Assessing Laptop Use in Higher Education: The Laptop Use Scale

Abstract

The laptop computer is considered one of the most used and important technological devices in higher education, yet limited systematic research has been conducted to develop a measure of laptop use in college and university. The purpose of the following study was to develop a research-based, theoretically grounded scale to assess student use of laptops inside and outside higher education classrooms. The Laptop Use Scale addressed four key areas: in-class academic use, in-class non-academic use, outside of class academic use, and outside of class non-academic use. Tested on 156 higher education students using laptops computers, the Laptop Use Scale showed acceptable internal reliability and good validity (face, content, construct, and convergent validity). It is argued that this scale can help assess and calibrate pedagogical strategies used to integrate laptops into higher education classrooms. Suggestions for future research on assessing student use of laptops are offered including a focus on multi-tasking behavior.

Full text

Assessing laptop use in higher education: The Laptop
Use Scale
Robin Kay
1
•Sharon Lauricella
2
Published online: 30 December 2015
Springer Science+Business Media New York 2015
Abstract The laptop computer is considered one of the most used and important
technological devices in higher education, yet limited systematic research has been
conducted to develop a measure of laptop use in college and university. The purpose
of the following study was to develop a research-based, theoretically grounded scale
to assess student use of laptops inside and outside higher education classrooms. The
Laptop Use Scale addressed four key areas: in-class academic use, in-class non-
academic use, outside of class academic use, and outside of class non-academic use.
Tested on 156 higher education students using laptops computers, the Laptop Use
Scale showed acceptable internal reliability and good validity (face, content, con-
struct, and convergent validity). It is argued that this scale can help assess and
calibrate pedagogical strategies used to integrate laptops into higher education
classrooms. Suggestions for future research on assessing student use of laptops are
offered including a focus on multi-tasking behavior.
Keywords Evaluate Assess Use Scale Higher education University Laptop
&Robin Kay
robin.kay@uoit.ca
Sharon Lauricella
sharon.lauricella@uoit.ca
1
Faculty of Education, University of Ontario Institute of Technology, 11 Simcoe St N,
PO Box 385, Oshawa, ON L1H 7L7, Canada
2
Faculty of Social Science and Humanities, University of Ontario Institute of Technology, 55
Bond Street East, Bordessa Hall, Oshawa, ON L1H 7L7, Canada
123
J Comput High Educ (2016) 28:18–44
DOI 10.1007/s12528-015-9106-5

Overview
According to the most recent study by the Educause Center for Analysis and
Research (ECAR) on the use of information technology by over 100,000
undergraduate students from 14 countries, laptop computers (hereafter referred to
as laptops) are cited as the most used and most important device for academic
purposes (Dahlstrom et al. 2013). However, research on the efficacy of using laptop
computers in higher education classrooms has produced mixed results. On the one
hand, a number of studies have identified clear benefits to using laptops during class
such as keeping students on-task and engaged (Hyden 2005), increased capability
for following lectures via PowerPoint or multimedia (Debevec et al. 2006), note
taking, academic use of software, collaboration among students, and improved
organization (Lauricella and Kay 2010). On the other hand, researchers have
observed non-academic, off-task use of laptops during class including surfing the
web for personal reasons, sending instant messages and emails to friends, playing
games, and watching movies (Barak et al. 2006; Barkhuus 2005; Lauricella and Kay
2010).
There are numerous factors that can influence the use and effectiveness of laptop
use in the classroom including teaching and learning strategies (for example Awwad
and Ayesh 2013; Dalsgaard and Godsk 2007; Enfield 2014; Kay and Lauricella
2011), classroom management (e.g. Aguilar-Roca et al. 2012; McCreary 2009),
student motivation, cognitive engagement (for example Barak et al. 2006; Fried
2008; Skolnik and Puzo 2008) and the nature of the course content (for example
Kay and Lauricella 2014). Regardless of the influential factor, though, it is critical to
have a theoretically grounded metric sensitive enough to assess a sufficiently wide
range of potential benefits and challenges of laptop use inside and outside of the
classroom. Without a scale that can provide reliable and valid feedback on laptop
use, it is very difficult to assess the relative import of variables such as pedagogy,
student characteristics, subject area, and engagement. It is also challenging to build
a solid, evidence-based understanding of laptop use to guide educators without a
consistent, comprehensive scale to compare results. Confusion and contradictions
over the impact of laptops reported in previous studies is partially confounded by
limitations in the metrics used to assess student use of laptops in higher education
classrooms (Lauricella and Kay 2010; Kay and Lauricella 2011). Some key problem
areas include scales that focus on general, non-specific behaviors (for example
Awwad and Ayesh 2013; Barak et al. 2006; Kraushaar and Novak 2010), a bias
toward focusing on distractions or negative behaviors (for example Barak et al.
2006; Fried 2008), limited reliability and validity (for example DiGangi et al. 2007;
Lindroth and Bergquist 2010; Wurst et al. 2008), and the absence of a guiding
framework. The purpose of the current paper, then, is to develop a comprehensive,
research-based measure of student use of laptops, grounded in instructional theory.
Assessing laptop use in higher education: The Laptop Use…19
123

Assessing laptop use in higher education: key parameters
Key parameters used to assess laptop use in higher education can be organized into
two main categories: behaviors that help students learn (academic use) and
behaviors that distract students from the learning process (non-academic use). Each
of these categories will be discussed in turn.
Academic use
A review of the research from 2006 to 2014 uncovered nine peer-reviewed articles
on assessing the academic use of laptops in higher education classrooms. Six
academic uses of laptops were identified including note taking, using the web to
research concepts and ideas when required, communicating with peers about
concepts presented in class, organizing files or information, using software, and
engaging with web-based interactive tools such as online surveys, case-studies or
instructional podcasts. These academic uses are summarized in Table 1with the
associated research studies. Note-taking (n =8) and web-based research (n =7)
were the laptop activities assessed most often, whereas as using software (n =2)
and web-based interactive tools were examined least often.
Non-academic use
A review of the literature from 2006 to 2014 uncovered nine articles examining non-
academic student use of laptops observed in higher education classrooms. Six non-
academic behaviors were noted: sending emails, surfing the web, instant messaging,
playing games, watching videos, and social networking. These non-academic
activities are displayed in Table 1with their respective research studies. The most
frequent non-academic activities observed were sending emails (n =9), surfing the
web (n =7) and instant messaging (n =6). The least frequent non-academic use of
laptops studied was social networking (n =2).
Methodological issues
A thorough examination of ten previous metrics (see Tables 1,2) used to examine
student use of laptops in higher education revealed at least six limitations including
a non-specific or general focus, a narrow range of behaviors assessed, an imbalance
in the number of academic and non-academic behaviors, the absence of reliability
and validity estimates, not examining student use of laptops outside of class, and
testing scales only in traditional, lecture-style classrooms. Each of these limitations
will be discussed in turn.
Non-specific or general focus
A number of studies used metrics that had a general focus (Awwad and Ayesh 2013;
Barak et al. 2006; Kraushaar and Novak 2010; Skolnik and Puzo 2008). Instead of
asking about a range of specific activities, a single, non-specific question about
20 R. Kay, S. Lauricella
123

