Critical Summary

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For March 7, you have an Annotated

Works Cited

due. What you need to do is conduct your research, then summarize the main argument of it, i.e., the thesis and then critique it/evaluate it. Please use the instructions found on Moodle for a Critical Summary. Aim for minimum 150-200 words, three to four sentences, but you can go longer if you need to. You will be using this work directly in your proposal/presentation, so you are not doing extra work.

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Works Cited

Al-Krenawi, Alean, John R. Graham, and Vered Slonim-Nevo. “Mental Health Aspects of Arab-Israeli Adolescents From Polygamous Versus Monogamous Families.” Journal of Social Psychology vol. 142, no. 4 (2002): pp. 446-460. Academic Search Complete. doi:10.1002/tox.20155.

Put your summary here.

Al-Krenawi, Alean, John R. Graham, and Salem Al-Krenawi. “Social Work Practice with Polygamous Families.” Child & Adolescent Social Work Journal vol. 14, no. 6 (1997): pp. 445-458. Academic Search Complete. Accessed 12 Apr. 2011.

Put your summary here.

lable at ScienceDirect

Computers in Human Behavior 77 (2017) 86e94

Contents lists avai

Computers in Human Behavior

journal homepage: www.elsevier.com/locate/comphumbeh

Full length article

In-lecture media use and academic performance: Does subject area
matter?

Daniel B. le Roux, Douglas A. Parry*

Cognition and Technology Research Group, Department of Information Science, Stellenbosch University, South Africa

a r t i c l e i n f o

Article history:
Received 6 April 2017
Received in revised form
11 August 2017
Accepted 21 August 2017
Available online 26 August 2017

Keywords:
Media multitasking
Academic performance
Cognitive control
Subject area

* Corresponding author. Stellenbosch University, Fac
Department of Information Science, Room 450, South

E-mail address: dougaparry@sun.ac.za (D.A. Parry

http://dx.doi.org/10.1016/j.chb.2017.08.030
0747-5632/

a b s t r a c t

The current generation of university students display an increasing propensity for media multitasking
behaviour with digital devices such as laptops, tablets and smartphones. A growing body of empirical
evidence has shown that this behaviour is associated with reduced academic performance. In this study
it is proposed that the subject area within which an individual is situated may influence the relationship
between media multitasking and academic performance. This proposition is evaluated, firstly, by means
of a meta-review of prior studies in this area and, secondly, through a survey-based study of 1678
students at a large university in South Africa. Our findings suggest that little or no attention has been
paid to variations between students from different subject areas in previous work and, based on our data,
that subject area does influence the relationship between media use and academic performance. The
study found that while a significant negative correlation exists between in-lecture media use and aca-
demic performance for students in the Arts and Social Sciences, the same pattern is not observable for
students in the faculties of Engineering, Economic and Management Sciences, and Medical and Health
Sciences.

1. Introduction

The current generation of university students are considered to
be part of the net generation (Tapscott, 1998), a cohort displaying
an unprecedented propensity for engaging and interacting with
mobile digital devices such as laptops, smartphones and tablets
(Cotten, McCullough, & Adams, 2011, ch. 25

).

They generally display
a positive relationship with digital media, exhibiting significantly
higher adoption and engagement rates than other generations
(Dahlstrom & Bichsel, 2014, p. 50). Extending from their significant
engagement with digital media in the course of their general life
(Junco & Cotten, 2011; Moreno et al., 2012; Thompson, 2013),
studies indicate that students are spending an increasingly larger
proportion of their time engaging with media while performing
academic activities (Burak, 2012; Fried, 2008; Jacobsen & Forste,
2011; Junco, 2012; Leysens, le Roux, & Parry, 2016).

In the context of this study digital media are conceptualised as
always-on, socially interactive, technologically mediated commu-
nication artifacts. Media facilitate access to the World Wide Web,

ulty of Arts & Social Sciences,
Africa.

).

providing opportunities for communication, collaboration and
other forms of social interaction, from anywhere and with a mini-
mal amount of effort (Wardley & Mang, 2015). They are charac-
terised by an increased level of interactivity (Bolter, 2003), a
hypertextual mode of operation (Conklin, 1987), increased in-
dividuality (Feenberg & Bakardjieva, 2004) and a deeper level of
involvement in peoples’ lives (Kennedy, 2006).

It has been argued that these characteristics contribute to
increased levels of, not only media use, but media multitasking
among individuals (Parry, 2017). Multitasking describes the pur-
poseful, concurrent performance of independent tasks, each asso-
ciated with distinct intentions (Salvucci & Taatgen, 2015). It can
take place either as a result of environmental interruptions, or as a
result of self-interruption. Such interruptions represent the re-
prioritisation of goals, with cognitive control being directed to the
goal currently possessing the highest priority (Clapp & Gazzaley,
2013).

Multitasking involves rapidly switching between various
ongoing activities creating continuous attention shifts and dis-
ruptions (Chen & Yan, 2016; Fried, 2008; Small & Vorgan, 2009).
While the ability to do this may be well-developed among certain
individuals, “it has been broadly shown that rapid switching
behaviour, when compared to carrying out tasks serially, leads to

mailto:dougaparry@sun.ac.za

http://crossmark.crossref.org/dialog/?doi=10.1016/j.chb.2017.08.030&domain=pdf

www.sciencedirect.com/science/journal/07475632

www.elsevier.com/locate/comphumbeh

http://dx.doi.org/10.1016/j.chb.2017.08.030

D.B. le Roux, D.A. Parry / Computers in Human Behavior 77 (2017) 86e94 87

poorer learning results in students and poorer performance of the
tasks being carried out” (Kirschner & De Bruyckere, 2017, p. 139).
This has highlighted the importance of not equating frequent
multitasking to effective multitasking. Kirschner and De Bruyckere
(2017) argue that there is no evidence to support the pervasive
belief that the net generation are better multitaskers than previous
generations. This misconception, they believe, has lead to the
adoption of various deleterious educational practices.

Logie, Trawley, and Law (2011) found that the ability to multi-
task is enabled by the interaction of multiple cognitive functions,
including memory, planning and intent. Gazzaley and Rosen (2016),
accordingly, argue that rapid switching between tasks will have a
stronger impact on the performance of individuals with underde-
veloped or impaired cognitive function, “such as children, older
adults, and individuals suffering from neurological and psychiatric
conditions” (Gazzaley & Rosen, 2016, p. 5).

