4 Reasons for Non-Adoption of Wearable Technology Devices _____________________________________________________ Reason % _____________________________________________________ Don’t have a need for item 45.9 Don’t know enough about it 21.3 Cost is too high 20.5 Have too many gadgets already 5.5 It’s a fad 4.2 Don’t like the way it looks 2.4 Don’t like the feel/weight of the product .3 (N) (381) ____________________________________________________

Of those who didn’t adopt a wearable technology device, 45.9% said they

had no need for the item, followed by 21.3% said they didn’t know enough about

it, 20.5% cited cost, 5.5% said they have too many gadgets already, 4.2%

believe it’s a fad, 2.4% didn’t like the way it looks, and .3% didn’t like the

feel/weight of the product (see Table 4). However, 29.6% of respondents said

they would consider purchasing a wearable device in the future in comparison to

23.8% that wouldn’t. And 46.6% said they were not sure if they would make the

purchase in the future or not.

In order to answer RQ2, respondents were recoded into interested and

non-interested students based on their response whether they would use

wearable technologies in the classroom. ANOVA analyses were then run

according to Rogers’ innovation attributes.


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Table 5 Adoption/Rejection of Wearable Technology Devices for Educational Purposes by Innovation Attributes _____________________________________________________________________________ Interested Non-Interested Students Students Innovation Attributes Mean SD Mean SD F-ratio df Sig. _____________________________________________________________________________ Relative Advantage 2.65 .68 3.46 .72 139.355 1, 418 .001 Compatibility 2.65 .84 3.48 .88 95.927 1, 417 .001 Complexity/Ease of Use 2.46 .63 2.84 .67 33.323 1, 392 .001 Trialability 3.84 .99 4.10 .80 7.893 1, 401 .01 Note: Scale from 1-5 strongly agree to strongly disagree

As shown in Table 5, interested students agreed that the innovation

characteristics of relative advantage (M=2.65, SD=.68) F(1, 418) = 139.355, p =

.001, compatibility (M=2.65, SD=.84) F(1, 417) = 95.927, p = .001,

complexity/ease of use (M=2.46, SD=.63) F(1, 392) = 33.323, p = .001 and

triability (M=3.84, SD=.99) F(1, 401) = 7.893, p = .01, do play a role in their use

and adoption of such device for educational purposes in comparison to non-

interested students. For the innovation attribute of observability, 88% of

interested students were knowledgeable and exposed to wearable technology

devices in comparison to 86% of non-interested students.

RQ3 examined how young adults perceive wearable technology use in

higher education. A majority of the respondents (59.6%) said they would be

interested to use one in their classes in comparison to 40.4% who said they

wouldn’t. Respondents had varied answers of the ways wearable technology

devices could be used in and outside the classroom.


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Table 6 Wearable Technology Device Use in Educational Settings _______________________________________________________ Activity % _______________________________________________________ Recording a class lecture 74.4 (n=338) Note-taking 55.3 (n=251) Taking photos during class related to topic 52.2 (n=237) Gathering information for class topic 51.3 (n=233) Taking photos related to outside class activities 37.9 (n=172) In-class assignments 37.2 (n=169) Finding a relevant reading to the class topic 36.8 (n=167) In-group activities 33.5 (n=152) For field trips or outside class activities 31.5 (n=143) Asking instructor questions 30 (n=136) ________________________________________________________ Note: Respondents had the option to select more than one option.

As shown in Table 6, 74% percent said the wearable device could be used

to record a class lecture, 55% said for note-taking, 52.2% said for taking photos

related to class topic, 51% said using the device for gathering class information,

37.9% said for taking photos outside the class, 37.2% said for in-class

assignments, 33.5% said using the device for in-group activities, 31.5% said for

field trips and 30% said for asking the instructor questions.

Conclusion/Discussion

This study shows that the adoption of wearable technologies by young

adults remains in a very early stage. Considering Rogers’49 adoption curve, we

are still in the innovator to early adopter stage. Furthermore, there is a greater

awareness of wearable technology devices, but less people who actually own

one. Both of these findings reflect a larger trend nationally and globally of this

kind of device and its adoption in the marketplace.50


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Of those who owned the device, many had several reasons for adopting it,

but the majority stated physical activity tracking as the main reason. For non-

adopters, the lack of necessity, lack of knowledge of the device, and cost were

the main reasons for not owning one. However, not all respondents were against

future adoption of such device since almost 30% were going to buy one in the

future and another 46% were uncertain.

