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ECAR Study of
Undergraduate
Students and
Information
Technology, 2014
EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
EDUCAUSE is a nonprot association and the
foremost community of IT leaders and professionals
committed to advancing higher education.
EDUCAUSE programs and services are focused
on analysis, advocacy, community building,
professional development, and knowledge creation
because IT plays a transformative role in higher
education. EDUCAUSE supports those who lead,
manage, and use information technology through a
comprehensive range of resources and activities. For
more information, visit educause.edu.
Contents
Foreword 3
Executive Summary 4
Introduction 6
Findings 8
Conclusions 34
Recommendations 35
Methodology 38
Acknowledgments 42
Appendix A: Participating Institutions 43
Appendix B: Validity and Reliability of Semantic Dierential
Constructs 46
Authors
Eden Dahlstrom, EDUCAUSE Center for Analysis and Research
Jacqueline Bichsel, EDUCAUSE Center for Analysis and Research
Citation
Dahlstrom, Eden, and Jacqueline Bichsel. ECAR Study of Undergraduate
Students and Information Technology, 2014. Research report. Louisville,
CO: ECAR, October 2014. Available from http://www.educause.edu/ecar.
©2014 EDUCAUSE. CC by-nc-nd.
ECAR Study of Undergraduate Students and
Information Technology, 2014 is published with
generous support from the report’s two sponsors:
GOLD Partner
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
Foreword
In this tenth anniversary of the ECAR student study, we nd a thriving research
program that is more worthwhile than ever. From 12 participating institutions to 213.
From 4,123 students to 75,306. And from a time when students “expressed only a
moderate preference for IT use in the classroom” to a time when “technology is omni-
present in the lives of students.” Today students overwhelmingly prefer and have expe-
rienced courses with at least some online components. Oh, how things have changed.
e challenge now for institutions is not whether or how much technology to use
but how to use technology in ways that are consonant with institutional culture and
identity to help students succeed in individual courses, in their college experience,
and in their educational objectives. e challenge is to meet students’ expectations
of functionality and performance and to support their preexisting technology envi-
ronments while applying the right technologies to deepen their educational engage-
ment. In some cases, institutions need to consider investing in or expanding their
use of such student-preferred technologies as early-alert systems and other learning
analytics, gaming and simulations, mobile devices, and more aordable alternatives
to traditional textbooks such as e-textbooks or open content. In other cases, faculty
and IT organizations need to consider how to better leverage the features in existing
applications such as the learning management system.
ese decisions will dier for each institution. One of the most valuable uses of the
ECAR student study is not to mark the passage of time but instead to help institutions
prepare for the future. Many higher education leaders are looking for guidance in
how and whether to invest in online learning and in technologies to support student
success. is study can help leaders frame and answer key questions about what their
students need and hope for from technology, which can help immensely as an institu-
tion develops its strategic objectives for educational technology.
e stakes are high. MOOCs are only the most publicized of an expanding and
evolving marketplace of alternatives to traditional higher education. At a time when
that tradition is growing increasingly unaordable, less expensive options look
increasingly attractive to students from all walks of life. Technology is, paradoxically,
both a potential solution—to make higher education more aordable and eective—
and the potential substrate of new business models for higher education that may
compete with today’s colleges and universities. Institutions that harness technology
in the service of their educational missions—and that cannily adapt their cultures to
achieve optimal potential from technology—will stand the greatest chance of thriving
in the decades to come. is study provides guidance to help higher education leaders
make wise investments in technologies and support.
—Susan Grajek, EDUCAUSE
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
Executive Summary
Since 2004, ECAR has partnered with higher education institutions to investigate the
technologies that matter most to undergraduate students. We do this by exploring
students’ technology experiences and expectations. In 2014, the ECAR technology
survey was sent to approximately 1.5 million students at 213 institutions, yielding
75,306 responses across 15 countries. is year’s ndings are based on a stratied
random sample of 10,000 U.S. respondents and shed light on a number of topics.
General student technology experiences and expectations
• Technology is embedded into students’ lives, and students are generally inclined
to use and to have favorable attitudes toward technology. However, technology
has only a moderate inuence on students’ active involvement in particular
courses or as a connector with other students and faculty.
• Students’ academic use of technology is widespread but not deep. ey are
particularly interested in expanding the use of a few specic technologies.
• Most students look online or to family or friends for technology support. e
minority who use institutional help desks report positive experiences.
Anytime, anywhere access to learning that is enabled by device
proliferation
• More students own mobile devices now than ever. Although students rate
network performance as generally good, projected increases in connected
devices could soon challenge even the most robust campus networks.
• Many students use mobile devices for academic purposes. eir in-class use is
more likely when instructors encourage such use; however, both faculty and
students are concerned about their potential for distraction.
Learning environments
• More students than ever have experienced a digital learning environment. e
majority say they learn best with a blend of online and face-to-face work.
• Undergraduates value the learning management system (LMS) as critical to
their student experience but rarely make full use of it. Today’s undergraduates
want a mobile-friendly, highly personalized, and engaging LMS experience.
• Most students support institutional use of their data to advise them on academic
progress in courses and programs. Many of the analytic functions students seek
already exist in contemporary LMSs.
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
• Few undergraduates have taken a massive open online course (MOOC).
Students still view traditional college degrees as the gold standard for résumés.
Few students would include digital badges, e-portfolios, or competency creden-
tials on their résumés.
Although technology is omnipresent in the lives of students, leveraging technology as
a tool to engage students is still evolving. We know from looking at longitudinal data
from past student studies that students still have a complex relationship with tech-
nology; they recognize its value, but they still need guidance when it comes to using
technology in meaningful and engaging ways for academics. Students are still ready
to use their mobile devices more for academics, but we haven’t yet seen widespread
application of this. Students also still prefer blended learning environments, and their
expectations are increasing for these hybrid online/face-to-face experiences. ese are
all issues that ECAR will continue to address by surveying students (and other popu-
lations of the academic community) to contribute to the body of knowledge around
end users’ technology experiences and expectations.
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
Introduction
In 2014, the EDUCAUSE Center for Analysis and Research (ECAR) partnered with
213 higher education institutions across 45 U.S. states and 15 countries to investigate
undergraduate information technology (IT) experiences and expectations (gure 1).
In this 11th year of data collection, ECAR tracked long-term technology trends and
asked students about contemporary and forward-looking (emerging) technology
issues. More than 75,000 students responded to the survey, and the ndings in this
report were developed using a representative sample of students from U.S.-based
higher education institutions and an opportunistic sample of non-U.S. responses (see
the Methodology section for more details).1
213
45
15
institutions
states
countries
Figure 1. Student study participation overview
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
is research project was designed to gather information directly from students via an
online survey about their experiences with technology. We asked them what devices
they own, how they use them, and what their general perceptions of technology are
at their respective colleges and universities. is research is important in gaining a
better understanding of how students use technology, which aspects of technology are
important to them and to their studies, and which technologies they would like to
see used more oen. is research also provides insight into individual dierences
in students’ inclination toward technology, adding important data that contradict
some stereotypes about students and technology. In addition, ECAR conducted
a faculty technology study in 2014. By investigating both student and faculty
perspectives on technology, ECAR is able to relate technology experiences in higher
education from two vantage points. e faculty companion project used a method-
ology similar to that of the student study to collect data about faculty’s technology
experiences and expectations. Side-by-side results are oered for the most compel-
ling ndings, and a separate report about the faculty study responses is available
from www.educause.edu/ecar.
e ndings from this study can help institutions focus on technology issues that
matter most to students. Any higher education institution can contribute data to
this annual project by contacting study@educause.edu, and participating institu-
tions receive the added bonus of seeing how their students’ responses compare with
responses from students at peer institutions in a personalized peer benchmarking
report. ese reports provide a framework for contextualizing the ndings for an
institution’s students.
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
Findings
Technology is embedded into students’ lives, and students are generally
inclined to use technology. However, technology has only a moderate
influence on students’ active involvement in particular courses or as a
connector with other students and faculty.
ECAR asked students to place themselves on a series of 100-point semantic dieren-
tial scales (see appendix B) related to their IT disposition (e.g., enthusiastic versus
reluctant, early versus late adopter, technophile versus technophobe); attitude (e.g.,
satised versus dissatised, pleased versus perturbed, useful versus useless, enhance-
ment versus distraction); and usage patterns (e.g., always versus never connected,
central versus peripheral, new versus old media, frequent versus infrequent). e
resulting scores reveal that students in general consider themselves to be sophisticated
and engaged with IT, averaging signicantly above the neutral position (50) on the
scales. On average, students reported positive dispositions toward IT (64), positive
attitudes toward IT (71), and high levels of IT usage (70; gure 2).
Disposition score: 64
Attitude score: 71
Usage score: 70
Figure 2. Mean scores of student semantic differential toward technology
ECAR averaged the scores on these three factors to derive a single score we call “tech
inclination.” ose with higher scores on the disposition, usage, and attitude factors
are therefore more inclined toward technology than those with lower scores. (More
information about students’ tech inclination appears in appendix B.) ere are large
individual dierences in tech inclination, with 95% of scores falling between 39
and 97. We can use these scores to inform our understanding of students’ behaviors
around technology use. e results of these analyses are infused throughout the nd-
ings of this report.