academic or non-academic use was listed, with examples provided in brackets (for
example Barak et al. 2006; Kraushaar and Novak 2010; Skolnik and Puzo 2008).
Asking non-specific questions makes it difficult to assess the relative frequencies of
specific behaviors and how these behaviors might link to pedagogical practices of
the instructor. Lack of specificity also creates confusion when comparing studies
because different authors list different examples when referring to academic or non-
academic use of laptops.
Narrow range of behaviors assessed
As a whole, the ten studies assessing the use of laptops in higher education
identified a wide variety of behaviors. However, individually, a number of studies
Table 1 Research summary of academic student use of laptops exhibited during class (n =9 studies)
Student use of
laptops examined
n
a
Research Theoretical
perspective
Note-taking 8 Annan-Coultas (2012), Awwad and Ayesh (2013),
DiGangi et al. (2007), Gaudreau et al. (2014),
Lauricella and Kay (2010), Lindroth and Bergquist
(2010), McCreary (2009) and Skolnik and Puzo (2008)
Associative
(Gagne
´1985;
Wilson and Myers
2000)
Web-based
research
7 Annan-Coultas (2012), Awwad and Ayesh (2013),
Gaudreau et al. (2014), Kay and Lauricella (2011),
Lindroth and Bergquist (2010), McCreary (2009) and
Skolnik and Puzo (2008)
Constructive
(Individual)
(Bruner 1960;
Newell 1980;
Piaget 1970)
Communication 5 Annan-Coultas (2012), Lauricella and Kay (2010),
Kraushaar and Novak (2010), Lindroth and Bergquist
(2010) and McCreary (2009)
Constructive
(Social)
(Laurillard 2002;
Vygotsky 1978)
Organizing 4 Annan-Coultas (2012), Annan-Coultas (2012), Kay and
Lauricella (2011) and McCreary (2009)
Constructive
(Individual)
(Bruner 1960;
Newell 1980;
Piaget 1970)
Use software 2 Kraushaar and Novak (2010) and Skolnik and Puzo
(2008)
Constructive
(Individual)
(Bruner 1960;
Newell 1980;
Piaget 1970)
Web-based
interactive tools
1 Lauricella and Kay (2010) Constructive
(Individual)
(Bruner 1960;
Newell 1980;
Piaget 1970)
Situative
(Lave and Wenger
1991)
a
Number of studies to examine this student use of laptops
Assessing laptop use in higher education: The Laptop Use…21
123

focused on a narrow range of behaviors. For example, when targeting academic use
of laptops, Kraushaar and Novak (2010) noted MS-Office and browsing skills,
Annan-Coultas (2012) employed taking PowerPoint notes and ‘‘googling’’ concepts,
and Fried (2008) listed note taking exclusively. All of these criteria are reasonable,
but would arguably work better at building overall understanding of student use of
laptops if they were combined.
Academic versus non-academic imbalance
In a majority of studies that assessed the use of laptops, there is an imbalance
between academic and non-academic use of laptops in favor of the latter category
(Awwad and Ayesh 2013; Barak et al. 2006; Fried 2008; Gaudreau et al. 2014;
Kraushaar and Novak 2010; Lauricella and Kay 2010). It is common practice to list
one or two academic behaviors (for example note taking, browsing the web) and
Table 2 Research summary of non-academic student use of laptops exhibited during class (n =9
studies)
Student use of
laptops
examined
n
a
Research Theoretical underpinnings
Personal emails 9 Annan-Coultas (2012), Awwad and Ayesh (2013),
Barak et al. (2006), Fried (2008), Gaudreau et al.
(2014), Kraushaar and Novak (2010), Kay and
Lauricella (2011), McCreary (2009) and Skolnik
and Puzo (2008)
Collaboration
(Prensky 2010; Small and
Vorgan 2009; Tapscott
2009)
Surfing the web 7 Barak et al. (2006), Fried (2008), Gaudreau et al.
(2014), Kraushaar and Novak (2010), Lauricella
and Kay (2010); McCreary (2009) and Skolnik
and Puzo (2008)
Freedom
(Prensky 2010; Small and
Vorgan 2009; Tapscott
2009)
Instant
messaging
6 Annan-Coultas (2012), Fried (2008), Awwad and
Ayesh (2013), Kraushaar and Novak (2010),
Lauricella and Kay (2010) and McCreary (2009)
Collaboration
(Prensky 2010; Small and
Vorgan 2009; Tapscott
2009)
Playing games 5 Barak et al. (2006), Fried (2008), Kraushaar and
Novak (2010), Lauricella and Kay (2010),
McCreary (2009) and Skolnik and Puzo (2008)
Entertainment
(Prensky 2010; Small and
Vorgan 2009; Tapscott
2009)
Watching web-
based media
4 Awwad and Ayesh (2013), Barak et al. (2006),
Gaudreau et al. (2014) and Lauricella and Kay
(2010)
Entertainment
(Prensky 2010; Small and
Vorgan 2009; Tapscott
2009)
Social
networking
2 Annan-Coultas (2012) and Kraushaar and Novak
(2010)
Collaboration
(Prensky 2010; Small and
Vorgan 2009; Tapscott
2009)
a
Number of studies to examine this student use of laptops
22 R. Kay, S. Lauricella
123

three to six non-academic behaviors (for example email, instant messaging,
browsing the web, playing games, watching videos). While it is possible that more
non-academic behaviors are pursued by students, the built-in imbalance in the
presentation of possible options may bias the results and conclusions. A balanced
number of academic and non-academic behaviors permits a more balanced analysis.
Limited reliability and validity
Out of the ten studies that measured student use of laptops in higher education
classrooms, only two provided estimates of reliability (Awwad and Ayesh 2013;
Lauricella and Kay 2010) and one examined validity (Lauricella and Kay 2010).
While critical information can be gleaned from preliminary Likert questions, case
studies, and open-ended questions (for example DiGangi et al. 2007; Lindroth and
Bergquist 2010; Wurst et al. 2008), reliability and validity of measures helps to
increase confidence in assessment tools and the possibility for extending the results
to a larger population.
Missing outside class behaviors
No peer-reviewed studies have examined student use of laptops outside the
classroom. While it may seem more critical to determine the impact of laptops in the
classroom, understanding how laptops are used outside the classroom is also
important, particularly with respect to distractions and potential barriers to students
completing academic work. Furthermore, there may be a connection between how a
student behaves in class and how they behave outside of class.
Limited context of testing scales
Eight out of the ten studies evaluating laptop use in higher education have been
conducted in classrooms where a traditional lecture-style teaching approach is used
(Awwad and Ayesh 2013; Barak et al. 2006; DiGangi et al. 2007; Fried 2008;
Gaudreau et al. 2014; Kraushaar and Novak 2010; McCreary 2009; Skolnik and
Puzo 2008). This kind of restricted context may bias the results and account for the
more limited and general parameters used to assess in class academic behaviors.
Some might argue that a lecture format is representative of a majority of higher
education classrooms, however, a number of alternative approaches are being used
(for example Dalsgaard and Godsk 2007; Enfield 2014; Kay and Lauricella 2011;
Kolb and Kolb 2005; Lasry et al. 2008; Lewis and Lewis 2005). A comprehensive
scale for assessing laptop use in higher education classrooms needs to extend further
than lecture-driven activities, such as note taking or browsing the web, in order to
capture the full range of possible behaviors experienced.
Guiding framework for academic and non-academic behaviors
Previous research on evaluating laptop use in higher education has yet to define a
guiding framework or set of principles to organize behaviors displayed. As stated
Assessing laptop use in higher education: The Laptop Use…23
123