As for classroom use, the majority of respondents saw the multimedia

capabilities (e.g. video, photo) as well as information gathering and storage

features of the wearable technology device as possible aids in their learning of

class materials.

Adoption of the wearable technology device among the young adults was

statistically significant to the innovation attributes of relative advantage,

compatibility, complexity/ease of use, trialbility and observability, reflecting

Rogers’51 diffusion of innovations adoption pattern.

Future research in this area could explore how such devices are being

used by young adults by conducting focus groups, in-depth interviews or having

self-tracking diaries charting the adoption process.

The findings from this study only reflect the perceptions of young adults

from one southwestern university and are not generalizable to the larger

population, but the study does provide insight into the early adoption of the

wearable technology device into the market. It also shows how young adults who

own these devices are using them for information gathering and utility purposes

albeit for physical fitness tracking. However, these purposes can be extended


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into the classroom setting. Thus, this study provides the start to an important

conversation in the academy about how innovations like the wearable technology

device can be explored for use in higher education.

Becker, Vlad and Simpson state that journalism and mass communication

programs need to adapt and innovate in order to maintain and grow enrollments

in the years and decades ahead if they want to be sustainable.52

Journalism and mass communication programs that adopt innovations

such as wearable technology devices before widespread use in society and the

journalism profession may help to prepare journalism students for the unknown

when they enter the workforce in the future.53

Also, as ICT adoption research has identified, educators who adopt such

innovations can have an impact on students’ general ICT adoption and influence

their students’ behaviors and perceptions of the adoption of such ICTs by how

they present and use such ICTs in the classroom.54 Journalism educators can

play a crucial role in this process by how they adopt an ICT like the wearable

device in the classroom and how their students react to such an ICT and

consider its use for the classroom but also the future newsroom they may work in.

It is through new ways of exploration and experimentation with such

innovations that higher education and for that matter, journalism and mass

communication programs can keep pace with the market, remain sustainable for

the future, and help in the creation of a new market of professionals who have

the latest skills to do their job, and are on the cutting-edge of innovation in order

to explore the possibilities of how work can be done in the 21st century.


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Notes 1 Lee Bernard Becker, Tudor Vlad, and Holly Anne Simpson, “2013 Annual Survey of Journalism Mass

Communication Enrollments: Enrollments Decline for Third Consecutive Year,” Journalism & Mass Communication Educator 69 (4 2014): 349-365, first published on October 27, 2014, doi:10.1177/1077695814555432; Maria Marron, “Transitions and Challenges,” Journalism & Mass Communication Educator 68 (1 2013): 3–5, doi: 10.1177/1077695813475789 ; John Pavlik, “A Vision for Transformative Leadership: Rethinking Journalism and Mass Communication Education for the Twenty-First Century,” Journalism & Mass Communication Educator 68(3 2008): 211–22 doi: 10.1177/1077695813499561; Becker, Vlad and Simpson, “2012 Annual Survey of Journalism and Mass Communication Enrollments: Enrollments Decline for Second Year in a Row.”

2 ICT is a technology that provides information through communication technologies such as computers, the Internet, cell phones and video conferencing. 3 Laurence Johnson, Samantha Adams Becker, Victoria Estrada, and Martín, “Technology Outlook for STEM+ Education 2013-2018: An NMC Horizon Project Sector Analysis,” The New Media Consortium, 2013, http://www.nmc.org/publications/2013-technology-outlook-stem (accessed January 30, 2014), 12; Laurence Johnson, Samantha Adams Becker, Victoria Estrada, and Freeman, “NMC Horizon Report: 2014 Higher Education Edition,” The New Media Consortium, 2014, http://redarchive.nmc.org/publications/2014-horizon-report-higher-ed (accessed November 8, 2014). 4 Andreas Veglis and Andreas Pomportsis, “Journalists in the Age of ICTs: Work Demands and Educational Needs,” Journalism & Mass Communication Educator 69(1 2014): 70-71; Carol Smith, Haeryon Kim, and James Bernstein, “Computer-Mediated Communication and Strategies for Teaching,” Journalism & Mass Communication Educator, 48(1 1993): 80-83; William Smith, “Computer-Mediated Communication: An Experimental Study,” Journalism Educator, 48(4 1994): 27-33; John Russial, "Mixed Messages About Pagination and Other Skills," Newspaper Research Journal 16 (winter 1995): 60-70; Ann Auman, Frank Fee, and John Russial, "Noble Work, but Undervalued: The Status and Value of Copy Editing in Journalism Schools," Journalism & Mass Communication Educator 57 (summer 2002): 139; Lewis A. Friedland and Sheila Webb, "Incorporating Online Publishing into the Curriculum," Journalism & Mass Communication Educator (autumn 1996): 54-65; Jennifer Wood Adams and Meussa L. Voynich, “The Use of Design Technology in U.S. Journalism Programs,” Journalism & Mass Communication Educator (spring 2008):42-54; Cindy Royal, "Teaching Web Design in Journalism and Mass Communications Programs: Integration, Judgment and Perspective," Journalism & Mass Communication Educator 59 (winter 2005): 400-14; Michael Huntsberger and Alan Stavitsky, “The New "Podagogy": Incorporating Podcasting into Journalism Education,” Journalism & Mass Communication Educator (winter 2007): 397-410. 5 Du and Thornburg, “The Gap between Online Journalism Education and Practice: The Twin Surveys.” 6 Pavlik, “A Vision for Transformative Leadership: Rethinking Journalism and Mass Communication