The phrase media multitasking refers to the simultaneous use of
at least one type of media, while engaging in any number of other
media or non-media activities (Jeong & Hwang, 2012). It follows
that media use does not necessarily equate to media multitasking.
However, it is our premise, based on previous work by ourselves
and others (Judd, 2013; Carrier, Cheever, Rosen, Benitez, & Chang,
2009; Rosen, Mark Carrier, & Cheever, 2013; le Roux & Parry,
2017; Parry, 2017), that frequent rapid switching between multi-
ple media and non-media tasks is, among members of the net
generation, the norm. While this form of behaviour may create the
illusion of tech savviness and effective multitasking, Kirschner and
De Bruyckere (2017) warn that a broad range of evidence suggests
that this is a myth.

1.1. Media multitasking and academic performance

A recent meta-review of studies investigating media multi-
tasking and academic performance indicates that there appears to
be a negative correlation between media multitasking behaviour in
academic contexts and academic performance (Van der Schuur,
Baumgartner, Sumter, & Valkenburg, 2015). This outcome sug-
gests that media multitasking implies some form of cognitive cost
for learning, impeding the processing and encoding of information
into long term memory (Oulasvirta & Saariluoma, 2004). We adopt
the position that individuals possess an inherently limited capacity
to pay attention to multiple simultaneous sensory stimuli (Gazzaley
& Rosen, 2016; Kahneman, 1973; Kirschner & De Bruyckere, 2017;
Loh & Kanai, 2015; Ophir, Nass, & Wagner, 2009). It follows that,
for students, increased levels of media multitasking during aca-
demic activities reduce the cognitive resources available for
retaining or making sense of academic content.

A number of studies involving students demonstrate how the
learning process is negatively affected by media multitasking
behaviour. In the majority of these studies academic performance
refers to academic outcomes, such as course marks, test scores or
year averages (GPA) (Van der Schuur et al., 2015). Within this line of
research studies either adopt a correlational methodology, making
use of self-administered questionnaires relating to use frequencies,
habits and academic outcomes or an experimental approach in
which participants are generally exposed to media during an aca-
demic activity and their understanding and retention of content is
measured.

Van der Schuur et al. (2015) reviewed a sample of studies
(n ¼ 43) that investigated the effect of media multitasking on ac-
ademic performance. Of the 43 studies reviewed 17 indicate a
significant negative correlation, four studies found no significant
relationship and in the remainder of the studies the significance or
direction of the relationship was not calculable. Negative correla-
tions were found to exist in experimental as well as survey based

studies. However, the correlations were small to moderate in
strength, with no studies finding strong correlation. This should, of
course, be interpreted with recognition of the wide spectrum of
determinants of academic performance as a dependent variable.

Junco and Cotten (2012) examined the relationship between
media use frequency while studying and academic performance
through a survey of a large sample of students. The findings indi-
cated that students’ media multitasking behaviour was not curbed
by academic tasks d students reported that they frequently
engaged in technologically mediated, off-task activities whilst
engaged in academic study. Upon analysis of the data, Junco and
Cotten (2012) found that use of Facebook and texting activities
were negatively correlated with indicators for academic achieve-
ment while talking on the phone, e-mail, instant messaging and
web searching were not. It was concluded that the type as well as
the purpose of the particular technologically mediated activity
matters in terms of the educational impacts derived from
multitasking.

In another study Risko, Buchanan, Medimorec, and Kingstone
(2013) required participants to observe a pre-recorded lecture.
While observing this lecture half of the participants were instruc-
ted to complete a series of online activities on laptops. Directly
following the lecture students’ recall of the presented content was
tested. Through filmed observations of the lecture Risko et al.
(2013) were able to determine that those students who engaged
in the mediated tasks spent less time attending to the lecture than
their peers. In addition to spending less time attending to the lec-
ture, the participants in the mediated condition retained less in-
formation than those in the control condition. Risko et al. (2013)
performed a mediation analysis to determine if the reduction in
attention to the lecture mediated the effect of the laptop activities
on retention. The results are consistent with the authors’ hypoth-
esis that those in the laptop condition will achieve diminished
performance because their attention is focused on the medium, not
the lecture. Interestingly, 79% of the participants indicated that they
felt their engagement with media during the lecture influenced
their performance. Risko et al. (2013) found that there was no
relation between this indication and the test outcomes.

While these studies and others (e.g. Fried, 2008; Leysens et al.,
2016; Rosen et al., 2013) have reported negative correlations be-
tween in-lecture media multitasking and academic performance,
other studies found no difference in performance outcomes as a
result of media multitasking behaviour (Elder, 2013; Lee, Lin, &
Robertson, 2012). This suggests that further research is required
to enhance our understanding of the dynamics of the relationship
between these constructs.

2. Media multitasking and academic performance in
different subject areas

In this study we propose that the subject area within which an
individual is situated will influence the relationship between media
multitasking and academic performance. We define the term sub-
ject area to refer to the general overarching category a particular
academic discipline falls within. Findings described in previous
studies (e.g. Davis, Pastor, & Barron, 2004; Osterman, 2015; Saleh,
2001) suggest that there exists a scope for personal and psycho-
logical differences between individuals studying different subject
areas at university. Saleh (2001), for example, found a significant
effect of brain hemisphericity on students’ academic major, or
subject area. The author found that arts/literature, education,
nursing, communication and law students tended to be right
brained while business/commerce, engineering and science stu-
dents were left brained (Saleh, 2001, p. 196). Based on survey study
of 101 subjects, Osterman (2015) found that, under certain

D.B. le Roux, D.A. Parry / Computers in Human Behavior 77 (2017) 86e9488

circumstances, there exists a relationship between academic major,
or subject area and, thinking style. His results indicated that stu-
dents following a Humanities major generally demonstrated
balanced linear/non-linear thinking styles, while students following
a major in Business tended to demonstrate more linear thinking
styles. Interestingly, while not directly associating thinking styles
with particular subject areas, Chen and Ji (2015) found thinking
style to interact with the use of educational media in determining
academic performance. Their findings indicated that, while thinking
style did not predict overall media use, subjects who demonstrated
abstract reasoning used non-educational media less frequently.
They found, furthermore, that when subjects demonstrated con-
crete reasoning, educational media use was associated with
improved academic performance. However, their data did not show
support for the proposition that thinking style moderates the rela-
tionship between non-educational media use and academic per-
formance. We propose that these differences may play a role in
students’ media multitasking behaviour, and, the subsequent im-
plications for academic performance.