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
We did not nd any large dierences in tech inclination related to
any of the demographic variables studied. Eect sizes for age, gender,
ethnicity, enrollment (part-time versus full-time), and residence
(on-campus versus o-campus) were small. erefore, assumptions or
stereotypes about these demographic factors (e.g., younger adults are
more tech inclined than older adults) are not supported by our data; in
fact, when it comes to age, older students rated themselves higher on
this scale than did younger students.2 Interestingly, the average student
scores on the tech inclination factors (disposition, attitude, usage)
are similar to those obtained from the faculty sample in ECAR’s 2014
study of faculty and IT.3 ese results, then, challenge the stereotyp-
ical assumption that (younger) students are considerably more tech
inclined than (older) faculty. Institutions that participated in both the
student and faculty studies in 2014 can compare the results for their
students and their faculty in a more meaningful way than our general
ndings here portray.
Given the degree to which technology is embedded in the lives of
undergraduates, one might expect to nd that students are more
prepared to use technology or that they have higher expectations of
technology to enhance the learning environment than they did a few
years ago. ECAR didn’t nd overwhelming evidence that this is the
case. Today’s undergraduates feel no more prepared to use technology
in higher education than did their counterparts from a few years ago.
About two in three students (67%) in 2014 agreed or strongly agreed
that they had adequate technology skills when they entered college,
roughly the same percentage as in the 2012 and 2013 student study
ndings. To better understand the areas in which students feel decient
in their technology preparedness, ECAR specically asked students
if they wished they had been better prepared to use basic soware
programs and applications (34% agreed) or institutionally specic tech-
nology such as the LMS (44% agreed) when they rst started college.
ese new data help explain why students are generally technologically
condent but not necessarily comfortable with their institution’s tech-
nology services and applications.
What does it mean to be tech
inclined?
ECAR categorized students into low,
medium, and high tech-inclined groups
based on their responses to the items
in the semantic differential questions.
Because the mean response is well above
the midpoint of the scale, the categorical
distribution is asymmetric, with cutoffs
as follows:
• 0–49 = technology inclination is low
(9% of respondents)
• 50–79 = technology inclination is
medium (68% of respondents)
• 80–100 = technology inclination is high
(23% of respondents)
Students with high tech-inclination
scores are those with the highest
combined positive disposition toward,
attitude about, and use of technology.
Where applicable and appropriate,
students’ tech-inclination scores are
included in the analysis and findings of
this report.
EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
10
Half of undergraduate students (49%) said they get more involved in courses that use
technology. is is up from 37% in 2010 but has remained relatively at since then
(ranging from 49% to 54%). Although technology can sometimes be distracting,
it also provides students the opportunity to stay connected with each other, their
instructors, and the institution. ECAR has been tracking student perceptions of tech-
nology as a means to connect with others since 2011; whereas technology has waned
as a connector between individual students and faculty, it has remained relatively
consistent and has even grown stronger in connecting students to the institution.4
In 2014, about half of undergraduates said that technology makes them feel more
connected to other students (51%) and to their instructors (54%), whereas two in
three said it makes them feel more connected to the institution (65%).
Students’ academic use of technology is widespread but not deep.
They are particularly interested in expanding the use of a few
specific technologies.
Figure 3 shows students’ experiences with various types of technologies and their
expectations about being more eective students if they were better skilled at using
the technology. Most students have used the learning management system in at
least one course (83%), but only about half (56%) have used it in most or all of their
courses. ese numbers seem rather low, given that 99% of higher education insti-
tutions have an LMS in place and 86% of faculty say they use the LMS. A separate
ECAR study, e Current Ecosystem of Learning Management Systems in Higher
Education, explores this particular topic in greater detail.5 It demonstrates that faculty
and students value the LMS as an enhancement to their teaching and learning experi-
ences, but relatively few use these systems to their full capacity. By looking at students’
experiences with technology and their expectations of that same technology, we can
see where opportunities exist to more fully use and holistically integrate a technology
into the teaching and learning environment, and to better train users.
The majority of undergrad-
uates said most or all of
their instructors
• have adequate technology
skills for carrying out
course instruction (72%
in 2014, up from 66%
in 2013)
• effectively use technology
to support academic
success (68% in 2014,
about the same as in
2013, 67%)
— ECAR student studies, 2013
and 2014
EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
11
Percentage of respondents
...the course or learning
management system
...my laptop during class*
...my tablet during class*
...e-portfolios
...3D printers
...nonkeyboard or nonmouse
interfaces
...my smartphone during class*
...social media as a learning tool
...simulations or
educational games
...recorded lectures or
“lecture capture”
...online collaboration tools
...e-books or e-textbooks
Did not use Used in at least one course
In the past year I used (or did not use)...
Agree/strongly agree they could be more
effective if they were better at using it
50
250% 75 100%
Used in most or all courses
*Among device owners
Figure 3. Extent of technology use and expectations that the technology can
increase student effectiveness
To better understand students’ expectations for course technology, ECAR asked them
which technologies they would like their instructors to use more…or use less (gure
4). Lecture capture, early-alert systems, and freely available course content top the list
of what students want their instructors to use more.
Each of these technologies also has a low “use it less” companion percentage. Two technol-
ogies, social media as a learning tool and e-portfolios, had “use it less” rates that exceeded
the “use it more” rates. Comparing these data with data from previous years, we see small
but noticeable declines in nearly all “use it more” rates. E-portfolios and simulations/
educational games were the only two technologies whose “use it more” rates increased (by
3 and 2 percentage points, respectively). Tech inclination is positively related to all items
in the “use it more/use it less” question (r = .153–.297). In other words, the more tech
inclined students are, the more they wish their instructors would use these resources/tools.
In 2013, 49% of students
said it was important to
be better skilled at using
technology. In 2014, 34%
said they wish they had
been better prepared to use
basic soware programs
and applications, and 44%
said they wish they had been
better prepared to use insti-
tutionally specific technology
when they entered college.
A student’s advice to faculty:
“Have more online content
to support course content.”
— Anonymous ECAR 2014
student survey respondent
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
Percentage of respondents
*Among device owners
50
250% 75 100%
The course or learning management system
My laptop during class*
My tablet during class*
E-portfolios
3D printers
Wish their instructors
would use it more
Wish their instructors
would use it less
Early-alert systems
Freely available course content
My smartphone during class*
Social media as a learning tool
Simulations or educational games
Recorded lectures or “lecture capture”
Online collaboration tools
E-books or e-textbooks
Figure 4. Percentage of students saying they wish their instructors would use a
technology more…or less
Most students look online or to family or friends for technology support.
The minority who use institutional help desks report positive experiences.
When they need technology support or assistance for school-related activities,
students most frequently search online resources such as Google or YouTube (71%;
gure 5). is is not surprising, since the Internet has changed the way in which ques-
tions are asked and answered. In a 2012 study by the Pew Research Center’s Internet
& American Life Project, 94% of the teachers surveyed said their [teen] students are
“very likely” to use Google or other online search engines in a typical research assign-
ment.6 It makes sense that these teens (now older) would transfer this skill set to the
way they seek tech support. Next to “Googling it,” students most frequently look to
those closest to them for immediate technology assistance (69%). Younger students
(76%) are more likely than older students (54%) to look to peers, family, and friends
or to look online for support. Older students (31%) are more likely than younger
students (19%) to use the help desk. Females, part-time students, upperclassmen, and
students who are highly tech inclined are also more likely to use the help desk.
In 2014, 73% of students
agreed or strongly agreed
that they like to keep their
academic and social lives
separate. This is up from
60% in 2013 and provides
context for why just one
in three students said
they wish their instructors
would use social media
as a learning tool more,
fewer than those who said
they wish their instructors
would use it less.
— ECAR student studies,
2013 and 2014
EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
13
Among the one in ve students who said they use the college or university help desk,
76% rated their experience as good or excellent, 9% rated the experience as fair or
poor, and the remaining students had a “neutral” experience. Help desk ratings by
service modality are displayed in gure 5, with the greatest percentage of positive
ratings (good/excellent) going to services that require personal interaction, such
as walk-in service (79%) and e-mail or phone help (both about 70%). Impersonal
activities, such as using a self-service FAQ (52%) or a web-based form (54%), received
some of the lowest service ratings.
Percentage of respondents
Rating of help desk services
Phone
Web form
Walk-in
Overall rating of help desk
Self-service FAQ
When students need tech help, they look to:
Chat/instant messaging
E-mail
Poor Fair
50
250% 75 100%
50
0% 100%
Neutral Excellent
Good
The
company
or vendor
Instructors
or TAs Peers, friends, or
family
Google, YouTube, or
another online
source
The college/
university
help desk
22%
Figure 5. Students’ experiences with technology support networks
Students who are
highly tech inclined
(as measured by their
tech-inclination score)
are more likely to agree
that
• They get more actively
involved in courses
that use technology*
• They felt adequately
prepared to use the
technology needed in
their courses when
they entered college
• Technology makes
them feel more
connected to the insti-
tution, faculty, and
other students
Students lower in tech
inclination are more
likely to agree that
• The in-class use of
mobile devices is
distracting
• They wish they had
been better prepared
to use both institu-
tionally specific tech-
nology (e.g., the LMS)
and basic soware
programs (e.g., MS
Office)
* Notably, students who are
more tech inclined agree that
they get involved in courses
that use technology at five
times the rate of less tech-
inclined students.