earlier, two general categories of behaviors have emerged: academic and non-
academic behaviors. Potential theoretical underpinnings of each of these categories
will be discussed.
Academic behaviors
Mayes and de Freitas (2007) reviewed four learning theories that support pedagogy
for a digital age including associative, constructive-individual, constructive-social,
and situative. Associative learning (Gagne
´1985; Wilson and Myers 2000), or
building understanding and competencies step-by step, is closely aligned with the
lecture and note-taking use of laptops observed in previous studies (Table 1).
Constructive learning by an individual (Bruner 1960; Newell 1980; Piaget 1970), or
achieving personal understanding through active, independent discovery is indica-
tive of laptop use such as web searches by students for concepts, organization of
digital materials, and using software or web-based interactive tools to explore new
ideas (Table 1). Social construction of knowledge (Laurillard 2002; Vygotsky
1978), or acquiring understanding through dialogue and collaboration, is repre-
sented by the use of laptop communication tools with peers (Table 1). Finally,
situated learning (Lave and Wenger 1991), or developing practice in a particular
community with authentic tasks is aligned with the use of specific, real-world web-
based learning tools (Table 1). These four theories provide a framework that
supports the full range of academic-based student use of laptops reported in the
literature and included in the assessment tool developed in the current study.
Non-academic behaviors
Small and Vorgan (2009) suggested that the brains of digital natives or the net
generation (Tapscott 2009) are evolving differently from those of previous
generations of students. Specifically, this new breed of student has a shorter
attention span when faced with more traditional forms of teaching. In addition,
students are in a state of ‘‘contiguous partial attention’’ (Tapscott 2009, p. 18)—they
keep tabs on everything and never truly focus on anything. Prensky (2010) has also
noted unique characteristics of the digital generation including twitch speed,
multitasking, random-access, graphics-first, connectedness, and a desire to have fun.
Tapscott (2009) took the analysis of the Net Generation one step further when he
and his colleagues studied almost 10,000 individuals born between 1977 and 1997
from 12 countries. The results revealed a set of key behaviors indigenous to the Net
Generation including freedom to do whatever they want, whenever they want, a
propensity for collaboration, and a desire for entertainment in work and play. These
characteristics line up well with those identified in previous studies assessing laptop
use (Table 2). Surfing the web for a wide range of personal needs and information is
representative of the net generation’s expectation of freedom without contextual
boundaries. Sending personal emails, instant messaging, and social networking are
consistent with the Net Generation’s desire to keep in contact and to collaborate.
Playing games and watching video-based materials is aligned well with the ‘‘Net
Generation’s’’ proclivity for being entertained. Tapscott (2009) study provides a
24 R. Kay, S. Lauricella
123

lens in which to organize non-academic use of laptops observed previously and
employed in the scale developed for the current study.
Purpose
The purpose of this study was to develop and test a comprehensive, research-based
instrument for assessing student use of laptops inside and outside of higher
education classrooms.
Method
Participants
One hundred fifty-six university students (54 males, 102 females) in their first
(n =40), second (n =63), third (n =3) or fourth year (n =50) of university
participated in the study. They were enrolled in either communication (n =107 out
of a possible 120 students, 89 % response rate) or teacher education (n =49 out of
a possible 58 students, 84 % response rate) courses while using their laptops.
The average self-reported grade for the course in which they used the laptop was
81.4 % (SD =6.3, range 45–90). A majority of students reported that they were
either very interested (n =62, 40 %) or interested (n =75, 48 %) in the course
they were taking when using their laptop. Over 90 % of the students reported being
very comfortable (n =93, 60 %) or comfortable (n =52, 33 %) with using
computer technology. All students leased an IBM laptop from the university and
had wireless access to the web throughout the campus.
Teaching context
The scale was tested in communication and teacher education university classes
where the four primary learning frameworks, outlined by Mayes and de Freitas
(2007) were used (see Table 1). First, an associative approach was followed when
the primary learning goal was to present and discuss key concepts, with a step-by-
step, traditional PowerPoint lecture with discussion. Second, a constructive
(individual) approach was employed when students were achieving understanding
through active, independent discovery in the form of web-based searches for
concepts, reviewing published articles, participating in online voting and surveys,
working through interactive web-based learning objects, creating presentations and
learning materials with tool-based software, and engaging with subject-specific
software. Third, social construction of knowledge was pursued with team activities
such as online case studies, concept-map creation, assessment of video podcasts,
and sharing and coordinating ideas with discussion boards. Finally, situative
learning was used by deliberate efforts to weave real-world examples, problems,
and situations into the associative and constructive-based activities listed above.
Assessing laptop use in higher education: The Laptop Use…25
123

Instrument development
Three steps were followed to develop and select items for the Laptop Use Scale in
this study. First, the results from Lauricella and Kay’s (2010) original study were
carefully reviewed and used to ultimately expand the range of content assessed for
academic and non-academic use of laptops. Based on student qualitative feedback,
several items were added to the academic use construct including expanded forms of
note taking, interacting with the web in a variety of ways, communicating with
peers, and using subject-specific software. Regarding non-academic use of laptops,
more current technology-based activities were added including watching video
podcasts and using Facebook.
Second, after creating a list of new items based on student feedback (Lauricella
and Kay 2010), items were carefully checked by two experts in the field of
education and technology who taught in laptop-based classrooms. The experts
agreed that the items suggested by students were reasonable and representative of
student interaction with laptops. The experts added the use of Twitter because they
perceived this new activity as one that students in Lauricella and Kay’s (2010) study
may not have used. A similar process was followed to select items for academic and
non-academic use of laptops outside of the classroom. The non-academic behaviors
largely mirrored those items selected for the in-class scale. However, based on
student comments from Lauricella and Kay’s (2010) study and the analysis of the
two experts, academic use was expanded to include organizing notes, sharing notes,
and collaboration with peers.
Finally, an extensive review of laptop scales used in the previous studies was
conducted (see Table 1) to determine the full range of possible items that could be
included in the Laptop Use Scale. These items were merged with those identified in
steps one and two above to create the final version of the Laptop Use Scale
(‘‘Appendix’’).
Procedure
In the final class of the semester, students were invited to participate in an
anonymous, online survey. Participation was voluntary, and the instructor, who left
the class while the survey was being completed, was unable to determine who chose
to participate. As a further precaution, the data was not accessed until all marks for
the courses were submitted. The survey took 10–15 min to complete.
Data collected
Descriptive variables
Students were asked their gender, year of study, average grade in the previous year
of study, course taken while using their laptop computer, estimated average grade in
the course in which they were enrolled, comfort level with computer technology and
how many hours per day they used their laptop computer (‘‘Background
information’’ section—Items 1 to 7 in Appendix).
26 R. Kay, S. Lauricella
123

Laptop Use Scale
The Laptop Use Scale focused on four key areas of use: academic use of laptops
inside the classroom, non-academic use of laptops inside the classroom, academic
use of laptops outside the classroom, non-academic use of laptops outside the
classroom. Academic use of laptops inside the classroom (‘‘Academic use DURING
class’’ section—8 items in Appendix) focused on note taking, searching the web,
interactive tools, and communication with peers. Non-academic use of laptops of
laptops inside the classroom (‘‘Non-academic use DURING class’’ section—6 items
in Appendix) looked at game playing, watching podcasts, and communication with
social media and email. Academic use of laptops outside the classroom (‘‘Academic
OUTSIDE if class’’ section—9 items in Appendix) focused on organizational tasks,
searching the web, production tools, sharing resources and collaborating with peers.
Finally, non-academic use of laptops outside of the classroom (‘‘Academic
OUTSIDE if class’’ section C—7 items in Appendix) included game playing,
watching videos, communication with social media, and email. All questions used a
five-point Likert scale with the following options: never, rarely, sometimes,
frequently, or very frequently. Descriptive statistics for the Laptop Use Scale are
presented in Table 3.
Student comments
Students were asked four open-ended questions about academic and non-academic
use of laptops inside and outside of the classroom:
1. Overall what are the biggest benefits to having a laptop IN class for this course?
Why?
2. Overall what are the biggest distractions in having a laptop IN class for this
course? Why?
3. Overall what are the biggest benefits to having a laptop OUTSIDE class? Why?
4. Overall what are the biggest distractions to having a laptop OUTSIDE class?
Why?
Table 3 Description of Laptop Use Scale
Scale No. items Possible range Internal reliability
LES
In-class (academic use) 8 8–40 r=0.80
In-class (non-academic use) 6 6–30 r=0.87
Outside class (academic use) 8 8–40 r=0.85
Outside class (non-academic use) 7 7–35 r=0.78
Assessing laptop use in higher education: The Laptop Use…27
123