In a study specifically focusing on associations between students’
in-class use of media and their academic success Gaudreau,
Miranda, and Gareau (2014) examined differences occurring be-
tween four subject areas (Arts 14.5%, Social Sciences 37%, Health
Sciences 28%, Sciences 20.4%) amongst a large sample (n ¼ 1129) of
Canadian students. Following analysis of self-administered ques-
tionnaires, Gaudreau et al. (2014) found small, but significant dif-
ferences (Cohen’s d from � .25 to � .34) in the media behaviours
described by students across different subject areas. Science stu-
dents were found to be less likely to: use their laptop for note-taking
purposes, browse unrelated websites and visit social networking
services. Despite finding differences in behaviour between students
from different subject areas, Gaudreau et al. (2014) did not find
significant differences between subject areas in terms of the rela-
tionship between media multitasking and academic performance.

2.1. Subject area analysis

To investigate, in more detail, the role of subject area in the
relationship between media multitasking and academic perfor-
mance we performed a systematic review of literature. A general
pool of literature within this area was composed through a series of
steps, conducted in an iterative manner. As a starting point a
number of specialist journals1 were selected. Through keyword
searches relevant articles were located, considered and included in
the general pool. Following this, a ‘snowball’ approach, as sug-
gested by Webster and Watson (2002), was used to discover
additional relevant sources. Two methods were employed: back-
wards search, and forwards search. Backwards search involved
extracting literature from the sources cited in these initial studies.
Forwards search involved finding studies that cited the literature
already contained within the general pool.

Having finalised the general pool of literature (n ¼ 56), a specific
sample from this general pool was extracted using a non-
probability sampling strategy as described by Berg, Lune, and
Lune (2004, p. 35). Through purposive sampling a sample of liter-
ature displaying particular attributes was created. Literature were
required to meet a number of criteria in order to be considered for
the sample. These criteria include: either an experimental or a
correlational methodology, publication post 20072, a focus on

1 Selected according to focus area and impact factor. Computers in Human
Behavior, Computers & Education, Cyberspychology, Behaviour, and Social
Networking.

2 Following the widespread proliferation of social media services.

digital media as defined in this study, an attempt to determine the
correlation between, specifically, media multitasking and measures
for academic performance.

The sample of collected literature consisted of 35 (n ¼ 35) peer-
reviewed, primary studies matching the inclusion criteria specified
(see Appendix A). Looking at the source location of these studies 11
appear in Computers in Human Behavior, eight in Computers & Ed-
ucation, one in Cyberspychology, Behaviour, and Social Networking
and 15 in other journals and conference proceedings. 20 of the
studies sampled adopt a correlational methodology and 15 adopt
an experimental methodology. 20 of the 35 studies were published
within the last five years (from 2017), with the remaining 15 studies
being published between 2008 and 2012 (inclusive).

Following the construction of the literature sample, the sample
characteristics as well as the findings reported in each study were
collected and analysed. If a particular subject area was mentioned it
was classified accordingly. If no subject area was mentioned the
study was classified as ‘not specified’. Finally, if multiple subject
areas were mentioned the study was classified as ‘multiple’. Table 1
presents an overview of the literature sample in terms of the
number of studies reporting the specific subject area of the par-
ticipants, as well as aggregates of the direction of correlations with
academic performance described in such studies. Upon analysis of
the literature, it can be observed that the largest individual category
of subject area reported is Arts and Social Sciences, with all 12
studies identified in this category showing a significant negative
relationship between media multitasking and academic perfor-
mance. Interestingly, eight studies did not specify the subject area
of their sample, while ten studies covered multiple subject areas
without specifying the particular subject areas.

2.2. Conclusions

From this brief analysis we conclude that little or no attention
has been paid to variations in the relationship between media use
and academic performance across multiple subject areas. Specif-
ically, it appears that most studies consider students from social
science programs or fail to specify subject area. In studies where
students from a wider range of subject areas participated, varia-
tions in results were mostly not reported. Finally, when multiple
study areas exist within the sample, the interplay between this
factor and other outcomes achieved are not explored. One excep-
tion in this regard is Gaudreau et al. (2014) who compare academic
performance outcomes to specific subject areas, finding no signif-
icant difference. However, Gaudreau et al. (2014, p. 246) concede
that their samples were not entirely representative of students
across faculties.

We propose that subject area may moderate the relationship
between media multitasking and academic performance. This
proposition is based on two premises. The first concerns the notion
that different subject areas tend to attract students with different
thinking styles (concrete/abstract or linear/non-linear) (Chen &
Yan, 2016; Osterman, 2015; Saleh, 2001). We contend that such
differences may influence the utilisation of cognitive control
functions such as working memory and attentional control which,
as has been found (Chan, Shum, Toulopoulou, & Chen, 2008; Pollard
& Courage, 2017), will influence multitasking performance in an
individual. Our second premise concerns differences in the nature
of academic work performed in different subject areas. We propose
that the performance costs for learning resulting from media use
during lectures may be higher in some subject areas than others.
This effect can result, for example, from differences in the degree to
which learning requires the effective utilisation of a cognitive
process such as working memory. It follows, under this premise,
that the processes of knowledge acquisition in particular academic

Table 1
Correlational and experimental studies arranged by subject area.

SA Correlational Experimental Totals

Number DP ns Number DP ns

ASS 4 410, 23, 24, 26 8 84, 8, 9, 18, 19, 28,30,31 12
EMS 1 13 2 27, 17 3
EDU 1 15 1
N/A 6 61, 13, 14, 15, 25, 29 2 234, 35 8
Multiple 9 76, 11, 16, 20, 21, 22, 27 212, 33 2 22, 32 11
Totals 20 17 3 15 15

Note. SA¼ Subject Area, ASS ¼ Arts & Social Sciences, EMS ¼ Economic & Management Sciences, EDU ¼ Education, N/A ¼ the faculty was not specified DP ¼ Decreased
performance, ns ¼ no significant correlation found.
The numbers in superscript indicate the specific studies considered, details of which are available in the attached appendix.

D.B. le Roux, D.A. Parry / Computers in Human Behavior 77 (2017) 86e94 89

areas may more sensitive to interference than others.

3. Research design

To address the problem outlined in the preceding section we
pose the following research question:

Do statistically significant differences exist between the correla-
tions of in-lecture media multitasking and academic performance
amongst university students from different subject areas?