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
More students own mobile devices now than ever. Although
students rate network performance as generally good,
projected increases in connected devices could soon
challenge even the most robust campus networks.
Mobile device ownership continues to increase, with 86% of under-
graduates owning a smartphone in 2014 (up from 76% in 2013) and
nearly half of students (47%) owning a tablet (up from 31% in 2013).
Laptop ownership has leveled o, with 90% of students owning one in
2014 (up 1% from 2013). Figure 6 depicts device ownership history,
2015 projections, and relative comparisons with the current adult
population. ECAR stopped tracking desktop computer ownership in
2014, but projections from past data suggest that it is about half of the
undergraduate student population.
In 2013, ECAR began asking students who didn’t yet own specic
devices about their intentions to purchase those devices in the next
year. We overestimated rst-time laptop purchases by 5 percentage
points, our estimate for tablets was spot on at 16%, and new-to-market
smartphone purchases were underestimated by 3 percentage points.
Projected device ownership for 2015 is depicted in gure 6. Comparing
undergraduate student device ownership with Pew’s media and tech-
nology trend data, undergraduates own laptops and smartphones at
much higher rates than the general adult population. Pew estimates that
61% of all adults own a laptop and 58% own a smartphone. (According
to Pew, 83% of adults ages 18–29 have a smartphone.) Student tablet
ownership is only slightly higher than Pew’s general population esti-
mate (47% versus 42%).7
Percentage of all students saying
they use these devices in class for
class-related purposes:
70%
laptops
59%
smartphones
35%
tablets
Among device owners, in-class
use is
74%
laptops
66%
smartphones
62%
tablets
— ECAR student study, 2014
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
0%
25
50
75
100%
Percentage owning
2012
ECAR
study
’13 ’14 2015
ECAR
projection
Adult population,
Pew survey
E-reader
Tablet
Smartphone
Laptop
Figure 6. Device ownership history and 2015 projections
Undergrads and smartphones
Smartphone ownership for undergrad-
uates doesn’t vary much by student
demographics or institution type, but
those who rank high on ECAR’s tech-
inclination classification are more likely
to own smartphones.
Smartphone ownership by students’
tech inclination:
90%
high
87%
medium
69%
low
—ECAR student study, 2014
* Tech inclination is measured by students’ semantic
differential scores, displayed in figure 2.
EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
16
e consumerization of technology and the bring your own device (BYOD—or
even bring your own everything [BYOE]) trend means that students have a highly
competitive market from which to choose their device brand and operating system.
Fewer students own Windows laptops in 2014 (69%) than in 2013 (75%), but
Windows still dominates the laptop market. Among tablet owners, iPads have 58%
of the undergraduate market share (down from 63% in 2013). e smartphone
market share was stable from 2013 to 2014, with 54% of smartphone owners having
an iPhone and 43% having an Android phone this year. Compared with older
students (ages 25-plus), younger students (ages 18–24) favor Mac/iOS products.
In countries other than the U.S. and Canada, the Android OS is more popular.
Students operate in a diversied, consumer-oriented market for technology, and
an institutional mobile strategy that is device agnostic will prove to be robust yet
adaptable as more products pour into the market.
Today’s campus networks need to accommodate dierent types of devices and
operating systems, as well as growing numbers of devices per student. More than
half of undergraduates (54%) say they typically connect at least two devices to the
college/university network at the same time. Younger students are the power users
of college/university networks; nearly twice as many students under 25 years of age
connect two or more devices at a time to the network (61% compared with 35% for
students 25-plus). About 1 in 10 students (8%) try to connect three or more devices
to the network at the same time,8 and this will likely increase as wearable technology
and the Internet of ings9 matures into everyday devices that students can use and
aord.10 Figure 7 shows that a majority of students rate their network experience as
good or excellent. ough younger students (the power users) are more critical of
their network experience than older students, the majority of them still rate network
experiences as good or excellent.
Among the 99% of students
who own an Internet-capable
device,
8%
own just one device
92%
own at least two devices
59%
own three or more devices
— ECAR student study, 2014
Internet of Things to challenge network capacity
It is important to note the extent to which experts forecast the rapid growth
of Internet of Things technologies in order to prepare for the potential
impact on campus networks and the potential opportunities for adminis-
trative or pedagogical applications. A recent International Data Corp (IDC)
forecast projects wearable technology will exceed more than 19 million
units in 2014, with smart device breakthroughs occurring through 2016,
and Gartner projects the installed base of the Internet of things to reach 26
billion units by 2020.1
1. Ramon T. Llamas, Worldwide Wearable Computing Device 2014–2018 Forecast and
Analysis, IDC Report, March 2014; Peter Middleton, Peter Kjeldsen, and Jim Tully,
Forecast: The Internet of Things, Worldwide, 2013, Gartner Report, November 2013.
17
EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
Percentage of respondents giving good/excellent ratings
Network performance
Ease of login to Wi-Fi network
provided by the institution
Reliable access to Wi-Fi specifically in
classroom/instructional spaces
Reliable access to Wi-Fi
throughout campus
50
250% 75 100%
Ages 18-24 Ages 25+
Figure 7. Percentage of students rating wireless network experiences as good or
excellent
Many students use smartphones or tablets for academic purposes, although
in-class use is still uncommon. Students are more likely to apply mobile
devices to academics when instructors encourage their use in class.
Noticeably more students used their smartphones, tablets, and e-readers for
academics in 2014 than in previous years, a nding that corresponds with the general
trend in increased device ownership. Among students who use these devices for
academic work, attitudes about the importance of these devices hasn’t changed much
during this same time period (gure 8). e importance students place on these
devices is directly related to their tech-inclination level. In other words, the more
tech inclined students are, the more likely they are to use the devices for academic
purposes11 and the more important they rate the devices to their academic success.
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
0%
25
50
75
100%
Percentage of respondents
E-reader Tablet Smartphone Laptop
2012 ’13
’14
Use for academics Importance to academic success
(very/extremely important)
Figure 8. Changes in use and importance of devices for academics12
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
Mobile devices for academics
Although increased use of personal digital devices for academics corresponds with trend increases in device
ownership, attitudes about the importance of these devices for academic success haven’t changed much. A few
data points help to explain this:
• Few instructors (30%) create assignments that incorporate mobile technology, suggesting there is not a
widespread strategy for leveraging personal mobile technology in the classroom.
• Many instructors (67%) agree that in-class use of mobile devices is distracting, with over half (55%) banning
or discouraging their use.
• About half of undergraduates (47%) are also concerned that in-class use of mobile devices can be distracting.
• Few undergraduates have experience using personal devices with regularity across courses (31% of laptop
owners, 19% of smartphone owners, and 25% of tablet owners use their devices in most or all of their
courses), suggesting either they are not allowed to use these in class (see figure 9) or don’t see the value in
using these devices.
Given this context, it is not at all surprising to see flat or decreasing trends in students’ attitudes about the impor-
tance of these devices for their academic success. Mobile devices in particular have not been embraced by faculty as
engaging teaching and learning tools, and students have yet to see the value in using them for their academic work.
Figure 9 shows students’ 2014 in-class BYOD experiences.13 Comparing this year’s
data with the 2013 student study results, we see almost no growth toward embracing
personal mobile device use in the classroom. ough twice as many students were
encouraged or required to use a smartphone in class this year compared with last year,
this was still only 6% of students. Smartphones are still the most likely devices to be
discouraged or banned from in-class use, with 69% of students reporting so in 2014
(down from 74% in 2013).
Percentage of respondents
Smartphone
Laptop
Tablet or iPad
Wearable (e.g., Google Glass)
Banned Discouraged
50
250% 75 100%
Neither discouraged
nor encouraged
Required
Encouraged
Figure 9. Students’ in-class BYOD experiences
EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
Undergraduate Students and IT, 2014
20
Concerns that mobile devices are unwanted distractions rather than critical learning
tools are justied by recent literature showing that multitasking is less productive
rather than more productive14 and that nearly all students will use mobile devices
for non-class-related purposes when given the opportunity.15 In addition, research
has shown that taking handwritten notes enables learning more than taking notes
via laptop.16 However, these types of studies oen focus on the pitfalls of replacing
manual activities (such as taking notes) with technology rather than using technology
in meaningful ways to engage students in the learning process. Laptops and mobile
devices can be used in certain types of class activities to form additional connections
with material, thereby also enhancing learning.17 Designing course activities and
assignments that use mobile devices to deepen engagement for students is one way to
harness the power of these tools as academic resources rather than distractions.