Data analysis
A series of analyses and procedures were conducted to assess the reliability and
validity of the Laptop Use Scale. These included conducting:
1. internal reliability estimates for the Laptop Use Scale constructs (reliability);
2. student and expert analysis of items (face validity);
3. frequency of laptop use assessed by the Laptop Use Scale constructs (content
validity);
4. student comments (content validity);
5. a principal component factor analysis for the Laptop Use Scale (construct
validity);
6. correlations among constructs within the Laptop Use Scale (construct validity);
and
7. correlations among Laptop Use Scale constructs and descriptive variables—
self-reported average grade (current course and previous year), interest in
course, year of study, hours per day on laptop (convergent validity).
It should be noted parametric statistics were used to assess the Likert data
obtained from the Laptop Use Scale. Some researchers have suggested that non-
parametric statistics are required because Likert data is ordinal, not continuous
(Jamieson 2004; Kuzon et al. 1996). However, Norman (2010) after a detailed
analysis of arguments for non-parametric tests, argues that parametric tests are valid
for Likert data. Norman’s (2010) conclusions are supported by a number of other
statistical analysts (Carfio and Perla 2008; Murray 2013).
Student comments were categorized using an inductive approach outlined by
Miles and Hubrman (1994) and based on a scoring scheme developed by Lauricella
and Kay (2010). Once the categories were identified, inter-rater reliability estimates
from 96 to 98 % were achieved.
Results
Internal reliability
The internal reliability estimates for the Laptop Use Scale constructs based on
Cronbach’s awere 0.80 (in-class academic use of laptops), 0.87 (in-class non
academic use of laptops), 0.87 (outside class—academic use of laptops), and 0.77
(outside class—non-academic Use of laptops) (Table 3). According to Kline (1999)
and Nunnally (1978), these moderate to high values are considered acceptable in-
ternal reliability levels for measures in the social sciences.
Face validity
Face validity for the Laptop Use Scale was established by comparing, contrasting
and adding items based on the results and feedback from a previous scale assessing
28 R. Kay, S. Lauricella
123

laptop- related behaviours in higher education settings (Lauricella and Kay 2010).
Next, two experts in the field of education and technology agreed that the items
proposed were reasonable and representative of student laptop use in the classroom.
Finally, all items were cross-checked with a composite list created from a
comprehensive literature review of laptop use scales (Table 1). This triangulation of
data sources helped to establish face-validity for the Laptop Use Scale in this study.
Content validity
Frequency analysis: behaviors in the classroom
A frequency analysis was run to determine the extent to which students reported in-
class academic and non-academic behaviors chosen for the Laptop Use Scale (Table 4).
At least 20 % of the students engaged in most items sometimes, frequently, or very
frequently. However, only 6–8 % of the students watched movies or used Twitter. We
decided to drop these two items from the in-class, non-academic construct because they
were not representative of what was actually taking place in the classroom.
Frequency analysis: behaviors outside of the classroom
A second frequency analysis was conducted for out of class academic and non-
academic behaviors chosen for the Laptop Use Scale (Table 5). At least 30 % of the
Table 4 Frequency analysis for academic and non-academic behaviors inside the classroom (n =156)
Variable M(SD) Never/rarely
(%)
Sometimes
(%)
Freq/very
Freq (%)
Academic use
Use software program for academics 4.3 (1.0) 6 10 84
Use notes posted by professor 4.0 (0.9) 6 20 74
Follow a PowerPoint presentation 3.8 (1.2) 15 14 71
Search web for academic purposes 3.7 (1.0) 9 28 63
Communicate with peers for academics 3.5 (1.3) 23 22 54
Take notes 3.2 (1.3) 31 24 44
Use online interactive tools 3.3 (1.0) 16 42 42
Participate in online surveys 2.9 (1.1) 39 32 29
Non-academic use
Personal instant messaging 3.1 (1.4) 36 16 48
Search web for personal reasons 3.1 (1.2) 27 33 40
Facebook 2.9 (1.4) 40 21 40
Personal email 2.9 (1.3) 40 24 35
Play games 1.8 (1.1) 80 11 10
Watch podcasts 1.8 (1.1) 80 10 10
Watch movies
a
1.3 (0.8) 92 5 3
Use twitter
a
1.2 (0.7) 94 3 3
a
Variable was not used in the final scale because it was not exhibited frequently enough
Assessing laptop use in higher education: The Laptop Use…29
123

students engaged sometimes, frequently, or very frequently in all but one of the
behaviors listed. Only 16 % of the students used Twitter, therefore we decided to
remove this item from the scale, because it was not representative of what was
actually taking place outside of the classroom.
Student comments: behaviors in the classroom
Students offered 175 comments about academic behaviors inside the classroom.
Reports of taking notes (n =53 comments), searching the web (n =51 comments),
following PowerPoint presentations (n =31 comments), using academic software
(n =29 comments), and collaboration with peers (n =16 comments) were
consistent with the proposed scale items for in-class academic behaviors. The only
items not commented on were the two items involving online interactive tools. This
finding is consistent with the relatively infrequent use on interactive tools noted in
the frequency analysis presented in Table 4.
Students made 163 comments about non-academic use of laptops inside the
classroom. Seventy-nine comments (64 %) focused on the use of Facebook, instant
messaging, and sending personal emails, a result that is consistent with the first three
items chosen for the in-class non-academic scale in Table 4. Comments about
playing games and entertainment, in general (n =15 comments), were also
consistent with the items suggested for the in-class non-academic scale. Students did
Table 5 Frequency analysis for academic and non-academic behaviors outside of the classroom
(n =156)
Variable M(SD) Never/rarely
(%)
Sometimes
(%)
Freq/very
Freq (%)
Academic use
Use software program for academics 4.3 (0.8) 4 7 89
Search the web for academics 3.9 (0.8) 4 22 74
Communicate with peers for academics 3.8 (1.1) 12 22 65
Working with peers on assigned group work 3.7 (1.0) 13 24 63
Sharing notes and course resources 3.6 (1.0) 17 25 58
Organizing course notes and materials 3.6 (0.9) 9 34 57
Online interactive activities 3.2 (1.0) 26 37 37
Searching the university library databases 3.0 (1.0) 29 39 32
Non-academic use
Personal web search 4.4 (0.8) 3 11 87
Personal email 4.2 (0.9) 4 19 77
Facebook 4.0 (1.2) 12 15 73
Personal instant messaging 4.0 (1.3) 14 15 71
Watch podcasts 3.6 (1.2) 18 25 58
Watch movies 3.2 (1.3) 30 27 43
Play games 2.8 (1.3) 39 27 34
Use Twitter
a
1.6 (1.2) 84 4 12
a
Variable was not used in the final scale because it was not exhibited frequently enough
30 R. Kay, S. Lauricella
123