To address the question a web-based survey was designed to
collect data at a large, residential, South African university3. The
first section concerned subjects’ media use in general as well their
media use in structured academic contexts (i.e, lectures, practical
classes or tutorial classes). After investigating previous studies of
media use among South African students we identified six forms of
online media typically used by the target population. These
included:

1. Social Networks (SN)
2. Micro-blogs (MB)
3. Online encyclopedic (or structured data) browsing (ENC)
4. Instant Messaging (IM)
5. Search (engine) activities (SE)
6. The university’s e-learning platform (EL)

For each medium two Likert-type questions were used to elicit
frequency of use – one for general use and one for in-lecture use. For
general use five indicators were provided (“Not at all”; “Sometimes
(at least once per month)”; “Often (at least once per week)”; “At
most once a day”; “Multiple times per day”). For in-lecture use the
indicators were “Not at all”; “Once or twice”; “Every 10 min”;
“Every 5 min”; “Constantly”.

The survey also elicited demographic variables including age,
gender, language, highest qualification of parents and subject area.
Subject area was elicited by asking students to indicate, using a
drop-down list, in which of the university’s 10 faculties their degree
program was located. The university’s 10 faculties are:

1. Agricultural Science (AGR)
2. Arts and Social Science (ASS)
3. Economic and Management Science (EMS)
4. Education (EDU)
5. Engineering (ENG)
6. Law (LAW)
7. Medicine and Health Science (MHS)
8. Military Science (MIL)

3 The university is ranked inside the top 500 on the 2016/2017 Times Higher
Education World University Rankings Times Higher Education (2017).

9. Natural Science (NAT)
10. Theology (THE)

Finally, respondents were asked to report their general level of
academic performance in the previous academic year through a
series of indicators ranging, in 5% intervals, from “Below 40%” to
“96%e100%”4.

A single round of invitations to complete the survey was sent, by
e-mail, to 14 122 registered undergraduate students at the uni-
versity. Because subjects’ academic performance in the previous
academic year was elicited, first-year students were excluded from
the list of recipients. Completion of the survey was incentivised by
offering recipients a chance to win a ZAR 1000 (roughly USD 80) gift
voucher through a separately-managed lucky draw. Recipients of
the invitations were asked to provide informed consent to partic-
ipate in the study prior to completion of the survey. As part thereof
they were informed that their participation would be both volun-
tary and anonymous. A total of 1678 completed surveys were
submitted within a three-week period following the invitation. The
collected data was analysed using IBM SPSS Statistics version 24.

4. Findings

4.1. Overview of the sample population

Of the 1678 students which comprised the sample population
82% were between 20 and 23 years of age at the time of survey
completion, 7% did not disclose their age and the rest were older
(predominantly 24 or 25 years of age). 51% of respondents are fe-
male, 49% are male and two respondents indicated ”other”. In terms
of first language, 48% of respondents indicated Afrikaans, while 43%
indicated English. The remaining 10% indicated isiXhosa (2%), Zulu
(2%), Sepedi (1%), other African language (3%) or other European
language (2%). Just under 60% of respondents indicated that at least
one of their parents has a university qualification. This includes
Bachelor’s degrees (31%), Honours and/or Master’s degrees (25%)
and Doctorates (4%). 38% of respondents’ parents’ highest qualifi-
cation is a high school certificate.

Table 2 provides a summary of the subject areas of the sample
and the sizes of the populations5 they represent. The larger fac-
ulties including Engineering (n ¼ 402), Economic and Management
Science (n ¼ 353), Medical and Health Science (n ¼ 263), Arts and
Social Science (n ¼ 223) and Natural Science (n ¼ 196) were well
represented enabling margins of error of 6% or smaller for a 95%
confidence level. However, the smaller faculties were not well

4 Within the South African university system a mark of 50% represents a fail, and
a mark above 75% refers to a distinction.

5 Populations were calculated as all non-first year undergraduate students in
each faculty.

Table 2
Respondents per home faculty.

Faculty Male Count Female Count Total Count Percent Population MOE

ENG 310 92 402 24.0 1880 4%
EMS 188 170 353 21.0 3303 5%
MHS 60 212 263 15.7 1606 6%
ASS 68 155 223 13.3 2306 6%
NAT 88 107 196 11.7 1397 6%
AGR 58 49 107 6.4 987 9%
LAW 25 38 63 3.8 283 11%
EDU 9 31 40 2.4 653 15%
THE 11 8 19 1.1 153 21%
MIL 6 4 10 0.6 353 31%
Missing 2 0.1

Total 818 856 1678 100 100

Table 4
Frequency table for academic performance categories.

Category Range Frequency (Valid Percent)

All ASS EMS ENG MHS NAT

1 Below 40% 0.1 0 0.3 0.2 0 0
2 40e45% 0.4 0 0.6 0.7 0 0.5
3 46e50% 1.7 0 3.1 1.7 1.1 1.0
4 51e55% 14.0 7.7 20.2 19.2 6.8 10.7
5 56e60% 24.2 23.4 25.3 28.9 18.3 25.0
6 61- 65% 20 21.2 17.3 20.7 20.2 21.9
7 66- 70% 17 18.9 11.6 12.5 23.6 19.4
8 71e75% 13 17.1 12.2 8.2 18.3 12.8
9 76e80% 6.6 8.1 6.0 6.0 8.7 4.1
10 81e85% 2.2 3.2 2.6 1.5 2.7 3.1
11 86e90% 0.5 0 0.9 0.2 0.4 1.0
12 91e95% 0.1 0.5 0 0 0 0.5
13 96e100% 0 0 0 0 0 0

Mean 6.16 6.54 5.9 5.77 6.63 6.26

D.B. le Roux, D.A. Parry / Computers in Human Behavior 77 (2017) 86e9490

presented in the sample obtained and, for a 95% confidence level,
margins of error of 9% or greater were found for Agricultural Sci-
ence (n ¼ 107), Law (n ¼ 63), Education (n ¼ 40), Theology (n ¼ 19)
and Military Science (n ¼ 10). For this reason these faculties are
excluded in the remainder of the analysis.

4.2. Frequency of media use for different subject areas

To analyse media use frequency we calculated six use frequency
scales:

1. General use (GU) calculated as the aggregate of general use
across all six channels.

2. General social media use (GSM) calculated as the aggregate of
general use across social networks, instant messaging and mi-
cro-blogs.

3. General utilitarian use (GUT) calculated as the aggregate of
general use across encyclopedias, search engines and the e-
learning platform.

4. In-lecture use (LU) calculated as the aggregate of in-lecture use
across all six channels.

5. In-lecture social media use (LSM) calculated as the aggregate of
in-lecture use across social networks, instant messaging and
micro-blogs.

6. In-lecture utilitarian use (LUT) calculated as the aggregate of in-
lecture use across encyclopedias, search engines and the e-
learning platform.