Education for the Twenty-First Century.” Jean Folkerts, “History of Journalism Education,” Journalism & Communication Monographs 16 (4 2014): 227-299, first published on July 22, 2014 doi:10.1177/1522637914541379.

7 Stuart Dredge, “How Vice's Tim Pool used Google Glass to cover Istanbul protests,” The Guardian July 30, 2013, http://www.theguardian.com/technology/2013/jul/30/google-glass-istanbul-protests-vice (accessed November 1, 2014); Alastair Reed, “Could Google Glass change the face of journalism?: Journalism.co.uk, January 15, 2014

https://www.journalism.co.uk/news/could-google-glass-change-the-face-of-journalism-/s2/a555596/ (accessed November 1, 2014).

8 Laurence Johnson, Samantha Adams Becker, Victoria Estrada, and Martín, “Technology Outlook for STEM+ Education 2013-2018: An NMC Horizon Project Sector Analysis,” The New Media Consortium, 2013, http://www.nmc.org/publications/2013-technology-outlook-stem (accessed January 30, 2014), 12.

9 Stuart Dredge, “How Vice's Tim Pool used Google Glass to cover Istanbul protests,” The Guardian July 30, 2013, http://www.theguardian.com/technology/2013/jul/30/google-glass-istanbul-protests-vice (accessed November 1, 2014); Alastair Reed, “Could Google Glass change the face of journalism?: Journalism.co.uk, January 15, 2014, https://www.journalism.co.uk/news/could-google-glass-change-the-face-of-journalism-/s2/a555596/ (accessed November 1, 2014); Alastair Reed, “Smartwatches and the digital future of news,” Journalism.co.uk, January 20, 2014, https://www.journalism.co.uk/news/smartwatches-and-the-digital-future-of-news/s2/a555625/ (accessed November 1, 2014).

10 Johnson, Becker, Estrada, and Martín, “Technology Outlook for STEM+ Education 2013-2018: An NMC Horizon Project Sector Analysis.” ; http://cdn.nmc.org/media/2014-nmc-horizon-report-he-EN-SC.pdf

11 Johnson, Becker, Estrada, and Martín, “Technology Outlook for STEM+ Education 2013-2018: An NMC


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Horizon Project Sector Analysis.” http://cdn.nmc.org/media/2014-nmc-horizon-report-he-EN-SC.pdf; Laurence Johnson, Samantha Adams Becker, Victoria Estrada, and Freeman, “NMC Horizon Report: 2014 Higher Education Edition,” The New Media Consortium, 2014, http://redarchive.nmc.org/publications/2014-horizon-report-higher-ed (accessed November 8, 2014). 12 Adam Offitzer, “How Journalism Professors Are Bringing Google Glass into the Classroom,” American Journalism Review, April 10, 2014, http://ajr.org/2014/04/10/google-glass-journalism-new-perspective/ (accessed May 30, 2014). Some journalism programs include: DePaul University, Michigan State University, Texas Christian University, Lehigh University, San Diego State University, among others. 13 Chris Gayomali, “Meet the USC Journalism Professor Leading Course on Google Glass,” Fast Company, April 3, 2014, http://www.fastcompany.com/3028476/whos-next/meet-the-usc-journalism- professor-leading-a-course-on-google-glass (accessed May 30, 2014) 14 Margaret DeFleur and Lucinda Davenport, “Innovation Lag: Computer-Assisted