not report watching video podcasts, movies, or using Twitter which is consistent
with the relatively low frequencies reported for these items in Table 4.
Student comments: behavior outside of the classroom
Students commented 155 times on the academic use of laptops outside of the
classroom. Use of software for academic purposes (n =17 comments), searching
the web and library databases to conduct research (n =66), collaborating with peers
(n =40 comments), and organization (n =10 comments) were consistent with the
frequency of student laptop use reported Table 5. Engaging in online interactive
activities was the only item on the outside-class, academic construct that was not
commented on by students, a result that is consistent with the low frequency of use
reported in Table 5.
Students offered 111 comments on non-academic use of laptops outside of
classroom. Reports of social interaction (for example Facebook, email, instant
messaging) with peers (n =54 comments), watching videos and podcasts, and
playing games (n =42 comments) were consistent with the frequency of laptops
behaviors reported in Table 5. Personal web-search and use of Twitter were not
commented on by students which was consistent with the low frequency score of
these behaviors in noted Table 5.
Construct validity
Principal component analysis
The first principal components analysis was conducted to determine whether in-class
academic construct was distinct from in-class non-academic construct. The results
from the varimax rotation (using Kaiser normalization) are presented because they
simplify the interpretation of the data (Field 2005). The Kaiser–Meyer–Olkin
measure of sampling adequacy (0.846) and Bartlett’s test of sphericity (p\.001)
indicated that the sample size was acceptable. The analysis confirmed that the two
proposed constructs, in-class academic and in-class non-academic use of laptops,
were distinct (Table 6). The only item to overlap was ‘‘Communication with Peers’’.
A second principal components analysis was run to determine whether the outside
class academic construct was distinct from outside class non-academic construct.
The results from the varimax rotation (using Kaiser normalization) are presented
because they simplify the interpretation of the data (Field 2005). The Kaiser–Meyer–
Olkin measure of sampling adequacy (0.815) and Bartlett’s test of sphericity
(p\.001) indicated that the sample size was acceptable. The analysis confirmed that
the two proposed constructs, in-class academic and in-class non-academic use, were
distinct (Table 7). The only item to overlap was ‘‘Emailing for Personal Reasons’’.
Correlations among Laptop Use Scale constructs
Correlations among all but one of the Laptop Use Scale constructs were modest but
significant ranging from 0.20 to 0.57 (Table 8). Shared variances, ranging from 4 to
Assessing laptop use in higher education: The Laptop Use…31
123

32 %, were small enough to support the assumption that each construct measured
was distinct. Correlations were generally higher within academic activities (inside
and outside the classroom) and within non-academic activities (inside and outside of
the classroom) than between academic and non-academic activities. In other words,
(a) students who engaged academic activities during class appeared more likely to
engage in academic activities outside of class and (b) students who engaged non-
academic activities in class seemed more likely to engage in non-academic activities
outside of class. There was one exception—the correlation between the in-class and
outside-class academic constructs was the same as the correlation between the in-
class academic and outside-class non-academic construct.
Convergent validity
In-class academic use
The only significant correlation for the in-class academic use construct was a
positive correlation with interest in the course (r=0.23, p\.01). Students who
were more interested in the course engaged more in academic activities (Table 9).
Table 6 Varimax rotated factor loadings on inside class behaviors (Laptop Use Scale)
Construct Factor 1 Factor 2
In class use (academic)
Search the web for academic purposes .776
Use the notes posted by the instructor .717
Follow a PowerPoint presentation on your laptop computer .713
Use software program for academic purposes .694
Take notes on my laptop .650
Use online interactive tools (for example learning objects, applets) .566
Participate in online surveys .515
Communicate with peers for academic reasons .450 .568
In-class use (non-academic)
Use instant messaging for personal reasons (for example MSN, Skype) .852
Search the web for personal reasons .851
Go on Facebook .838
Watch short video clips for personal use(for example YouTube) .717
Use email for personal reasons .679
Play games .667
Factor Eigenvalue PCT of VAR CUM PCT
1 4.10 29.3 29.3
2 3.46 24.7 55.0
32 R. Kay, S. Lauricella
123

In-class non-academic use
The correlation between the non-academic construct was significant and negative
for average grade reported for the previous year (r=-0.31, p\.01), current grade
reported (r=-0.26, p\.01), and year of study (r=-0.29, p\.01). In other
words, students who reported lower grades and being new to the university were
Table 7 Varimax rotated factor loadings on outside of class behaviors (Laptop Use Scale)
Construct Factor 1 Factor 2
Outside class use (academic)
Search the web for academic purposes .795
Working with peers on assigned group work .783
Communicate with peers for academic purposes .756
Organizing course notes and materials .726
Sharing notes and course resources .717
Use software program for academic purposes .614
Online interactive activities (for example learning objects. Applets) .613
Searching the university library databases for articles/books .578
Outside class use (non-academic)
Watch short video clips for personal use (for example YouTube) .793
Watch movies .761
Search the web for personal reasons .692
Use Instant Messaging for personal reasons (for example MSN, Skype) .664
Go on Facebook .619
play games .581
Use email for personal reasons .382 .398
Factor Eigenvalue PCT of VAR CUM PCT
1 4.14 27.6 27.6
2 3.22 21.5 49.1
Table 8 Correlations among Laptop Use Scale constructs (n =155)
Scale In-class
(academic)
In-class (non-
academic)
Outside-class
(academic)
Outside-class
(non-academic)
In-class (academic) 1.00 0.38** 0.38** 0.24**
In-class (non-academic) 1.00 0.08 0.57**
Outside-class (academic) 1.00 0.20*
Outside-class (non-academic) 1.00
*p\.05; ** p\.01
Assessing laptop use in higher education: The Laptop Use…33
123

somewhat more likely to engage in more non-academic use of laptops during class
(Table 9).
Outside-class academic use
Correlations between the outside-class, academic construct were significant and
positive for average grade reported for the previous year (r =0.24, p\.05), current
grade reported (r=0.40, p\.01), and hours per day on the laptop (r=0.32,
p\.01). Students who reported higher grades and used laptops more regularly were
more engaged in more academic laptop-related behaviors outside of class (Table 9).
Outside-class non-academic use
Correlations between the outside-class, non-academic construct were significant and
negative for year of study (r=-0.26, p\.01) and significant and positive for
hours per day on the laptop (r=0.23, p\.01) and computer comfort level
(r=0.34, p\.01). In other words, students who were new to the university, as
well as students who were on the laptops more during the day and who felt more
comfortable with computers were more likely to participate in non-academic
activities outside of class (Table 9).
Discussion
Overview
The purpose of the current study was to develop a comprehensive, research-based
metric for assessing student use of laptops inside and outside of higher education
classrooms. Particular attention was directed toward developing a broad scale,
Table 9 Correlations among Laptop Use Scale constructs and external variables
Scale Previous
grade
average
Current
grade in
course
Interest
in course
Year of
study
Hours on
laptop per
day
Computer
comfort level
In-class
(academic)
0.06 0.12 0.23** -0.13 0.10 0.10
In-class (non-
academic)
-0.31** -0.26** 0.01 -0.29** 0.10 0.18*
Outside-class
(academic)
0.24* 0.40** -0.06 0.15 0.32** -0.04
Outside-class
(non-
academic)
-0.06 -0.16 0.04 -0.26** 0.23** 0.34**
*p\.05; ** p\.01
34 R. Kay, S. Lauricella
123