The means and standard deviations of the six variables for each
of the subject areas are reported in Table 3. An analysis of variance
test (Oneway ANOVA) was performed to determine the variance
between the use variables between the faculties. For general use
(GU) the test revealed no significant variation between faculties,
F(4, 1432) ¼ 1.594, p ¼ 0.173. However, significant variation was
found for general social media use (GSM), F(4, 1432) ¼ 18.402,
p ¼ 0.000, general utilitarian media use (GUT), F(4, 1432) ¼ 13.238,
p ¼ 0.000, in-lecture use (LU), F(4, 1432) ¼ 6.212, p ¼ 0.000, in-

Table 3
Means and standard deviations for the six use frequency variables for the different
subject areas.

Faculty GU GSM GUT

M SD M SD M SD

All 23.73 2.62 11.52 1.94 12.20 1.
ASS 24.10 2.88 11.98 2 12.12 1.
EMS 23.77 2.43 11.75 1.79 12.02 1.
ENG 23.56 2.51 10.94 1.97 12.63 1.
MHS 23.84 2.79 11.95 1.75 11.89 1.
NAT 23.81 2.46 11.25 1.93 12.56 1.

lecture social media use (LSM), F(4, 1432) ¼ 4.215, p ¼ 0.002, and
in-lecture utilitarian media use (LUT), F(4,1432) ¼ 6.326, p ¼ 0.000.

To investigate these variations a post-hoc Tukey HSD test was
performed to compare the faculties based on each of the use vari-
ables. The data suggests statistically significant lower general social
media use among ENG students than students in MHS (p < 0.01) and students in ASS (p < 0.01). Conversely, ENG students use util- iterian media more frequently than their peers in ASS (p < 0.01), MHS (p < 0.01) and EMS (p < 0.01). Similarly, NAT students use utilitarian media significantly more than students in EMS (p < 0.01) and MHS (p < 0.01). Students in ASS have significantly higher levels of in-lecture media use, than their peers in NAT (p < 0.01), and MHS have significantly higher in-lecture media use than their peers in ENG (p < 0.05) and NAT (p < 0.01).

4.3. Media use and academic performance

Table 4 presents a frequency table of the academic performance

LU LSM LUT

M SD M SD M SD

63 11.69 3.35 6.17 2.11 5.52 1.97
69 12.16 3.91 6.42 2.39 5.74 2.29
51 11.87 3.18 6.44 2.2 5.43 1.65
72 11.40 2.44 5.98 1.94 5.42 1.04
89 12.25 3.62 6.34 2.21 5.91 2.23
44 10.97 3.08 5.65 1.88 5.32 1.87

Std. Deviation 1.69 1.59 1.8 1.63 1.56 1.67

Table 6
Significance of differences between correlations for in-lecture media use and aca-
demic performance calculated using the Fisher r-to-z transformation.

ASS EMS ENG MHS

EMS ¡3.18**
ENG ¡2.23* 1.17
MHS ¡2.00* 1.1 �0.05
NAT 1.28 �0.98 �0.7 �0.6

*. Difference is significant at the 0.05 level (2-tailed).
**. Difference is significant at the 0.01 level (2-tailed).

D.B. le Roux, D.A. Parry / Computers in Human Behavior 77 (2017) 86e94 91

categories. Within the South African higher education systems a
mark of greater than or equal to 50% is required to pass a module.
The mean (based on selected category) was 6.16 with a standard
deviation of 1.69.

Table 5 presents bivariate correlations (Spearman’s rho, two-
tailed) between the six use frequency variables for the full sam-
ple and each of the subject areas. For the full sample significant
negative correlation was found between in-lecture media use
across all media and academic performance (r ¼ �.055, p < 0.05). The correlation was slightly stronger and also significant (r ¼ �.081, p < 0.01) when only the use frequencies of the three social media, as presented by LSM, were considered.

For students in Arts and Social Science (n ¼ 222) negative corre-
lations with academic performance were found between general use
across all media (r ¼ �.143, p < 0.05), in-lecture use across all media (r ¼ �.239, p < 0.01), in-lecture use of social media (r ¼ �.174, p < 0.01) and in-lecture use of utilitarian media (r ¼ �.226, p < 0.01). For students in Engineering (n ¼ 401) negative correlations were found between academic performance and general use across all media (r ¼ �.113, p < 0.05) as well as general use of social media (r ¼ �.127, p < 0.05). For Medical and Health Science (n ¼ 263) general use of social media correlated negatively with academic performance (r ¼ �.123, p < 0.05) and for Natural Science (n ¼ 196) in-lecture use of social media correlated negatively with academic performance (r ¼ �.157, p < 0.05). For the faculty of Economic and Management Science (n ¼ 352) no significant correlations were found between any of the six use variables and academic performance.

Finally, to address our primary research question, we performed
the Fisher r-to-z transformation to determine the significance of
the differences between subject areas in terms of the correlations
found between in-lecture use (LU) and academic performance. The
results are shown in Table 6. Statistically significant differences
were found between ASS and EMS, ASS and ENG, as well as ASS and
MHS.

5. Discussion

Our findings suggest that the influence of general and in-lecture
media use on academic performance among university students
differs depending on subject area. Previous studies have relied
predominantly on data obtained for students in Social Science
programs and, as confirmed by our own data, there is evidence of a
negative correlation between in-lecture media use and academic
performance for this subject area. There is also evidence of weak
correlation between in-lecture social media use and academic
performance for students in Natural Science. However, this study
has found that in-lecture media use is not a predictor of academic
performance in other subject areas. In terms of general media use
we found that the use of social media is a predictor of academic
performance for students in Engineering and Medical and Health
Sciences.

From these findings emerges the question of why these

Table 5
Correlations between academic performance and media use.

Subject Area N GU GSM GUT LU LSM LUT

All 1672 �.029 �.031 �.013 ¡.055* ¡.081** �.013
ASS 222 ¡.143* �.096 �.112 ¡.239** ¡.174** ¡.226**
EMS 352 .046 .022 .064 .030 .014 .050
ENG 401 ¡.113* ¡.127* �.047 �.056 �.096 �.002
MHS 263 �.064 ¡.123* .026 �.060 �.113 �.008
NAT 196 .045 �.009 .080 �.117 ¡.157* �.054

**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

differences exist. More specifically, which aspects of a particular
subject area may influence the relationship between students’ level
of media use and their academic performance. Identification of
such aspects would be an important step towards understanding
the possible mechanism(s) by which media use may influence ac-
ademic performance. We briefly propose three (not mutually
exclusive) interpretations of our findings.