Classrooms Vs. Newsrooms,” Journalism Educator 48 (2 1993): 26-36; Kevin Lee and Charles Fleming, “Problems of Introducing Courses in Computer-Assisted Reporting,” Journalism Educator, 50 (3 1995): 23-34; Lucinda Davenport, Fred Fico, and Margaret DeFleur, “Computer-assisted Reporting in Classrooms: A Decade of Diffusion and a Comparison to Newsrooms,” Journalism & Mass Communication Educator, 57 (1 2002) 2-22; Kimberly Lawless and James Pellegrino, “Professional Development in Integrating Technology into Teaching and Learning: Knowns, Unknowns, and Ways to Pursue Better Questions and Answers,” Review of Education Research 77, 4 (2007): 575–614; Willam Sugar, Frank Crawley, and Bethann Fine, “Examining Teachers’ Decisions to Adopt New Technology,” Educational Technology & Society 7, 4 (2004): 201–13; Timothy Teo, “A Path Analysis of Pre-Service Teachers’ Attitudes to Computer Use: Applying and Extending the Technology Acceptance Model in an Educational Context,” Interactive Learning Environments 18, 1 (2010): 65–79; Carol Smith, Haeryon Kim, and James Bernstein, “Computer-Mediated Communication and Strategies for Teaching,” Journalism & Mass Communication Educator, 48(1 1993): 80-83.

15 Lee and Fleming, “Problems of Introducing Courses in Computer-Assisted Reporting.”; Patrick Sutherland, “Diffusion of Courses with World Wide Web Features: Perceptions of Journalism and Mass Communication Program Administrators,” Journalism & Mass Communication Educator, 58 (1 2003):384-395; Shelton Gunaratne and Byung Lee, “Integration of Internet Resources into Curriculum and Instruction,” Journalism & Mass Communication Educator 51 (2 1996): 25-35; DeFleur and Lucinda Davenport, “Innovation Lag: Computer-Assisted Classrooms Vs. Newsrooms.”; Davenport, Fico, and DeFleur, “Computer-assisted Reporting in Classrooms: A Decade of Diffusion and a Comparison to Newsrooms;” William Smith, “Computer-Mediated Communication: An Experimental Study,” Journalism Educator, 48(4 1994): 27-33; John Russial, "Mixed Messages About Pagination and Other Skills," Newspaper Research Journal 16 (winter 1995): 60-70; Ann Auman, Frank Fee, and John Russial, "Noble Work, but Undervalued: The Status and Value of Copy Editing in Journalism Schools," Journalism & Mass Communication Educator 57 (summer 2002): 139; Lewis A. Friedland and Sheila Webb, "Incorporating Online Publishing into the Curriculum," Journalism & Mass Communication Educator (autumn 1996): 54-65; Jennifer Wood Adams and Meussa L. Voynich, “The Use of Design Technology in U.S. Journalism Programs,” Journalism & Mass Communication Educator (spring 2008):42-54; Cindy Royal, "Teaching Web Design in Journalism and Mass Communications Programs: Integration, Judgment and Perspective," Journalism & Mass Communication Educator 59 (winter 2005): 400-14; Michael Huntsberger and Alan Stavitsky, “The New "Podagogy": Incorporating Podcasting into Journalism Education,” Journalism & Mass Communication Educator (winter 2007): 397-410.

16 Toby Hopp, “Subjective Norms as a Driver of Mass Communication Students’ Intentions to Adopt New Media Production Technologies,” Journalism & Mass Communication Educator 68 (4 2013): 348– 364, doi: 10.1177/1077695813506993

17 Martin Fishbein and Icek Ajzen, Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. (Reading, MA: Addison-Wesley, 1975).

18 Icek Ajzen, “From Intentions to Actions: A Theory of Planned Behavior,” in J. Kuhl and J. Beckmann (eds.), Action-control: From Cognition to Behavior. (NY: Springer, 1985), 11-39; Kiernan Mathieson, “Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior,” Information Systems Research 2 (1 1991): 173-191; Shirley Taylor, S., and Peter Todd,“Understanding Information Technology Usage - a Test of Competing Models,” Information Systems Research 6 (2 1995): 144-176; Shirley Taylor, S., and Peter Todd, “Assessing IT Usage: The Role of Prior Experience,” MIS Quarterly 19 (4 1995): 561-570.

19 Fred D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly 13 (3 1989): 319-340; Fred D. Davis, Richard Bagozzi, and Paul Warshaw, “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models,” Management Science 35 (8 1989):982-1003.