addressing a wide and balanced range of academic and non-academic use of laptops,
providing reliability and validity estimates, and testing in a context where a variety
of teaching methods was used. A scale was developed to assess academic and non-
academic use of laptops inside and outside of higher education classrooms.
Addressing methodological concerns
Six methodological concerns were observed in previous research and addressed in
the current study. First, each of the academic and non-academic constructs had a
wide range of clear, specific behaviors. Second, a comprehensive list of items was
used based on a conglomerate of previous laptop scale items. Third, a roughly equal
balance of academic and non-academic uses was included. Fourth, reliability and
validity of the current scale were assessed. Fifth, an assessment of use of laptops
outside the classroom was conducted. Finally, the scale was tested in a teaching
environment that incorporated a wide range of teaching methods and not strictly
limited to a lecture-based format.
Reliability
The internal reliability estimates (0.77–0.87) for the Laptop Use Scale were good
(Kline 1999; Nunnally 1978), as was the inter-rater reliability of the categories
(96–98 %) used to assess student comments. Only two previous studies provided
estimates of reliability (Awwad and Ayesh 2013; Lauricella and Kay 2010), yet it is
argued that this metric is a standard and fundamental element of any evaluation tool
and should be calculated for future research studies, if the sample size permits.
Validity
Aside from face validity, which was determined by a review of Laptop Use Scale
items by two experts in field of education and technology, three types of validity
were assessed for the Laptop Use Scale—content, construct and convergent.
Content validity was examined to determine whether the items truly matched the
types of use that students display inside and outside higher education classrooms.
Construct validity was calculated to determine if there were clear academic and
non-academic categories of laptop use. Convergent validity was examined to
determine if the academic and non-academic constructs of the Laptop Use Scale
were consistent and associated with other variables such as interest in the course,
grades, year of study, and hours per day on the laptop. Each of these forms of
validity will be discussed in turn.
Content validity
For the Laptop Use Scale to have sufficient content validity, we needed to determine
the extent to which the measure represents the use of laptops by higher education
students. A frequency analysis of academic and non-academic use of laptops, both
inside and outside of the classroom, were consistent with the list of activities chosen
Assessing laptop use in higher education: The Laptop Use…35
123

for the Laptop Use Scale. Frequency of student comments also matched the
selection of items for the Laptop Use Scale. It is reasonable to conclude that content
of the Laptop Use Scale is representative of how students behave with laptops inside
and outside of higher education classroom.
Outlier behaviors that were removed from the final scale included watching full-
length movies and using Twitter. Previous research noting multitasking, twitch-
speed, and limited focus of Net Generation students (Prensky 2010; Small and
Vorgan 2009) is consistent with the higher education students in this study not
watching full length movies. The limited use of Twitter is predicted from a recent
report indicating that only 18 % of college students use this social media tool
compared to 68 % who use Facebook (Pew Research Center 2013). It is important
to recognize that use of tools can change from year to year, so specific scale items
may need to be modified. For example, according the Pew Research Centre,
Pinterest and LinkedIn are popular social media tools that might deserve
consideration in future assessments of laptop use (Pew Research Center 2013).
Another option would be to include a general item labelled ‘‘social media tools’’
because the specific tools used may not be as critical as the level of distraction
observed.
Construct validity
The first principal components analysis revealed two distinct constructs related to
the use of laptops in higher education classrooms (academic use, non-academic use)
with only one item overlapping, ‘‘communication with peers.’’ It is conceivable that
communication with peers through email, instant messaging, and social media is a
continuous task for students of the Net Generation. As Small and Vorgan (2009)
suggested, today’s generation of students want to be in contact with everything, all
the time so whether they are engaging in academic or non-academic behavior may
be irrelevant. Nonetheless, academic and non-academic constructs are consistent
with previous research on student use of laptops (Awwad and Ayesh 2013; Barak
et al. 2006; DiGangi et al. 2007; Fried 2008; Gaudreau et al. 2014; Kraushaar and
Novak 2010; McCreary 2009; Skolnik and Puzo 2008).
The second principal components analysis revealed two distinct constructs
related to the use of laptops outside higher education classrooms (academic use,
non-academic use) with only one item overlapping, ‘‘emailing for personal
reasons.’’ While previous research has not examined laptop use outside the
classroom, the academic versus non-academic categories are consistent with those
observed inside the classroom.
A positive, significant but relatively small correlation (r=0.38) between
academic use of laptops inside and outside of the classroom supports the assumption
that these two constructs are related, but still distinct. A similar conclusion can be
drawn with respect to the relatively small (r=0.57), but significant correlation
between non-academic behaviors inside and outside the classroom.
36 R. Kay, S. Lauricella
123

Convergent validity
Six variables were used to explore convergent validity and included: previous
average grade, current grade average in the course, interest in the course, year of
study, hours per day on the laptop, and computer comfort level. With respect to
non-academic use of laptops during class, the expected negative correlations with
grades and year of study were observed. However, for academic use of laptops in
the class, the only significant correlation was with higher interest in the course.
One would predict that students with better grades or more experience at the
university would be more inclined to engage in academic use of laptops. Further
research, perhaps in the form of interviews to focus groups, is needed to
understand this anomaly.
With respect to correlations among academic and non-academic laptop use
outside of the classroom and the six convergent variables assessed, significant
correlations with grades, interest in the course, and hours per day on the laptop
indicated that the Laptop Use Scale had some degree of convergent validity. One
notable exception was the significant and positive correlation between non-
academic use of laptops outside the classroom and greater comfort level with
computers. This same correlation was not significant with respect to academic use
of laptops outside the classroom. Perhaps students who are very comfortable with
computers use them within a much broader spectrum than students who are less
comfortable, and extend their use to gaming and entertainment. Again, more in-
depth research needs to be conducted to understand the complexities of why
students behave the way they do with laptops.
Summary
The purpose of this study was to develop a research-based, comprehensive scale to
assess student use of laptops inside and outside of the classroom. The Laptop Use
Scale (Laptop Use Scale) was comprised of four constructs: academic use (inside
class), non- academic use (inside class), academic use (outside class), non-academic
use (outside class).
All scale constructs showed acceptable internally reliability. A principal-
components factor analysis confirmed good construct validity. Correlations among
the Laptop Use Scale constructs were significant but small enough to support the
existence of four distinct constructs. Content validity was reinforced by frequencies
of laptop use reported and student comments. Finally, the four Laptop Use Scale
constructs were correlated some but not with all six of the variables used to establish
convergent validity. Further research is recommended with respect to understanding
the dynamics of academic and non-academic laptop use in in higher education
classrooms, perhaps employing interviews or focus groups.
Implications for education
The ubiquity of student laptop ownership and use in higher educational institutions
(Dahlstrom et al. 2013) requires a clear understanding of how these devices are used
Assessing laptop use in higher education: The Laptop Use…37
123

inside and outside the classroom. One starting point for assessing student use of
laptops is to develop an evaluation scale. Previous studies have used an assortment
of metrics that have produced mixed results (Awwad and Ayesh 2013; Barak et al.
2006; DiGangi et al. 2007; Fried 2008; Gaudreau et al. 2014; Kraushaar and Novak
2010; McCreary 2009; Skolnik and Puzo 2008). In this study, we developed a scale
that was based on a composite of previous laptop us items, and grounded in current
theory on technology-based learning (Mayes and de Freitas 2007) as well as typical
behaviors observed in the Net Generation (Tapscott 2009). Reliability and validity
estimates indicated that the Laptop Use Scale could be used to assess academic
benefits and challenges experienced by higher education students.
There are several possible scenarios for using the Laptop Use Scale in
educational settings. First, this scale could be helpful for exploring the impact of
specific teaching strategies on academic versus non-academic laptop behaviors in
the classroom. Second, the use of laptops in variety of disciplines could be
compared to determine the extent to which subject area and context influence the
use of laptops in the classroom. Third, scale feedback could help assess and address
non-academic use of laptops outside of the classroom. While instructors could and
would not control behaviors beyond the realm of their personal classrooms,
describing and disseminating patterns of use might be helpful to higher education
students, particularly if they were in their first year of study. Fourth, having a
reliable and valid tool for assessing laptop use is essential to test the effectiveness of
various in-class, laptop management strategies such as having laptop-free zones,
requiring students to close laptops at certain points in a class, and restricting access
to particularly distracting sites.
Caveats and future research
Data from the Laptop Use Scale appeared to be reliable, valid, and grounded in
previous research. However, there are several caveats that should be articulated
for future research on student use of laptops. First, the sample size, while
reasonably large, consisted of communication and education students. The Laptop
Use Scale needs to be tested on students in a wider range of subject areas such as
medicine, arts, law, engineering and science where use of laptops could vary
substantially.
Second, even though the participation response rate was over 80 %, there is risk
of bias based on students whose chose to participate in the study and those who did
not. However, it is not clear what the precise nature of this bias would be. To
address potential biases in participation, future researchers might provide a small
incentive to encourage all students to participate.
Third, while qualitative data were collected in the form of written comments, it
would be prudent to collect interview or focus group data to understand why
students use certain tools or engage in particular activities inside and outside the
classroom.
Fourth, the Laptop Use Scale does not address multitasking or the switching
between academic and non-academic laptop use. This behavior has been reported in
38 R. Kay, S. Lauricella
123