Some devices are encouraged or required more oen than others. ECAR found a
large dierence (20 percentage points) in the percentage of students using tablets for
academic purposes and a small dierence (7 percentage points) in the percentage of
students using smartphones for academic purposes when students are encouraged or
required to use these in class as opposed to when their use is discouraged or banned.
ere is almost no dierence in the percentage of students using laptops when
they are encouraged or required, likely because of a ceiling eect of laptop use for
academic purposes. In other words, most students use laptops for academic purposes
regardless of whether their use in class is encouraged.
Handheld mobile devices are important multipurpose tools for students. Among
students who said they use a smartphone or tablet, the percentage reporting each
of various activities as at least moderately important appears in gure 10. e top 5
issues are a mix of administrative tasks (checking grades and accessing the LMS) and
engagement activities (communicating with other students outside class and looking
up information while in class).
Students who own each
of these devices say they
could be more effective if
they were better skilled at
using…
52%
my laptop
48%
my tablet
37%
my smartphone
…in class
— ECAR student study, 2014
Instructors say they could
be more effective if they
were better skilled at
integrating…
45%
students’ laptops
45%
students’ tablets
34%
students’ smartphones
…in their courses
— ECAR faculty study, 2014
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Percentage reporting at least moderate importance
Record instructor’s lecture or in-class activities
Use the mobile device as a digital passport
for access or identification
Participate in interactive class activities
Register for courses
Access library resources
Capture static images of in-class activities or
resources
Read e-texts
Access information about events, student
activities, and clubs/organizations
Use the course or learning management system
Look up information while in class
Check grades
Communicate with other students about
class-related matters outside class sessions
50
250% 75 100%
Figure 10. Importance of using a handheld mobile device for various student-
related activities
Institutions are responding to the demand for student-facing mobile-enabled services.
Nearly all students (92–96%) reported that they can access enterprise-level systems
from their handheld mobile devices. Figure 11 shows the mobile-enabled services
students use and their assessments of them. Younger students (18–24) consume
mobile-enabled services at higher rates than older students (ages 25-plus) and are
more critical of the service. ese data could be an indicator that student-facing
college and university services and applications are not as mobile friendly as they
could be. Looking to younger students to predict expectations of tomorrow’s students
will help higher education IT units develop and prioritize mobile-friendly services
and activities. With continued increases in mobile device ownership and more
consumer experience with transaction-oriented mobile device activities in other areas
of their lives (e.g., banking and shopping), students’ expectations for mobile access
will likely increase.
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50
250% 75 100%
Check grades
Access information about events, student
activities, and clubs/organizations
Used in the past year Rated good/excellent
Read e-texts
Use the course or learning
management system
Register for courses
Access library resources
Percentage of respondents
Figure 11. Use of and experience with institutional services on mobile devices
More students than ever have experienced a digital learning
environment. The majority say they learn best with a blend of online
and face-to-face work.
More than four in ve students (85%) took at least a few courses that were blended
(contained at least some online components and some face-to-face components) in
the past year, up from 79% in 2013. Students are more likely to experience blended
courses at public than private institutions. Additionally, almost half (47%) have taken a
completely online course during this same time period. is is similar to 2013 (46%).
When asked in which type of environment they tend to learn most, 72% of students
said that courses with some online components are preferred for learning. Only 18%
of students said they learn most in courses with no online components, down from
25% in 2013. e number of online components students say is best for learning
in their courses depends on their age (gure 12). Whereas more younger students
(ages 18–24, 74%) than older students (ages 25-plus, 66%) say having some online
components is better for learning, older students (19%) are more likely than younger
students (6%) to say they learn best when a course is completely online. In addi-
tion, part-time students (16%) are more likely than full-time students (9%) to say
that they learn most in completely online courses, and o-campus students (12%)
are more likely than on-campus students (3%) to say they learn most in completely
online courses. Older, part-time, and o-campus students are also more likely to have
taken an entirely online course in the past year. ese data align with other research
showing that older, nontraditional students are more likely to take online courses and
MOOCs.18 Note that taking an online course (which may be a matter of convenience)
is a separate issue from stating that one learns better in such courses.
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Percentage of students who reported a preference
Ages 25+
Ages 18–24
Have no online
components
Have some online
components
Are completely
online
50
250% 75 100%
Students say they learn most in courses that...
Figure 12. Students’ learning environment preference for online components in
courses, by age
Students who say they prefer completely online classes have higher mean tech-
nology inclination scores (75) than students who say they prefer no online compo-
nents at all in their classes (61). In fact, students who are the most tech inclined
prefer completely online courses at more than three times the rate of the least
tech-inclined students (17% to 4%). ose who are less tech inclined prefer fewer
online components in their courses.
Undergraduates value the LMS as critical to their student experience but
rarely make full use of it. Today’s undergraduates want a mobile-friendly,
highly personalized, and engaging LMS experience.
e learning management system is a staple in higher education. Nearly all higher
education institutions (99%) have at least one.19 ese systems are multifaceted. ey
function as digital learning environments, administrative systems for course manage-
ment, and enterprise systems for institutional analytics and other purposes. Seven in
10 faculty (72%) say the LMS is a very useful tool for student learning, and the LMS as
a digital learning environment has great potential to extend the traditional classroom
space into the boundless Internet.
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Because of the functional importance of the LMS to higher education, ECAR asked
students a series of questions about their experiences with and expectations of the
LMS. Despite the systems’ ubiquity and the fact that 58% of institutions preload the
LMS with basic course content, only about one in two students use the systems in
most or all of their courses (56%). Students who are more tech inclined use the LMS
to a greater extent (r = .132). ough the LMS might not be applicable for every
assignment in every class, these data suggest the LMS could be underused as an
anytime, anywhere digital learning environment. Figures 13–16 show four dimen-
sions of students’ typical experience with the LMS.
Did not use
at all Used in at
least one course
17% 12%
Used in about half
of my courses
In the past year, to what extent have you used the LMS?
16%
Used in most of
my courses
28%
Used in all my
courses
28%
Figure 13. Students’ use of the LMS
e majority of students (61%) who used the LMS from a mobile device rated their
institution’s support for this activity positively (as good or excellent). is still
leaves room for an improved experience for about two in ve students. Note that
few (8%) gave the lowest rating of “poor,” so LMS improvements may not need to be
epic overhauls.
I could be a more effective student if I were better skilled at using the LMS.
Institutional support for the LMS on a handheld mobile device
Poor Fair
8% 13%
Neutral
19%
Good
40%
Excellent
20%
Figure 14. Students’ ratings of institutional support for the LMS
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Half of students (51%) said they could be more eective if they were better at using
the LMS (gure 15). is nding was nearly universal among dierent types of
students and institutions. e exception was that students who ranked highest on the
ECAR tech-inclination scale agreed or strongly agreed at higher rates than students
on the lower end of the scale that they could be more eective if they were better at
using the LMS (57% versus 42%). Perhaps students with greater tech inclination see
the potential for using the LMS in more engaging ways and aspire to do so, whereas
students with less tech inclination are more likely to take the LMS at face value.
Regardless of the explanation, these data are evidence that there is misalignment
between the ways always-connected students (many with a lifetime of technology
exposure) use technology to connect socially or for entertainment purposes and the
ways they use technology in educational activities.20
In the past year, to what extent have you used the LMS?
I could be a more effective student if I were better skilled at using the LMS.
Strongly
disagree Disagree
8% 9%
Neutral
32%
Agree
33%
Strongly agree
18%
Figure 15. Students’ assessment of their need for additional LMS skills
ree in four students (78%) said it was at least moderately important to access
the LMS from a handheld mobile device; it was extremely important for 33% of
respondents (gure 16). is supports the longitudinal ndings from previous
ECAR student studies in which we reported that students hold high expectations
for anytime, anywhere access to course materials and for leveraging the use of their
personal digital devices inside and outside class.
Institutional support for the LMS on a handheld mobile device
How important is it that you are able to access the LMS from a handheld mobile device?
Not at all
important Not very
important
11% 11%
Moderately
important
20%
Very important
24%
Extremely
important
33%
Figure 16. Students’ expectations of LMS access from handheld mobile devices
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ECAR also asked students about their satisfaction with various features and opera-
tional functions of the LMS. Satisfaction levels were highest for basic features such
as accessing course content; they were lowest for advanced features such as using the
LMS in engaging or collaborative ways (gure 17). ECAR found similar results in the
2014 faculty study, with only about half of faculty (51%) saying they were satised
with the LMS as a way to engage in meaningful interactions with students.
Percentage of respondents who were satisfied/very satisfied
Collaborating on projects for study groups
with other students
Accessing course content
Submitting course assignments reliably
Checking course progress
Managing your assignments
Receiving timely feedback on course
assignments
Accessing news about your institution
Receiving meaningful feedback on course
assignments
Engaging in meaningful interactions with
your instructors
Engaging in meaningful interactions with
other students
50
250% 75 100%
Figure 17. Overview of student satisfaction with LMS features and operational
functions
Students were asked what features they would add if they could design the LMS from
scratch. e top 5 issues they noted for improvement were:
1. Better features for interaction and communication
2. A more user-friendly interface
3. More (or better) instructor participation
4. Ease of access to journals or other resources
5. Better functionality, e.g., having the LMS function on a touchscreen
environment
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As published in the recent report on the future of learning management systems,21
ECAR also asked students how interested they are in their institution’s providing them
with various aspects of personalization to the LMS, with the majority (about three
in ve) showing enthusiasm for each personalized feature suggested (gure 18). e
majority of students (69%) are very or extremely interested in having the LMS provide
personalized support and information about progress toward their degree goals.