1. Perhaps the most obvious interpretation is that students from a
particular subject area demonstrate, as found by Chen and Ji
(2015) and Osterman (2015), a degree of similarity in terms of
thinking style. It should be acknowledged that, while this effect
may result from the preferences of students when selecting
their major subjects, thinking styles may also be nurtured by
nature of content and pedagogical styles of different subject
areas. This point is well articulated by Kolb (2014, p. 241) who
states that: “For students, education in an academic field is a
continuing process of selection and socialization to the pivotal
norms of the field governing criteria for truth and how it is to be
achieved, communicated and used, and secondarily, to periph-
eral norms governing personal styles, attitudes and social re-
lationships”. It follows, that our observation of differences
between subject areas may actually reflect differences between
various thinking styles, represented by the proxy variable of
faculty.
This interpretation suggests that thinking styles, through

their dictation of the utilisation of cognitive processes, impact
multitasking performance during the learning process. Some
thinking styles, accordingly, may be more or less resilient than
others to the costs associated with media use during lectures. In
the context of our findings this would suggest that the non-
linear, abstract thinking style generally associated with stu-
dents of the social sciences/humanities (Chen & Ji, 2015;
Osterman, 2015; Spink & Park, 2005) involves cognitive pro-
cesses which are more susceptible to the costs of interference
during learning.
Closely related to this interpretation is the argument that

students from different subject areas may demonstrate different
personality traits which, in turn, influence information seeking
behaviour. Previous studies suggest, firstly, that thinking style
and personality traits are related (Zhang, 2002) and, secondly,
that these traits influence information seeking behaviour
(Heinstrom, 2006). Heinstrom (2006) found that individuals
with an “energetic personality, high motivation, and positive
emotionality” are more likely to find useful or interesting in-
formation while not consciously looking for it (incidental
acquisition) (Heinstrom, 2006, p. 588). Our data does not justify
conclusions to be drawn in this regard, we suggest future
research to consider both thinking style and personality mea-
sures in relation to multitasking performance.

2. A second interpretation concerns the nature of the content be-
ing taught/learned as opposed to attributes of the learners. This

D.B. le Roux, D.A. Parry / Computers in Human Behavior 77 (2017) 86e9492

view considers the possibility that certain attributes of the
content being taught during a lecture make the effectiveness of
the learning process more/less susceptible to subjective media-
induced interferences. Our data suggest that media-induced
distractions may be more influential during the teaching of
content associated with social science subject matter than
during the teaching of other subject areas. The oft-used
distinction between soft and hard disciplines provides a high-
level indicator of the epistemological differences between sub-
ject areas. While hard disciplines are characterised by “the
quantitative nature of knowledge”, which requires numerical
calculation and experimental skills, the soft disciplines are
characterised as “free-ranging and qualitative, with knowledge-
building a formative process and teaching and learning activities
largely constructive and interpretative” (Neumann, Parry, &
Becher, 2002, p. 408). Our findings suggest that the effective
acquiring of knowledge during lectures in a soft discipline may
be more sensitive to media-induced interference.

3. Finally, the use of academic performance as dependent variable
implies that comparison across subject areas will be influenced
by the existence of differences in academic assessment policies
and norms between areas. Accordingly, the finding that social
science students’ performance correlates negatively with in-
lecture media use may suggest that assessment methods used
in this subject area are more sensitive to students’ receptiveness
during lectures as opposed to, for example, their time spent
preparing for tests. This is, of course, reflected in the assessment
styles adopted by different disciplines. Neumann et al. (2002, p.
408) found that “science-based subjects are more likely to uti-
lise assessment tasks that emphasise the acquisition of knowl-
edge blocks in a cumulative process, whereas in the humanities
and social sciences, together with the social professions (soft
pure and soft applied fields), assessment tasks emphasise
knowledge application and integration, usually in essay or
explanatory form”. Unlike the assessment of, for example, a
mathematical calculation that enables objective verification of
correctness, the assessment of essays is, in principle, influenced
by assessor subjectivity. One may expect, accordingly, a degree
of correspondence between the assessor’s interpretation of
content, implicitly communicated through his/her teaching, and
the assessment standards applied. This correspondence, in turn,
would be reflected in association between attentiveness during
lectures and academic performance.

Our findings imply that the relationship between media multi-
tasking and academic performance may be more nuanced than
suggested by earlier work on this topic. The bias towards students
of the social sciences observed in previously published studies may
have obscured the complex interplay between the nature of
knowledge, the cognitive processes by which it becomes acquired
and assessed, and the role of media induced interferences in these
processes.

Educators should be mindful in their interpretation of our
findings. Specifically, we do not reject Kirschner and De Bruyckere
(2017)’s argument that members of the net generation have no
particular aptitude for multitasking. Our data do not provide suf-
ficient evidence that media use during lectures only impact the
learning performance of students in arts and social sciences, while
their peers in other subjects are not susceptible to these effects. We
propose, rather, that attention be paid to the role that subjective
attributes like thinking style and personality type play in deter-
mining learners’ sensitivity to interference. In addition, we
encourage careful attention to the role of the nature of content in a
particular discipline and the implication thereof for learning.

From a research perspective we believe more studies are

required that investigate the underlying cognitive processes that
result from media-induced interferences. We suggest that future
research be undertaken to investigate, firstly, whether the subject
area differences reported here also exist at other learning in-
stitutions. Secondly, we call for studies to test the validity of the
three interpretations we propose by considering thinking style,
nature of content and forms of assessment as independent
variables.

Finally, we have to acknowledge a number of limitations in our
research design. The first is that all our data, including respondents’
academic performance, are self-reported. Secondly, the use of
students’ home faculty as a proxy variable for subject area may
imply an oversimplification of the construct. The use of faculty
assumes a degree of correspondence in the nature of academic
work performed in programs and courses presented by a particular
faculty. We acknowledge, however, that some overlap exists in this
regard. More textured data (e.g., academic performance in partic-
ular courses) may provide better insight into how the nature of
content moderates the correlation between media use and aca-
demic performance.

Funding sources

This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.

Appendix A

Articles used in the subject area review.