two recent laptop studies (Kraushaar and Novak 2010; Sana et al. 2013) and should
be considered in future scale development.
Fifth, the specific uses of laptops observed are partially linked to technological
developments. Five years ago, social media tools would not have been relevant, but
now they occupy considerable academic and non-academic attention from many
higher education students. Consequently, specific academic and non-academic uses
of laptops may need to be added or subtracted depending on how the technology
changes.
Sixth, to maximize the accuracy of assessing academic and non-academic in class
laptop activities, it would particularly valuable and informative to install tracking
software, like RescueTime, to record time spent specific laptops programs.
Matching detailed tracking information with scale response data is one way to
further establish validity of the Laptop Use Scale.
Seventh, it would be a judicious next step to assess the predictive validity of the
Laptop Use Scale constructs with student performance in the class where the laptop
was being used.
Finally, the Laptop Use Scale could be used to investigate individual differences
in academic and non-academic laptop activities inside and outside of the classroom
based on gender, grade level, computer experience, academic ability, and special
learning needs.
Appendix: Laptop Use Scale
Background information
1. What is your gender? (Male, Female)
2. What year of university are you in? (1, 2, 3 or 4)
3. What was your average grade in all your courses last year? (\50, 50–59, 60–69,
70–70, 80–89, 90?)
4. What course are you taking? _____________
5. What is your average in the course right now? (\50, 50–59, 60–69, 70–70,
80–89, 90?)
6. How comfortable are you with using computer technology? (Not at all
Comfortable, Somewhat Comfortable, Comfortable, Very Comfortable)
7. About how many hours per day do you spend using your laptop computer?
_____
Assessing laptop use in higher education: The Laptop Use…39
123

Academic use DURING class
How often did you do the following activities DURING class in this course?
Never Rarely Sometimes Freq Very
Freq
1. Take notes on my laptop 1 2 3 4 5
2. Use the notes posted by the instructor 1 2 3 4 5
3. Search the web for academic purposes 1 2 3 4 5
4. Use online interactive tools (for example learning
objects, applets)
12 3 45
5. Participate in online surveys 1 2 3 4 5
6. Follow a PowerPoint presentation on your laptop
computer
12 3 45
7. Communicate with peers for academic reasons (for
example instant messaging, email)
12 3 45
8. Use a software program for academic purposes (e.g.
Word, Excel, Access)
12 3 45
9. Overall what (if any) do you see are the biggest benefits to having a laptop IN
class for this course? Why?
Non-academic use DURING class
How often did you do the following activities DURING class in this course?
Never Rarely Sometimes Freq Very
Freq
1. Play games 1 2 3 4 5
2. Watch movies 1 2 3 4 5
3. Watch short video clips for personal use (for example
YouTube)
12 3 45
4. Search the web for personal reasons 1 2 3 4 5
5. Go on Facebook 1 2 3 4 5
6. Use Twitter 1 2 3 4 5
7. Use instant messaging for personal reasons (for
example MSN, Skype)
12 3 45
8. Use email for personal reasons 1 2 3 4 5
9. Overall what (if any) do you see are the biggest distractions to having a laptop
IN class for this course? Why?
40 R. Kay, S. Lauricella
123

Academic OUTSIDE if class
How often did you do the following activities OUTSIDE of class in this course?
Never Rarely Sometimes Freq Very
Freq
1. Organizing course notes and materials 1 2 3 4 5
2. Search the web for academic purposes 1 2 3 4 5
3. Online interactive activities (for example learning
objects. Applets)
12 3 45
4. Using a software program for academic purposes (for
example Word, Excel)
12 3 45
5. Sharing notes and course resources 1 2 3 4 5
6. Communicate with peers for academic purposes (for
example instant messaging, email)
12 3 45
7. Working with peers on assigned group work 1 2 3 4 5
8. Getting help from peers on computer related tasks 1 2 3 4 5
9. Searching the university library databases for
articles/books
12 3 45
10. Overall what (if any) do you see are the biggest benefits to having a laptop
OUTSIDE class? Why?
Non-academic use DURING class
How often did you do the following activities DURING class in this course?
Never Rarely Sometimes Freq Very
Freq
1. Play games 1 2 3 4 5
2. Watch movies 1 2 3 4 5
3. Watch short video clips for personal use (for example
You Tube)
12 3 45
4. Search the web for personal reasons 1 2 3 4 5
5. Go on Facebook 1 2 3 4 5
6. Use Twitter 1 2 3 4 5
7. Use instant messaging for personal reasons (for
example MSN, Skype)
12 3 45
8. Use email for personal reasons 1 2 3 4 5
Assessing laptop use in higher education: The Laptop Use…41
123

9. Overall what (if any) do you see are the biggest distractions to having a laptop
IN class for this course? Why?
10. Overall what (if any) do you see are the biggest distractions to having a laptop
OUTSIDE class? Why?
References
Aguilar-Roca, N. M., Williams, A. E., & O’Dowd, D. K. (2012). The impact of laptop-free zones on
student performance and attitudes in large lectures. Computers and Education, 59(4), 1300–1308.
doi:10.1016/j.compedu.2012.05.002.
Annan-Coultas, D. L. (2012). Laptops as instructional tools: Student perceptions. TechTrends, 56(5),
34–41.
Awwad, F., & Ayesh, A. (2013). Effectiveness of laptop usage in UAS university undergraduate teaching.
The Turkish Online Journal of Educational Technology, 12(2), 77–88.
Barak, M., Lipson, A., & Lerman, S. (2006). Wireless laptops as means for promoting active learning in
large lecture halls. Journal of Research on Technology in Education, 38(3), 245–263.
Barkhuus, L. (2005). Bring your own laptop unless you want to follow the lecture: Alternative
communication in the classroom. In Proceedings of the 2005 International ACM SIGGROUP
Conference on Supporting Group Work (pp. 140–143). New York, ACM. doi: 10.1145/1099203.
1099230.
Bruner, J. (1960). The process of education. Cambridge, MA: Harvard University Press.
Carfio, J., & Perla, R. (2008). Resolving the 50-year debate around using and misusing Likert scales.
Medical Education, 42(12), 1150–1152. doi:10.1111/j.1365-2923.2008.03172.x.
Dahlstrom, E., Walker, J. D., & Dziuban, C, M. (2013). The ECAR study of undergraduate students and
information technology (Research Report). Louiseville, CO: EDUCAUSE Center for Analysis and
Research. Retrieved from https://net.educause.edu/ir/library/pdf/ERS1302/ERS1302.pdf
Dalsgaard, C., & Godsk, M. (2007). Transforming traditional lectures into problem-based blended
learning: Challenges and experiences. Open Learning: The Journal of Open, Distance and
e-Learning, 22(1), 29–42. doi:10.1080/02680510601100143.
Debevec, K., Shih, M., & Kashyap, V. (2006). Learning strategies and performance in a technology
integrated classroom. Journal of Research on Technology in Education, 38(3), 293–307.
DiGangi, S., Kilic, Z., Yu, C. H., Jannasch-Pennell, A., Long, L., Kim, C., et al. (2007). One to one
computing in higher education: A survey of technology practices and needs. AACE Journal, 15(4),
367–387.
Enfield, J. (2014). Looking at the impact of the flipped classroom model of instruction on undergraduate
multimedia students at CSUN. TechTrends, 57(6), 14–27. doi:10.1007/s11528-013-0698-1.
Field, A. (2005). Discovering statistics using SPSS (2nd ed.). Thousand Oaks, CA: Sage Publications.
Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers and Education,
50(3), 906–914.
Gagne
´, R. (1985). The conditions of learning. New York: Holt, Rinehart and Winston.
Gaudreau, P., Miranda, D., & Gareau, A. (2014). Canadian university students in wireless classrooms:
What do they do on their laptops and does it really matter? Computers and Education, 70(3),
245–255. doi:10.1016/j.compedu.2013.08.019.
Hyden, P. (2005). Teaching statistics by taking advantage of the laptop’s ubiquity. New Directions for
Teaching and Learning, 101, 37–42.
Jamieson, S. (2004). Likert scales: how to (ab)use them. Medical Education, 38(12), 1217–1218. doi:10.
1111/j.1365-2929.2004.02012.x.
Kay, R. H., & Lauricella, S. (2011). Exploring the benefits and challenges of using laptop computers in
higher education classrooms: A formative analysis. Canadian Journal of Learning and Technology,
37(1). Retrieved from http://www.cjlt.ca/index.php/cjlt/article/view/565/299
Kay, R. H. & Lauricella, S. (2014). Investigating the benefits and challenges of using laptop computers in
higher education. Canadian Journal of Learning and Technology, 40(2), 1–25. Retrieved from
http://www.cjlt.ca/index.php/cjlt/article/view/831/393
42 R. Kay, S. Lauricella
123