Transitioning from independent administrative and enterprise systems to systems
that are interoperable is a general trend in higher education. With the maturation of
learning analytics in higher education, interoperability between the LMS and other
administrative systems (such as the student information system and planning and
advising systems) is increasingly important. Although many LMS products have
built-in analytics capabilities such as early alerts and progress tracking, many insti-
tutions are not yet taking full advantage of them, nor are they using them to support
student success initiatives. is gap is due in part to the complexities of the data and
systems-integration processes. Addressing this gap is important because a majority of
students have an interest in real-time feedback about their course progress through
personalized dashboards in the LMS (60% of students were very or extremely inter-
ested in this feature). ese are features that help students visualize how they are
doing in individual courses. An additional area of interest concerns adaptive learning
functions of the LMS, whereby students are provided with personalized quizzes or
practice questions oriented to their specic strengths or weaknesses so that they (or
their instructors) know what help they need (62%).
Support and information on
degree progress
Quizzes or practice questions
Visualizations and dashboards
Not at all
interested
Not very
interested
Very
interested
Extremely
interested
Moderately
interested
Percentage of respondents
50
250% 75 100%
Figure 18. Student interest in personalized LMS features
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Most students support institutional use of their data to advise them
on academic progress in courses and programs. Many of the analytic
functions students seek already exist in contemporary LMSs.
Students were given the following background information about learning analytics
in higher education and asked to provide their opinion:
Many colleges/universities are starting to use the data they collect from/
about students to create individualized messages about academic progress,
training, and guidance opportunities. ese data could come from transac-
tional records (e.g., logging in/out of a campus website/application/service),
tracking activities from your student ID/smart card, or direct input from
your advisors, counselors, or instructors. Which statement best describes
your opinion of this practice?
e majority of students are supportive of analytics, with two in three (68%) saying
they think the above explanation of learning analytics “sounds positive” or that it is
“great” (gure 19). Fewer than 1 in 10 (9%) expressed a negative point of view, and
about 1 in 4 (23%) were neutral about the topic. Students who ranked higher on the
ECAR tech-inclination scale tended to support analytics; the more tech inclined the
student, the higher their opinion of this practice (r = .177).
I am totally
against this. I think this is great.I am neutral.
This sounds
negative. This sounds
positive.
23%
6%
3% 43% 24%
Figure 19. Student opinions about data collection for learning analytics
ECAR also solicited opinions about specific learning analytics features that
could be “…made available through the LMS or through an integrated planning
and advising system (IPAS).” In 2013, 76% of students said they were at least
moderately interested in their institution’s providing guidance about course
offerings , such as “you may also like” or “we recommend” suggestions; this
year, 89% of students said they were at least moderately interested in guidance
about courses they might consider taking in the future. Nearly 9 in 10 students
(89%) in 2013 were at least moderately interested in their institution’s using
information about them to alert them to new or different academic resources.
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This year’s study had similar results (figure 20). The vast majority of students
are at least moderately interested in learning analytics, with automated tracking
of course attendance being something of an outlier. Only two in three students
(65%) said they were interested in this. Tech inclination is positively related to
all items in figure 20 (r = .148–.232). In other words, the more tech inclined
students are, the greater their interest in these analytics features. Though the
effect size across all students is small, the spread between students scoring
high on tech inclination and those scoring low on tech inclination is at least 19
percentage points for each item.
Percentage reporting at least moderate interest
…automated tracking of their course attendance
…suggestions for how to improve performance
…guidance about courses they might
consider taking in the future
…alerts if it appears their progress in a
course is declining
…suggestions about new or different academic
resources
…feedback about their performance compared
to that of other students
50
250% 75 100%
Students are interested in the use of learning analytics for...
Figure 20. Student interest in automated learning analytics features
According to the EDUCAUSE Core Data Service, 49% of U.S.-based institutions
have early-alert systems, 72% have academic advising systems, and 78% have
education planning/academic progress tracking systems. As academic leaders make
decisions about deploying, improving, or replacing these systems, they can point to
strong student interest in exploring analytics for academics.22 Eight in 10 faculty are
at least moderately interested in early-alert and intervention notication systems for
students, as reported in the ECAR study of faculty and IT.
Because higher education is still trying to understand the role of learning analytics
to improve academic performance, ECAR asked students an open-ended question
about what other alerts to consider or what advice they have for their institutions
concerning alerts. e top 4 answers among a random sample of 400 respondents
within the U.S.-based weighted sample of respondents are as follows:
Students are enthusiastic
about their instructors’
use of early-alert systems
to notify them of course
progress issues:
65%
say use it more
22%
say use it about the same
13%
say use it less
—ECAR student study, 2014
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1. Posting grades, class participation, attendance, and performance comparisons
(23%). Students are inherently interested in how they are doing in classes with
respect to their peers.
“Feedback about your performance compared to that of other students
in your class or your major.”
—Anonymous student comment
2. Calendar and schedule information. Event demarcations and reminders of
when assignments, quizzes, and other assessment items are due (22%). Students
want to leverage technology to keep them on task and on target for submitting
assignments on time.
“Definitely a time management one, giving you personalized deadlines
for readings and assignments so that you always knew what to keep on
top of.”
—Anonymous student comment
3. Supplemental information to enhance lecture and textbook material, practice
quizzes, and additional content-related workshops (17%). Students are inter-
ested in supplementing (not supplanting) course content with online resources.
“Ways to enhance your learning experience, build on material you’ve
learned, direction to off-campus opportunities for learning (such as
colloquia, conferences, or even MOOCs), etc.”
—Anonymous student comment
4. Early alerts, course and program guidance, and other personalized outreach in
the form of one-to-one communication with students (14%).
“It would be nice to be alerted on what you need to improve and maybe
get some type of e-mail link of some practice test or an instructional
media on how to improve.”
—Anonymous student comment
e rst three items on this list can easily be accommodated by basic features of
the LMS: current status (grades, new assignments posted) notications, upcoming
reminders (a calendar that would alert students to upcoming assignments/tests), and
ways to improve (alerts that let students know about supplemental information such
as practice tests and workshops). e fourth item includes more holistic educational
planning approaches wherein IPAS systems and services would come into play.
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Few undergraduates have taken a MOOC. Students still view
traditional college degrees as the gold standard for résumés. Few
students would include digital badges, e-portfolios, or competency
credentials on their résumés.
Only 6% of student respondents took a MOOC in the past year, which is twice the
rate of the previous year. Of those, half completed a MOOC (in 2013 the completion
rate was about 33%; gure 21). Males (8%) were more likely than females (4%) to
have taken a MOOC. Asians were more likely than other ethnicities to have taken a
MOOC. is breakdown was true for the previous year’s data as well.
ree in four students (76%) in 2014 said they do not know what a MOOC is. It is not
surprising that MOOCs are not getting a lot of traction in the undergraduate student
population, since this is not really the target audience of most MOOC providers.
Although undergraduates are no more aware of MOOCs than in 2013, more are
taking them and even more are completing them. However, whereas nearly half of
students took an online class in the past year (47%), very few have taken a MOOC
(6%) or knowingly earned a competency-based digital badge (7%). Students who
take MOOCs and earn digital badges have higher mean tech-inclination scores than
students who don’t.
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0%
25
50
75
100%
Percentage of respondents
Have taken a
MOOC
Haven’t taken a
MOOC
0%
20
40
60
80%
0%
2
4
6
8%
No, and I don’t know
what a MOOC is.
No, but I do know what
a MOOC is.
Yes, but I didn’t
complete one.
Yes, and I
completed one.
2013 2014
2013 2014
2013 2014
Figure 21. Students’ experience with MOOCs23
Badges are emerging as a way for individuals to digitally document ongoing commu-
nity engagement, professional development, and accomplishments, and they recog-
nize incremental learning in highly visible ways. Badges help create a learning path
and can benet a career portfolio. Microcredentialing is quickly emerging as a way
for professionals to document ongoing development and accomplishments.24
Despite this emerging trend for microcredentialing, only 7% of student respon-
dents earned a digital badge or other type of digital credential that certies their
competency in a topic, activity, or subject area in the past year. More notably, 27%
of students in the 2014 study didn’t know whether they had earned a digital badge,
suggesting that digital badges are not yet salient indicators of success or progress
33
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for students. Males (9%) were more likely than females (5%) to earn a digital badge.
MOOC completers were more inclined to have received a digital credential than
students who didn’t complete a MOOC (37% versus 6%). Most students (90%)
would include an undergraduate degree on their résumé, and a majority (53%)
would include a certicate from an accredited college or university. However, only a
minority of students would include an industry-based training program certicate
(35%), a certicate resulting from freely available course content work (26%), a digital
badge (21%), or an e-portfolio (18%) on their résumé (gure 22).