20 Everett Rogers, Diffusion of Innovations, Fourth Edition. (NY: The Free Press, 1995),11.


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21 Rogers, Diffusion of Innovations, 12. 22 Mark Weiser, “The Computer for the 21st Century.” Scientific American Special Issue on Communications, Computers, and Networks, (1991): 78-89. 23 Steve Mann, “Wearable Computing as Means for Personal Empowerment,” Keynote address presented at the First International Conference on Wearable Computing, Fairfax, Virginia, May 12–13, 1998, http://wearcam.org/icwckeynote.html (accessed January 30, 2014); Steve Mann and Hall Niedzviecki, Hall, Cyborg: Digital Destiny and Human Possibility in the Age of the Wearable Computer (Toronto: Doubleday, 2001). 24 Google launched its Google Glass prototype to the public in 2013, a pair of glasses that the user could wear to view the Internet, get directions, take video and photos all from a small lens near their eye. Although it remains in beta, latest figures place the number of Google Glass owners somewhere around 10,000. 25 Jason Kannon, “Wearable Technology Grows in Popularity,” SSI, September 18, 2013,

http://www.surveysampling.com/~/~/link.aspx?_id=D6BCAB8C95DC4E5AA23B9ADCD0099681&_z=z (accessed January 30, 2014).

26 Larry Shannon-Missal, “Are Americans Prepared to Sport Wearable Tech?” Harris Interactive, November 6, 2013, http://www.harrisinteractive.com/NewsRoom/HarrisPolls/tabid/447/ctl/ReadCustom%20Default/mid/150 8/ArticleId/1312/Default.aspx (accessed January 30, 2014). 27 Will Oremus, “Survey: People Don't Really Want to Wear Computers on Their Face,” Slate.com, November 25, 2013, http://www.slate.com/blogs/future_tense/2013/11/25/citrix_wearable_technology_survey_adults_prefer_ smartwatches_to_google_glass.html (accessed January 30, 2014). 28 Joe McKendrick, “Google Glass is Only the Beginning of Cloud-Powered Wearable Technology, Survey Suggests,” Forbes.com, June 5, 2013, http://www.forbes.com/sites/joemckendrick/2013/06/05/google- glass-is-only-the-beginning-of-cloud-powered-wearable-technology-survey-suggests/ (accessed January 30, 2014). 29 Rogers, Diffusion of Innovations, 262 30 Rogers, Diffusion of Innovations, 212-251. 31 Icek Ajzen and Martin Fishbein, Understanding Attitudes and Predicting Social Behavior (Englewood

Cliffs, NJ: Prentice-Hall, 1980); Martin Fishbein and Icek Ajzen, Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. (Reading, MA: Addison-Wesley, 1975); Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology.”; Davis, Bagozzi, and Warshaw, “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models.”; Viswanath Venkatesh, Michael Morris, Gordon Davis, and Fred Davis, “User Acceptance of Information Technology: Toward a Unified View,” MIS Quarterly 27 (3 2003): 425-478; Ritu Agarwal and Jayesh Prasad, “A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology,” Information Systems Research 9 (2 1998): 204-215; Gary Moore and Izak Benbasat, “Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation,” Information Systems Research, 2(3 1991): 192-222; Viswanath Venkatesh, Fred Davis and Michael Morris, “Dead Or Alive? The Development, Trajectory And Future Of Technology Adoption Research,” Journal for the Association of Information Systems, 8 (4 2007): 267-286.

32 Jian-Lian Chen, “The effects of education compatibility and technological expectancy on e-learning acceptance,” Computers & Education, 57 (2011): 1501–1511; Anoush Margaryan, Allison Littlejohn, and Gabrielle Vojt, “Are digital natives a myth or reality? University students’ use of digital technologies,” Computers & Education, 56 (2 2011): 429–440; Wilma Clark, Kit Logan, Rosemary Luckin, Adrian Mee, and Martin Oliver, “Beyond Web 2.0: mapping the technology landscapes of young learners,” Journal of Computer Assisted Learning, 25 (2009): 56–69l Chun Lai, Qiu Wang, and Jing Lei, “What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong,” Computers & Education, 59 (2012): 569-579; Hopp, “Subjective Norms as a Driver of Mass Communication Students’ Intentions to Adopt New Media Production Technologies.”