Kline, P. (1999). The handbook of psychological testing (2nd ed.). London: Routledge.
Kolb, A. Y., & Kolb, D. A. (2005). Learning styles and learning spaces: Enhancing experiential learning
in higher education. Academy of Management Learning and Education, 4(2), 193–212. doi:10.5465/
AMLE.2005.17268566.
Kraushaar, J. M., & Novak, D. C. (2010). Examining the effects of student multitasking with laptops
during the lecture. Journal of Information Systems Education, 21(2), 241–251.
Kuzon, W. M., Urbanchek, M. G., & McCabe, S. (1996). The seven deadly sins of statistical analysis.
Annals of Plastic Surgery, 37, 265–272.
Lasry, N., Mazur, E., & Watkins, J. (2008). Peer instruction: From Harvard to the two-year college.
American Association of Physics Teachers,76(11), 1066–1069. Retrieved from http://mazur.
harvard.edu/sentFiles/Mazur_61464.pdf
Lauricella, S. & Kay, R. H. (2010). Assessing laptop use in higher education classrooms: The laptop
effectiveness scale (LES). Australian Journal of Educational Technology,26(2), 151–163.
Retrieved from http://ajet.org.au/index.php/AJET/article/view/1087/342
Laurillard, D. (2002). Rethinking university teaching. A conversational framework for the effective use of
learning technologies. London: Routledge.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge:
Cambridge University Press.
Lewis, S. E., & Lewis, J. E. (2005). Departing from lectures: An evaluation of a peer-led guided inquiry
alternative. Journal of Chemical Education, 82(1), 135. doi:10.1021/ed082p135.
Lindroth, T., & Bergquist, M. (2010). Laptopers in an educational practice: Promoting the personal
learning situation. Computers and Education, 54(2), 311–320. doi:10.1016/j.compedu.2009.07.014.
Mayes, T., & de Freitas, S. (2007). Technology-enhanced learning: The role of theory. In H. Beetham &
R. Sharpe (Eds.), Rethinking pedagogy for a digital age (pp. 17–30). New York: Routledge.
McCreary, J. R. (2009). The laptop-free zone. Valparaiso University Law Review, 43, 1–87.
Miles, M. B., & Hubrman, M. (1994). Qualitative data analysis (2nd ed.). Thousand Oaks, CA: Sage
Publication.
Murray, J. (2013). Likert data: What to use, parametric or non-parametric? International Journal of
Business and Social Science, 4(11), 258–264.
Newell, A. (1980). One final word. In D. T. Tuma & F. Reif (Eds.), Problem solving and education:
Issues in teaching and research (pp. 175–189). Hillsdale, New Jersey: Lawrence Erlbaum
Associates.
Norman, G. (2010). Likert scales, levels of measurement and the ‘‘laws’’ of statistics. Advances in Health
Sciences Education—Theory and Practice, 15(5), 625–632. doi:10.1007/s10459-010-9222-y.
Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.
Pew Research Center. (2013). Social media update 2013. Washington: DC. Retrieved from http://www.
pewinternet.org/files/2013/12/PIP_Social-Networking-2013.pdf
Piaget, J. (1970). Science of education and the psychology of the child. New York: Orion Press.
Prensky, M. (2010). Teaching digital natives. Thousand Oaks, CA: Corwin.
Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users
and nearby peers. Computers and Education, 62(1), 24–31. doi:10.1016/j.compedu.2012.10.003.
Skolnik, R., & Puzo, M. (2008). Utilization of laptop computers in the school of business classroom.
Academy of Educational Leadership Journal, 12(2), 1–10.
Small, G., & Vorgan, G. (2009). iBrain: Surviving the technological alteration of the modern mind. New
York: Harper.
Tapscott, D. (2009). Grown up digital: How the Net generation is changing your world. New York:
McGraw-Hill.
Vygotsky, L. S. (1978). Mind and society. Cambridge, MA: Harvard University Press.
Wilson, B. G., & Myers, K. M. (2000). Situated cognition is theoretical and practical context. In D.
H. Jonassen & S. M. Land (Eds.), Theoretical foundations of learning environments (pp. 57–86).
New Jersey: Lawrence Erlbaum.
Wurst, C., Smarkola, C., & Gaffney, M. A. (2008). Ubiquitous laptop usage in higher education: Effects
on student achievement, student satisfaction, and constructivist measures in honors and traditional
classrooms. Computers and Education, 51(4), 1766–1783. doi:10.1016/j.compedu.2008.05.006.
Assessing laptop use in higher education: The Laptop Use…43
123

Robin Kay is currently a Full Professor and the Director of Graduate Studies in the Faculty of Education
at the University of Ontario Institute Of Technology in Oshawa, Canada. He has published over 120
articles, chapters and conference papers in the area of computers in education, is a reviewer for five
prominent computer education journals, and has taught computer science, mathematics, and educational
technology for over 20 years at the high school, college, undergraduate and graduate level. Current
projects include research on laptop use in higher education, BYOD in K-12 education, web-based
learning tools, classroom response systems, e-learning in secondary and higher education, video podcasts,
scale development with respect to computer attitude, use, and behaviour, gender differences in computer
related behaviour, emotions and the use of computers, the impact of social media tools in education, and
factors that influence how students learn with technology.
Sharon Lauricella is an award-winning Associate Professor. She is a two-time recipient of the UOIT
Teaching Award, the CJPS Faculty Teaching Award, and has been nominated for provincial and national
teaching recognition. Sharon holds a doctoral degree from Cambridge University in England. Her
undergraduate work was completed in Boston, Massachusetts, and Edinburgh, Scotland. Sharon instructs
courses including Nonviolent Communication, Professional Writing, Advanced Writing, Communication
Ethics, and Public Speaking. Sharon is keenly interested in the interplay between spirituality and
communication and student experiences with technology in learning.
44 R. Kay, S. Lauricella
123