Percentage of respondents saying they would include this type
of credential on their resume
E-portfolio
Undergraduate degree
Academic certificate
Industry certificate
Freely available course
content certificate
Competency-based
digital badge
50
250% 75 100%
Figure 22. Student intent for using degrees, certificates, badges, and other
credentials on their résumé
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Conclusions
Although technology is omnipresent in the lives of students, leveraging technology
as a tool to engage students in meaningful ways and to enhance learning is still
evolving. It is incongruous that 9 in 10 students rate themselves as having favor-
able technology inclinations, yet their technology experiences and expectations
suggest they lack the motivation, opportunity, or aptitude to use the full potential of
technology for academic purposes. For example, we see widespread use of tech-
nology among the student population, but a surprisingly high number of students
said they could be more eective in their student role if they were better skilled at
using dierent types of technologies. is includes institutional technologies such
as the learning management system and personal technologies such as in-class use
of laptops or tablets. ese ndings challenge the notion that students inherently
know how to use technology, and they compel us to nd learning-centric oppor-
tunities in the application of 21st-century technology to 21st-century education.
Moving in this direction will require experienced and thoughtful IT leadership to
help institutions optimize the impact of IT on academics. e future of technology
in higher education has less to do with the technology and more to do with the
leadership guiding the strategic use of technology.25 Strong IT leadership can help
bridge the gaps between student expectations and their classroom experiences (and
experiences with faculty technology use).
We live in an era in which technological innovation is so prolic that it is nearly
impossible to keep up with all of the options students (and faculty) have as technology
consumers. It is also nearly impossible to predict the next new technology innovation
and how it will replace, integrate with, or supplement current technology. Successful
technology leaders will be those who have invested in a robust yet nimble IT infra-
structure that can adapt to the growing possibilities technology brings to the teaching
and learning universe. We also need to promote a culture of innovation and exper-
imentation among students and faculty. Both populations are tech inclined enough
to gure out most of what needs to be done to leverage the technologies available to
them. Support, encouragement, and (research-driven) guidance will go a long way
in closing the gap between the promise that technology brings to education and the
reality of technology being used in meaningful ways to promote student learning.
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Recommendations
Although students are generally tech inclined, they do not necessarily
use technology to the full extent in supporting or enhancing their
academic endeavors.
• Do not assume that all students are tech inclined; assess incoming students’
technology literacy as it applies to institutional services and applications, and
direct those who are less tech inclined to supplemental or more personalized
help features for those services and applications.
• Use research on eective learning strategies to oer programs that help students
connect with technology in ways that enhance engagement, promote learning,
and help students stay connected with others.
• Support and encourage faculty in using technology as a tool to enhance teaching
and learning, and oer guidance on how to do so while minimizing the poten-
tial for distraction.
Students’ academic use of technology is widespread but not deep.
• Measure the extent to which students use the technologies the college or univer-
sity has deployed.
• Calibrate usage metrics of these technologies with institutional priorities;
implement policies, systems, or training programs that align with institutional
priorities to increase technology use in key areas.
• Benchmark against initial usage metrics to assess progress toward meeting insti-
tutional priorities.
• Provide students (and faculty) with specic guidance on productive uses of
technology in the classroom.
Students look to diverse sources for technology support.
• Have clear and accessible service-level options for students who look to the
college and university for tech support.
• Champion the paradigm shi to the DIY support (e.g., using Google or YouTube
and asking friends or family) that accompanies the bring-your-own-everything
culture, but be prepared to refer students in nding and using this support.
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Students operate in a diverse, consumer-oriented market for technology,
and institutions must provide infrastructure for the bring-your-own-
everything culture.
• Work on developing an agile, device-agnostic institutional mobile strategy
that will prove robust yet adaptable as more products come to market. Start by
assessing the current architecture for gaps in mobile agility.
• Create a scalable infrastructure and support the proliferation of mobile devices.
• When new products or services come to market, look to your campus IT
thought leaders or innovative faculty or sta for ideas and opportunities to
adapt the technology for administrative and pedagogical applications.
Increased use of personal digital devices for academics corresponds with
trend increases in device ownership among undergraduates over the past
few years, yet attitudes about the importance of these devices haven’t
changed much.
• Design course activities and assignments such that students’ personal mobile
devices can be used to deepen engagement.
• Look to students who consume mobile-enabled services at higher rates as a way
to predict expectations for tomorrow’s students. Develop and prioritize BYOD
and mobile-friendly services and activities.
• Assess the mobile-friendly nature of student-facing college and university
services and applications.
The majority say they learn best with a blend of online and face-to-face work.
• Assess local student demand for mixed-modality learning environments and
reconcile student demand with current oerings.
• Develop programs, services, and support to meet students’ expectations for
blended learning opportunities. Start by evaluating whether current services
and support practices are adapted for blended modalities.
Undergraduates value the LMS as critical to their student experience but
rarely make full use of it; tomorrow’s digital learning environment will
need to bridge this gap.
• Raise user awareness of LMS features.
• Provide training and support that are integrated into the LMS.
• Prioritize the user-friendliness of system interfaces when making new LMS
purchases or when making suggestions for upgrades to the current LMS.
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Students are open to institutional use of their data for advisement
on academic progress in courses and programs, and many of these
personalizing features exist in contemporary LMSs.
• Consider employing features that allow for immediate and integrated course
assessment feedback to students.
• Deploy features that allow students to share or view information about their
assessment metrics in comparison with other students’ performance.
• Look for ways to allow students to customize their view of their course progress.
• Encourage students to use the calendar, schedule, and reminder features of the
LMS for task management.
• Use the LMS to curate supplemental information from course lectures, quizzes/
tests, textbooks, and other content-related information.
• Deploy LMS features or an integrated planning and advising system that
provides students with early alerts, course and program guidance, and
personalized outreach of one-to-one communication to students.
Undergraduates still view the traditional college degree as the gold
standard for résumés but are experimenting with digital badges and
competency-based credentials. MOOCs are still novel for undergraduates.
• Experiment with microcredentialing (e.g., digital badges) to help familiarize
faculty and students with the process and potential value.
• Consider whether MOOCs t into the institution’s overall e-learning strategy.
If so, educate students about MOOCs and their potential value as possible
supplemental learning activities.
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Methodology
In 2014, ECAR conducted its latest annual study of undergraduate students and infor-
mation technology to shed light on how IT aects the college/university experience.
ese studies have relied on students recruited from the enrollment of institutions
that volunteer to participate in the project. Aer securing local approval to partici-
pate in the 2014 study (e.g., successfully navigating the IRB process) and submitting
sampling plan information, ECAR shared the link to the current year’s survey with
each participating institution. An institutional representative then sent the survey
link to students in the institution’s sample. Data were collected between February 10
and April 11, 2014, and 75,306 students from 213 institutional sites responded to the
survey (see table 1). ECAR issued $50 or $100 Amazon.com gi cards to 39 randomly
selected student respondents who opted in to an opportunity drawing oered as an
incentive to participate in the survey. In exchange for distributing the ECAR-deployed
survey to their undergraduate student population, participating colleges and univer-
sities received les containing anonymous, unitary-level (raw) data of their students’
responses, along with summary tables that compared their students’ aggregated
responses with those of students at similar types of institutions. Participation in this
annual survey is free, and any higher education institution can sign up to contribute
data to this project by e-mailing study@educause.edu.
Table 1. Summary of institutional participation and response rates
Institution Type*
Institution
Count Invitations
Response
Count
Group
Response
Rate
Percentage
of Total
Responses
U.S.
Subsample
(n = 10,000)**
AA 49 332,503 13,899 4% 18% 46%
BA public 8 18,226 1,884 10% 3% 3%
BA private 18 25,126 3,282 13% 4% 3%
MA public 35 188,248 14,645 8% 19% 15%
MA private 22 84,835 7,828 9% 10% 5%
DR public 42 324,969 20,755 6% 28% 24%
DR private 11 42,608 3,337 8% 4% 4%
Total U.S. 185 1,016,515 65,630 6% 87% 100%
Canada 12 62,684 3,198 5% 4% –
Other countries 16 76,674 6,478 8% 9% –
Overall 213 1,155,873 75,306 7% 100% –
*U.S. institutions not falling into the listed types were reclassified.
** Via a stratified random sample
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Countries represented in the non-U.S. sample:
• Canada
• Egypt
• Finland
• France
• Greece
• Hong Kong
• Ireland
• Italy
• Kazakhstan
• Kyrgyzstan
• Lebanon
• Morocco
• South Africa
• United Arab Emirates
e quantitative ndings in this report were developed using a representative sample
of students from 185 U.S.-based higher education college and university sites. A strat-
ied random sample of approximately 10,000 respondents was drawn from the overall
response pool to proportionately match a prole of current U.S. undergraduates. is
sample was based on IPEDS data on age, gender, ethnicity, Carnegie class, and insti-
tutional control (public/private) for U.S. undergraduates. (A similar methodology was
used for the 2013 sample.) e 2014 representative U.S. sample results in an approxi-
mate 1% margin of error for percentages estimated for the whole population. Margins
of error are higher for subsets of the population. e international respondents were
neither sampled nor weighted, but comparison data from Canada and other coun-
tries are included in the report to highlight dierences and similarities between U.S.
and non-U.S. results (see participant listing, appendix A). Findings from past ECAR
studies were also included, where applicable, to characterize longitudinal trends. All
ndings in this report refer to the U.S. representative sample unless otherwise noted.