1. Bellur, S., Nowak, K. L., Hull, K. S., 2015. Make it our time: In
class multitaskers have lower academic performance. Com-
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2. Bowman, L. L., Levine, L. E., Waite, B. M., Gendron, M., may
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of instant messaging while reading. Computers & Education
54 (4), 927e931

3. Clayson, D. E., Haley, D. A., 2012. An Introduction to Multi-
tasking and Texting: Prevalence and Impact on Grades and
GPA in Marketing Classes. Journal of Marketing Education 35
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4. Dietz, S., Henrich, C., jul 2014. Texting as a distraction to
learning in college students. Computers in Human Behavior
36, 163e167

5. Dindar, M., Akbulut, Y., 2016. Effects of multitasking on
retention and topic interest. Learning and Instruction 41,
94e105

6. Duncan, D. K., Hoekstra, A. R., & Wilcox, B. R., 2012. Digital
devices, distraction, and student performance: Does in-class
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7. Ellis, Y., Daniels, B., Jauregui, A., 2010. The effect of multi-
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Research in Higher Education Journal 8, 1

8. End, C. M., Worthman, S., Mathews, M. B., Wetterau, K., 2010.
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9. Fox, A. B., Rosen, J., Crawford, M., 2009. Distractions, dis-
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their laptops and does it really matter? Computers & Edu-
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14. Junco, R., 2012a. In-class multitasking and academic perfor-
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15. Junco, R., 2012b. Too much face and not enough books: The
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16. Karpinski, A. C., Kirschner, P. A., Ozer, I., Mellott, J. A., Ochwo,
P., may 2013. An exploration of social networking site use,
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States and European university students. Computers in Hu-
man Behavior 29 (3), 1182e1192

17. Kraushaar, J. M., Novak, D. C., 2010. Examining the affects of
student multitasking with laptops during the lecture. Journal
of Information Systems Education 21 (2), 241

18. Kuznekoff, J., Titsworth, S., 2013. The Impact of Mobile Phone
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19. Lawson, D., Henderson, B. B., 2015. The Costs of Texting in the
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20. Lau, W. W., 2017. Effects of social media usage and social
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21. Lepp, A., Barkley, J. E., Karpinski, A. C., 2013. The relationship
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22. Leysens, J.-L., le Roux, D. B., Parry, D. A., 2016. Can I have your
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24. Patterson, M. C., 2016. A Naturalistic Investigation of Media
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26. Ravizza, S. M., Hambrick, D. Z., Fenn, K. M., 2014. Non-
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In-lecture media use and academic performance: Does subject area matter?

1. Introduction
1.1. Media multitasking and academic performance
2. Media multitasking and academic performance in different subject areas
2.1. Subject area analysis
2.2. Conclusions
3. Research design
4. Findings
4.1. Overview of the sample population
4.2. Frequency of media use for different subject areas
4.3. Media use and academic performance
5. Discussion
Funding sources
Appendix A
References

Active Learning in Higher Education
2016, Vol. 17(3) 235 –247

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DOI: 10.1177/1469787416654798

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What’s used and what’s useful?
Exploring digital technology
use(s) among taught
postgraduate students

Michael Henderson
Monash University, Australia

Glenn Finger
Griffith University, Australia

Neil Selwyn
Monash University, Australia

Abstract
This article explores the digital technologies that taught postgraduate students engage with during their
studies, what these technologies are used for and how useful they are perceived to be. The article draws
upon data gathered from a survey of 253 masters and postgraduate diploma/certificate students across
two universities in Australia. Analysis of these data contrasts the varied use(fulness) of ‘official’ university
technologies such as learning management systems and library resources against ‘unofficial’ technologies such
as Wikipedia, Twitter, Facebook and free/open education resources. In particular, the data highlight notable
differences between students by subject area, domicile, mode of study and academic performance. The data
also highlight the perceived benefits of this technology use – with students primarily finding digital technology
useful in terms of supporting the logistics of university study rather than matters of learning per se. The article
concludes by considering what is missing from these current forms of technological engagement, particularly
in comparison with wider discourses about the educational potential of recent digital technologies.

Keywords
digital technology, Internet, students, technology-enabled learning, university, usefulness

Digital technology and the student experience

Universities now offer all manner of Masters qualifications, postgraduate diplomas and postgradu-
ate certificate programmes. However, students on taught postgraduate courses are considered

Corresponding author:
Michael Henderson, Faculty of Education, Monash University, Wellington Road, Clayton, Melbourne, VIC 3800, Australia.
Email: Michael.henderson@monash.edu

654798ALH0010.1177/1469787416654798Active Learning in Higher EducationHenderson et al.
research-article2016

Article

mailto:michael.henderson@monash.edu

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236 Active Learning in Higher Education 17(3)

rarely in the academic literatures on higher education and/or educational technology. As Masterman
and Shuyska (2012: 335) observe, taught postgraduate students remain ‘a comparatively under-
represented population’ in ongoing discussions and debates about the future of higher education in
the digital age. These authors suggest a number of possible reasons for this oversight. For example,
it might reasonably be presumed that taught postgraduates are capable and confident individuals
who have already successfully negotiated undergraduate study. There might be less institutional
imperative to research the postgraduate student experience due to the relatively modest income
streams that result from postgraduate enrolment and/or the diminished status of some courses.
Also, from the perspective of investigating ‘cutting-edge’ aspects of technology use, older post-
graduates are perhaps less compelling research subjects than late-teen ‘digital natives’ embarking
on undergraduate courses.

Yet the digital educational experiences of taught postgraduate students need to be seen as a distinct
and important aspect of contemporary digital higher education. In numerical terms, postgraduate
taught students make up a sizable proportion of students on (and off) university campuses. In the
United Kingdom, for example, there are 427,000 taught postgraduates pursuing a variety of courses
– most sizably in business, medicine and education-related subjects (Universities UK, 2014). These
headline figures belie a diverse student population. For example, around half of taught postgraduates
study on a part-time basis, nearly 60% are female, two-thirds are aged 25 years or more and over one-
third are from outside the United Kingdom (Universities UK, 2014). Similar figures can be found in
other Anglophone higher education systems, such as the United States, Canada and Australia.

The nature of postgraduate taught courses and the experiences of students enrolled in them
clearly need to be considered separately from undergraduate education – particularly when it
comes to making sense of the continuing digitization of teaching and learning. Of course, as with
undergraduate provision, postgraduate courses are built around engagement with institutional
resources such as ‘learning management systems’, e-journals and plagiarism detection tools.
Similarly, all postgraduate students are now expected to make extensive individual use of word-
processing, email, Google and other popular web applications. Yet perhaps more than undergradu-
ate provision, elements of postgraduate teaching that relied traditionally on part-time and/or
distance modes of provision are now making extensive use of online technologies. As such, use of
digital technology is both a potentially radical and wholly routine feature of the contemporary
taught postgraduate experience.