33 Lai, Wang, and Lei, “What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong.”

34 Hopp, “Subjective Norms as a Driver of Mass Communication Students’ Intentions to Adopt New Media Production Technologies.” 35 Chen, “The effects of education compatibility and technological expectancy on e-learning acceptance.” 36 Margaryan, Littlejohn, and Vojt, “Are digital natives a myth or reality? University students’ use of digital technologies.” 37 Chen, “The effects of education compatibility and technological expectancy on e-learning acceptance.”; Margaryan, Littlejohn, and Vojt, “Are digital natives a myth or reality? University students’ use of digital


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technologies.”; Clark, Logan, Luckin, Mee, and Oliver, “Beyond Web 2.0: mapping the technology landscapes of young learners.” 38 Chen, “The effects of education compatibility and technological expectancy on e-learning acceptance.”;

Margaryan, Littlejohn, and Vojt, “Are digital natives a myth or reality? University students’ use of digital technologies.”; Clark, Logan, Luckin, Mee, and Oliver, “Beyond Web 2.0: mapping the technology landscapes of young learners.”; Lai, Wang, and Lei, “What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong.”; Hopp, “Subjective Norms as a Driver of Mass Communication Students’ Intentions to Adopt New Media Production Technologies.”

39 Census survey means every student in the campus population was surveyed. The accuracy of the intended group and the reliability is higher than a sampling survey which only covers a cross section of the intended group the survey is intended for.

40 Despite this response rate from the census, the respondents are representative of the makeup of the campus population by gender, enrollment level and ethnicity. Campus enrollment was 84% undergraduate and 15% graduate. The ethnicity of the campus population was 36.7% were white, 29.5% were Hispanic, 13.3% were Asian/Pacific Islander, 5.4% were other, 3.7% were African American and .2% were Native American/American Indian. The campus population was 56% females and 44% males. Sixty-three percent of respondents were female and 36% were male. As for survey respondents, 74.7% were undergraduates and 24.5% were graduates. The ethnicity of the survey respondents was 41.7% were white, 24.5% were Hispanic, 18.1% were Asian Pacific Islander, 4.2% were other, 3.9% were African American and 1% were Native American/American Indian.

41 To aid participation in the survey, the researcher provided an incentive of a random drawing to win Apple or Android Gift Cards for those who completed the survey. 42 Johnson, Becker, Estrada, and Martín, “Technology Outlook for STEM+ Education 2013-2018: An NMC Horizon Project Sector Analysis; Shannon-Missal, “Are Americans Prepared to Sport Wearable Tech?” 43 Gary Moore and Izak Benbasat, “Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation,” Information Systems Research, 2(3 1991): 192-222; Shannon-Missal, “Are Americans Prepared to Sport Wearable Tech?”; Kannon, “Wearable Technology Grows in Popularity.” 44 Moore and Benbasat, “Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation.” 45 Rogers, Diffusion of Innovations. 46 Robert F. DeVellis, Scale Development, Theory and Applications, 2nd edition (Thousand Oaks, CA: Sage Publications, 2003). 47 Moore and Benbasat, “Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation,” 203. 48 Rogers states that an innovation can be classified as computer hardware or equipment, and that it will

have higher observability and likelihood for adoption than computer software that is not as apparent to observe or be exposed to. Wearable technology devices are physical objects such as glasses, smartwatches, etc. that are visible in a social system. Thus, the visibility of such an innovation can be answered by exposure to and knowledge/awareness of such innovation.

49 Rogers, Diffusion of Innovations. 50 Kannon, “Wearable Technology Grows in Popularity,”; Shannon-Missal, “Are Americans Prepared to

Sport Wearable Tech?”; Oremus, “Survey: People Don't Really Want to Wear Computers on Their Face.”

51 Rogers, Diffusion of Innovations. 52 Becker, Vlad and Simpson, “2012 Annual Survey of Journalism and Mass Communication Enrollments: Enrollments Decline for Second Year in a Row.” 53 Jane Singer, David Craig, Chris Allen, Virginia Whitehouse, Anelia Dimitrova and Keith Sanders, “Attitudes of Professors and Students About New Media Technology,” Journalism & Mass Communication Educator (summer 1996): 44. 54 Chen, “The effects of education compatibility and technological expectancy on e-learning acceptance.”;

Margaryan, Littlejohn, and Vojt, “Are digital natives a myth or reality? University students’ use of digital technologies.”; Clark, Logan, Luckin, Mee, and Oliver, “Beyond Web 2.0: mapping the technology landscapes of young learners.”; Lai, Wang, and Lei, “What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong.”; Hopp, “Subjective Norms as a Driver of Mass Communication Students’ Intentions to Adopt New Media Production Technologies.”

Wearable Technologies: Possibilities in Bringing Innovative Learning Experiences to the Classroom

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