All ndings are statistically signicant at the 0.001 level unless otherwise noted.
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Table 2. Demographic breakdown of survey respondents
U.S. Full
Sample
U.S.
Subsample Canada
Other
Countries
Basic Demographics
18–24 73% 70% 70% 91%
25+ 27% 30% 30% 9%
Male 36% 45% 37% 52%
Female 64% 55% 63% 48%
White 68% 55% – –
Black 5% 12% – –
Hispanic 10% 16% – –
Asian 8% 8% – –
Other/Multiple 9% 9% – –
Student Profile
Freshman 24% 27% 43% 35%
Sophomore 24% 28% 25% 22%
Junior 22% 20% 15% 19%
Senior 23% 18% 11% 19%
Other 6% 7% 6% 5%
Part time 19% 27% 9% 5%
Full time 81% 73% 91% 95%
On campus 31% 21% 15% 27%
Off campus 69% 79% 85% 73%
Academic Goal
Digital badges that certify my skills 8% 10% 15% 25%
Vocational/occupational certificate 8% 10% 23% 15%
Associate’s degree 17% 30% 14% 9%
Bachelor’s degree 79% 73% 56% 78%
Master’s degree 37% 35% 25% 59%
Doctoral degree 13% 13% 8% 23%
Another professional degree 9% 8% 9% 10%
Other 2% 2% 9% 2%
Major
Agriculture and natural resources 1% 1% 2% 2%
Biological/life sciences 7% 6% 5% 6%
Business, management, marketing 14% 14% 19% 17%
Communications/journalism 4% 3% 2% 2%
Computer and information sciences 6% 8% 6% 8%
Education, including physical education 7% 6% 5% 2%
Engineering and architecture 7% 7% 8% 27%
Fine and performing arts 3% 3% 2% 1%
Cont’d
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U.S. Full
Sample
U.S.
Subsample Canada
Other
Countries
Health sciences, including professional
programs 14% 14% 11% 6%
Humanities 3% 2% 3% 6%
Liberal arts/general studies 3% 4% 2% 1%
Manufacturing, construction, repair, or
transportation 0% 1% 1% 0%
Physical sciences, including mathematical
sciences 3% 3% 2% 4%
Public administration, legal, social, and
protective service 2% 3% 5% 2%
Social sciences 8% 7% 8% 4%
Other 15% 15% 17% 10%
Undecided 3% 3% 2% 1%
Table 2. Demographic breakdown of survey respondents (continued)
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Acknowledgments
is study was made possible by the collective eorts of survey administrators
from the 213 college/university sites that participated in the 2014 student study (see
appendix A). Each representative secured institutional approval to participate in the
study, provided sampling-plan information to our team, and distributed the ECAR
student survey link to their institution’s students. is research is an example of a
symbiotic partnership between ECAR and higher education institutions; it could not
happen without your contributions. ank you for joining us in this exploration of
student views of technology in higher education. is work was supported by the
project’s subject-matter experts. ank you for your insights about what matters most
to higher education with regard to the questions we asked in the survey and the inter-
pretation of the ndings:
• Malcolm Brown, Director, EDUCAUSE Learning Initiative, EDUCAUSE
• Christa Copp, Director of Academic Technology, Loyola Marymount University
• Veronica Diaz, Associate Director, EDUCAUSE Learning Initiative,
EDUCAUSE
• Kyle Dickson, Learning Studio Director, Abilene Christian University
• Charles Dziuban, Director, Research Initiative for Teaching Eectiveness,
University of Central Florida
• Glenda Morgan, E-Learning Strategist, University of Illinois at
Urbana-Champaign
• Craig Stewart, Associate Dean for Research Technologies, Indiana
University–Bloomington
• Kristen Vogt, Knowledge Management Ocer, NGLC, EDUCAUSE
• J. D. Walker, Research Associate, University of Minnesota
e authors are grateful to the myriad people on the EDUCAUSE sta who work
behind the scenes to produce ECAR research. We thank Susan Grajek for her lead-
ership; our data team (Pam Arroway, Mike Roedema, and Ben Schulman) for their
statistical support and guidance; Jamie Reeves for logistical support and research
assistance; D. Christopher Brooks, Ron Yanosky, and Joanna Grama for peer-reviewed
contributions to the work; Kate Roesch for producing all of the report graphics and
providing guidance about visualizing the data collected in this project; Gregory
Dobbin and the publications team for their assistance in preparing this report for
publication; and Lisa Gesner and Ashlan Sar for their marketing and communica-
tion support to achieve a polished, public-facing image and messaging campaign for
this work.
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Appendix A: Participating Institutions
Aalto University
Abilene Christian University
Al Akhawayn University
Alma College
e American College of Greece–Deree
College
e American University in Cairo
American University of Central Asia
e American University of Paris
American University of Rome
American University of Sharjah
Appalachian State University
Auburn University
Baldwin Wallace University
Ball State University
Bellevue University
Blue Ridge Community College
Brandman University
Brazosport College
Bridgewater State University
Brown University
Bucks County Community College
Butler University
California Lutheran University
California State Polytechnic University,
Pomona
California State University, Dominguez
Hills
California State University, Fresno
California State University, Northridge
California State University, Sacramento
Canadian University College
Castleton State College
Catawba College
Cecil College
Central Connecticut State University
Central Virginia Community College
Chadron State College
Chandler-Gilbert Community College
Chatham University
Clemson University
College of the Desert
College of Saint Benedict/Saint John’s
University
College of Wooster
Collin County Community College District
Community College of Vermont
Concordia University Texas
Confederation College
Coppin State University
Cornell University
Dabney S. Lancaster Community College
Danville Community College
DeVry University
Drexel University
Dublin City University
Durham College
Eastern Illinois University
Eastern Kentucky University
Eastern Shore Community College
Elon University
Emory University
Estrella Mountain Community College
Fleming College
Fordham University
Franklin W. Olin College of Engineering
Fullerton College
GateWay Community College
Geneva College
Georgetown College
Georgia College & State University
Georgia Gwinnett College
Georgia Southern University
Germanna Community College
Glendale Community College
Grace College and Seminary
Grand Canyon University
Hamilton College
Harvey Mudd College
Hawaii Pacic University
Hollins University
Humber College Institute of Technology &
Advanced Learning
Hunter College/CUNY
Cont’d
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Illinois Central College
Indiana University Bloomington
Indiana University-Purdue University
Indianapolis
Ithaca College
J. Sargeant Reynolds Community College
John Tyler Community College
Johnson State College
Joliet Junior College
Juniata College
Keene State College
Kent State University
Lambton College of Applied Arts &
Technology
Lawrence Technological University
Lebanese American University
Lethbridge College
LeTourneau University
Lipscomb University
Lord Fairfax Community College
Louisiana State University
Lourdes University
Loyalist College
Loyola Marymount University
Lyndon State College
Marietta College
McGill University
Mesa Community College
Messiah College
Michigan State University
Montgomery County Community College
Moreno Valley College
Mountain Empire Community College
Nazarbayev University
New Jersey Institute of Technology
New River Community College
Northern College
Northern Virginia Community College
Northwestern University
Oakland University
e Ohio State University
Old Dominion University
Oregon State University
Palo Alto College
Paradise Valley Community College
Patrick Henry Community College
Paul D. Camp Community College
e Pennsylvania State University
Philadelphia University
Phoenix College
Piedmont Virginia Community College
Pima County Community College District
Pitzer College
Purdue University
Rappahannock Community College
Rio Salado College
Saint Mary’s University
Saint Michael’s College
Salt Lake Community College
Salve Regina University
San Jose State University
Sauk Valley Community College
School of the Art Institute of Chicago
Scottsdale Community College
Seneca College of Applied Arts and
Technology
Seton Hall University
Sonoma State University
South Dakota State University
South Mountain Community College
Southern Methodist University
Southern New Hampshire University
Southside Virginia Community College
Southwest Virginia Community College
St. Norbert College
Tampere University of Technology
Tarleton State University
omas College
omas Nelson Community College
Tidewater Community College
Truman State University
Tus University
University College Dublin
University of Alaska Anchorage
University of Alaska Kenai
University of Alaska Kodiak
University of Alaska Mat-Su
e University of Arizona
University of Arkansas
University of Cape Town
Cont’d
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University of Cincinnati
University of Delaware
University of Florida
University of Hong Kong
University of Houston
University of La Verne
University of Maryland
University of Massachusetts Dartmouth
e University of Memphis
University of Michigan–Ann Arbor
University of Minnesota
University of Minnesota–Crookston
University of Minnesota–Duluth
University of Minnesota–Morris
University of Minnesota–Rochester
University of Mississippi
University of Montana
University of Nebraska at Kearney
University of Nebraska at Omaha
University of Nevada, Las Vegas
University of New Hampshire
University of New Mexico
University of North Dakota
University of North Texas at Dallas
University of Northern Iowa
University of Oregon
University of Pretoria
University of South Carolina Upstate
e University of South Dakota
University of Texas–Pan American
University of Washington
University of West Georgia
University of Wisconsin–Madison
University of Wisconsin–Milwaukee
University of Wisconsin–Superior
University of Wisconsin–Whitewater
Vermont Technical College
Virginia Commonwealth University
Virginia Highlands Community College
Virginia Western Community College
Washington University in St. Louis
Wayne State College
Wayne State University
West Virginia University
Western Carolina University
Winona State University
Wytheville Community College
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Appendix B: Validity and Reliability of Semantic
Differential Constructs
We asked respondents to place themselves on a series of semantic dierential scales
designed to measure their disposition toward technology, their attitudes toward
technology, and their usage of technology. On a 100-point slider scale, lower numbers
indicated certain characteristics about disposition, about attitudes, and about usage.
In contrast, higher numbers on the scale indicated opposite characteristics for dispo-
sition (enthusiast, early adopter, technophile, cheerleader, experimenter, supporter,
radical), for attitude (satised, content, pleased, benecial, useful, enhancement), and
for usage (always connected, central, new media, frequent, insatiable).
A principal components analysis (using varimax rotation and Kaiser normalization)
on the 18 slider-scale items revealed three primary factors that reected the precon-
ceived factors of disposition, attitude, and usage. ese three factors accounted for
64% of the variance in the semantic dierential responses. Cronbach’s alphas (reli-
ability) for each factor were .85 (disposition), .86 (usage), and .91 (attitude), indi-
cating these constructs have sucient reliability.
In terms of disposition, students were on average signicantly more positive than
negative on every scale in this series. ey were more likely to refer to themselves as
IT enthusiasts, early adopters, technophiles, cheerleaders, experimenters, supporters,
and radicals (see gure B1).
Conservative Radical
Cheerleader
Early adopter
Experimenter
Technophile
Supporter
Enthusiast
Skeptic
Late adopter
By-the-book
Technophobe
Critic
Reluctant
Mean score: 55
62
64
61
67
65
73
Figure B1. Student disposition toward technology
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Students also had signicantly more positive than negative attitudes toward IT,
claiming to be more satised, content, and pleased than dissatised, discontent, and
perturbed. Furthermore, they were much more likely to see IT as benecial, useful,
and an enhancement than as burdensome, useless, and a distraction (see gure B2).
Discontent Content
Pleased
Satisfied
Enhancement
Beneficial
Useful
Perturbed
Dissatisfied
Distraction
Burdensome
Useless
69
70
68
73
78
Mean score: 69
Figure B2. Student attitudes toward technology
In terms of usage, students reported on average being more connected than not, using
technology frequently and voraciously, and tending to have technology and new
media central to their lives (see gure B3).
Satiable Insatiable
New media
Central
Frequent
Always connected
Old media
Peripheral
Infrequent
Never connected
77
70
67
76
Mean score: 59
Figure B3. Student usage of technology
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EDUCAUSE CENTER FOR ANALYSIS AND RESEARCH
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e histogram below (gure B4) shows the normal distribution curve for overall
technology inclination scores, which were calculated as the mean of each student’s
disposition, attitude, and usage score. ere are large individual dierences in tech
inclination, with 95% of scores falling between 39 and 97.
1%
2%
3%
4%
5%
6%
0100
50
mean: 68
(SD: 15)
Percentage of respondents
Tech-inclination score
Figure B4. Histogram of semantic distribution mean scores for “tech inclination”
Additional details about this statistical analysis are available upon request through
study@educause.edu.
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Notes
1. A stratied random sample of 10,000 respondents was drawn from the overall response pool
of U.S. respondents to proportionately match a prole of current U.S. undergraduates (based
on IPEDS demographics and institutional data). See the Methodology section for more about
the sampling process and institution details.
2. Students ages 25-plus rated themselves an average of 71 on the tech-inclination scale, which
was higher than students younger than 25 years of age (67 on the scale). is is a signicant
(p < 0.001) dierence.
3. Eden Dahlstrom and D. Christopher Brooks, with a foreword by Diana Oblinger, ECAR
Study of Faculty and Information Technology, 2014, research report (Louisville, CO: ECAR,
July 2014), available from http://www.educause.edu/ecar.
4. e 2014 survey instrument updated the survey question language from “professors” (2011–
2013) to “instructors” (2014), and the scale changed from “Neither agree nor disagree” to
“Neutral” and from “Agree” to “Somewhat agree” during this same period. “Don’t know” was
added in 2013. Because these items are linguistic synonyms, we don’t expect these changes to
have a substantial eect on the longitudinal analysis; we are noting the changes nevertheless.
5. Eden Dahlstrom, D. Christopher Brooks, and Jacqueline Bichsel, e Current Ecosystem of
Learning Management Systems in Higher Education: Student, Faculty, and IT Perspectives,
research report (Louisville, CO: ECAR, September 28, 2014), available from http://www
.educause.edu/ecar.
6. Kristen Purcell et al., How Teens Do Research in the Digital World, Pew Research Internet
Project, November 1, 2012.
7. Pew Research Internet Project: Ownership estimates for cell phones, smartphones, tablets,
and e-readers (as of January 2014) and for laptops (as of April 2012).
8. More than twice as many younger students (9%) than older students (4%) try to connect
three or more devices to the college/university network at the same time.
9. “7 Things You Should Know About the Internet of Things,” EDUCAUSE Learning Initiative,
October 6, 2014.
10. Gartner’s 2014 Hype Cycle for Emerging Technologies places the Internet of ings at the
apex of the “inated expectations” curve and wearable user interfaces just past the apex.
11. Because of the nearly universal ownership and use of laptops, we don’t see much dierence in
use between students with high versus low tech-inclination ratings. However, the dierences
are particularly noticeable for smartphones and tablets, with greater than 20 percentage point
spreads in “use for academics” between high versus low tech inclination.
12. e percentage reporting very/extremely important to academic success is among students
reporting academic usage (rather than all survey respondents).
13. Although the percentages of students and faculty reporting bans and requirements on
in-class use of digital devices are more or less consistent with each other, students see faculty
as more discouraging and less encouraging than faculty see themselves. is is particularly
true for laptops, tablets, and smartphones. Wearables dier, but these devices may be too
novel to academic use to get a good read on experiences and expectations.
14. Anne Curzan, “Why I’m Asking You Not to Use Laptops,” Chronicle of Higher Education,
August 25, 2014.
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15. Bernard R. McCoy, “Digital Distractions in the Classroom: Student Classroom Use of Digital
Devices for Non-Class Related Purposes,” Journal of Media Education 4, no. 4 (2013).
16. Pam A. Mueller and Daniel M. Oppenheimer, “The Pen Is Mightier than the Keyboard:
Advantages of Longhand Over Laptop Note Taking,” Psychological Science 25, no. 6 (January
16, 2014), 1159–1168.
17. D. C. Rubin and W. T. Wallace, “Rhyme and Reason: Analyses of Dual Retrieval Cues,”
Journal of Experimental Psychology: Learning, Memory, and Cognition 15, no. 4 (1989):
698–709; Allan Paivio, Mental Representations: A Dual Coding Approach (Oxford, England:
Oxford University Press, 1986).
18. Ezekiel J. Emanuel, “Who Takes MOOCs? For Online Higher Education, the Devil Is In the
Data,” New Republic, January 4, 2014; Jozenia Torres Colorado and Jane Eberle, “Student
Demographics and Success in Online Learning Environments,” Emporia State Research
Studies 46, no. 1 (2010), 4–41.
19. Core Data Service, 2013, Module 8.
20. Jamie S. Switzer and Ralph W. Switzer, “The Myth of the Tech-Savvy Student: The Role of
Media Educators in a Web 2.0 World,” Journal of Media Education 4, no. 4 (October 2013):
15–27.
21. Dahlstrom, Brooks, and Bichsel, e Current Ecosystem of Learning.
22. For more information on integrated planning and advising services, see Ronald Yanosky,
Integrated Planning and Advising Services: A Benchmarking Study, research report (Louisville,
CO: ECAR, March 2014); and D. Christopher Brooks, IPAS Implementation Issues: Data and
Systems Integration, research report (Louisville, CO: ECAR, June 2014); both available from
the ECAR IPAS Research Hub.
23. Clarifying survey language from “No” to “No, but I do know what a MOOC is” could
account for some of the decrease in the percentage of students knowing what a MOOC is but
not having taken one in the last year.
24. See the EDUCAUSE Badging Program.
25. Stephen diFilipo, “Connecting the Dots to the Future of Technology in Higher Education,”
EDUCAUSE Review 46, no. 4 (July/August 2011).