To date, then, it is unfortunate that only a few studies have explored the everyday digital tech-
nology experiences of taught postgraduates. One exception was Masterman and Shuyska’s (2012)
small study of 23 Masters students at the University of Oxford. This research found that while not
possessing greater functional competence with technology, most students were developing skills
throughout their courses in accessing and evaluating online information and communicating via
technology. This study also highlighted the conscious choices made by these students not to engage
with technology – often reflecting the tension between the time pressure of intensified postgradu-
ate courses with the iterative process of gradually becoming more confident or even expert users
of a broad range of tools. Another pertinent investigation was Gourlay and Oliver’s study of 12
postgraduate students at the Institute of Education in the United Kingdom which highlighted a
number of characteristics of the contemporary postgraduate experience (Gourlay, 2014; Gourlay
and Oliver, 2013). These included students’ ‘constant entanglement’ with digital devices and, if
follows, the ever-present option of engaging in digital study. This was leading some postgraduates
to develop intimate and personal routines of studying (e.g. in bedrooms, bathrooms and on the
move). Crucially, this study uncovered a variety of student engagement with digital technologies
– including ‘surfers’, ‘gamblers’ and ‘sceptics’. Clearly, it makes little sense to presume that all
postgraduate students are making use of the same digital technologies in the same ways.

Henderson et al. 237

This latter point chimes with issues and tensions recurrent within the broader literature on tech-
nology and learning within university education. On one hand, there is plenty of evidence for the
potential of digital technology to support and sustain forms of active learning. Networked digital
technologies have undoubtedly transformed the generation and communication of knowledge and,
it follows, the ways in which learning and understanding take place (DeSchryver, 2015). The
potential of digital technologies to support or even ‘enhance’ learning has therefore been discussed
in terms of every significant development in digital technology over the past 20 years or so.
Recently, this has involved discussions over the educational benefits of podcasting (Dale and
Pymm, 2009), blogs and micro-blogs (Ebner et al., 2010), social networking sites (Brady et al.,
2010) and other forms of online social networks (Veletsianos and Navarrete, 2012). There has been
much written about the ways in which digital technology can support collective, creative and con-
nected forms of learning and study (e.g. Buzzetto-More, 2012; Sharpe and Beetham, 2010). New
technologies are widely seen to support students in the co-creation of knowledge with peers,
engagement in interest-driven informal learning practices, and the personalized engagement with
education on an ‘anytime, anyplace, any pace’ basis. This journal has featured a number of such
discussions and investigations. Bollinger and Armier’s (2013) study of the integration of student-
generated audio files into online courses demonstrated how this technology stimulated student
engagement and involvement in learning activities, as well as enhanced peer communication and
interaction. Similarly, Prestridge’s (2014) study of Twitter use in university teaching highlighted
the learning gains that can be achieved through the paraphrased interactions and dialogue that
constitute microblogging. Now similar claims are beginning to feature in emerging discussions
surrounding new ‘new’ technologies such as three-dimensional (3D) printing, augmented reality
and learning analytics (Bower et al., 2014; Drake and Pawlina, 2014; Siemens, 2013). Digital tech-
nology is clearly associated with much ongoing educational enthusiasm.

On the other hand, concerns remain over the less spectacular realities of digital technology use
within university teaching and learning (e.g. Losh, 2014). While many commentators may like to
imagine collaborative communities of content creators, in reality many undergraduate students’
engagement with technology is often far more passive, solitary, sporadic and unspectacular, be it on
or off-campus (Kennedy et al., 2010; Yılmaz et al., 2015). Undergraduates have been found to be
surprisingly ineffective in their use of the Internet and other research tools. As Jones’ (2012) ‘Net
Generation’ study concluded, students were found to report varying levels of digital confidence and
skills, often reporting ‘initial surprise or confusion at the array of technologies that were available’
(n.p.). As an exploratory study of university students’ use of social networking sites concluded, there
is a need for educators to ‘proceed[] with caution when using technology-enhanced learning, to
avoid over-generalizing the needs of the so-called Gen Y students’ (Lichy, 2012: 101).

Against this background, there is a need to add to the existing (predominantly qualitative)
research base on taught postgraduate students and digital technologies and to address the following
questions:

1. How are taught postgraduate students most commonly engaging with digital technologies
during their university studies?

2. In what ways – and to what extent – are different forms of digital engagement seen by
taught postgraduates as useful?

Throughout the investigation of these two broad areas of questioning, particular interest will be
paid to issues of patterning, specifically to the commonalities and differences between groups of
taught postgraduate students (e.g. subject disciplines, different types of course and modes of study,
gender, educational attainment, domicile status, cultural and linguistic diversity etc.).

238 Active Learning in Higher Education 17(3)

Table 1. Survey respondents by individual characteristics (n=253).

n

Percentage

Gender Female 172 71.7
Male 68 28.3

University University A (SE Australia) 150 59.3
University B (NE Australia) 103 40.7

Level of
study

Taught masters 131 51.8
Postgraduate certificate/diploma 122 48.2

Subject area Medicine (and allied subjects) 86 34.3
Business 39 15.5
Education 50 19.9
STEM subjects (sciences, technology, engineering, maths) 23 9.2
Other non-STEM (social sciences, humanities, arts) 53 21.1

Mode of
study

Full-time study 146 60.3
Part-time study 96 39.7

Academic
performance

High distinction 43 19.3
Distinction 107 48.0
Credit 62 27.8
Pass (or lower) 11 4.9

Domicile
status

Domestic students 187 77.3
International students 55 22.7

Employment
status

Working in paid employment while studying 175 72.3
Not working in paid employment while studying 67 27.7

First
language

English as first language 185 76.4
Language other than English as a first language 57 23.6

Disability Student with no declared disability 232 96.7
Student with declared disability 8 3.3

Some totals do not add up to 253 due to differing completion rates for each item.

Research methods

These questions are addressed through an analysis of survey data collected as part of research funded
by the Australian Government Office of Learning and Teaching. Data were collected during the
2014 academic year from students of two large Australian universities situated in the southeast and
northeast of Australia. These universities were of similar size, each with multiple campuses provid-
ing a breadth of courses at undergraduate and postgraduate levels with academic and vocational
orientations. Taught postgraduate students in both institutions were invited to complete an online
questionnaire containing items investigating their engagement with digital technologies. The self-
selecting sample of those students who chose to respond consisted of 253 students with an age range
of 21–69 (mean age = 33.3, standard deviation (SD) = 10.6) years. As can be seen in Table 1, the
sample was within expectation in terms of academic performance, mode of study, domicile status
and cultural and linguistic diversity. However, there was an over-representation of female students
(72% in our sample, but only 56% of University A’s overall intake and 58% of University B’s over-
all intake). There was also a slight over-representation of students taking medicine and related sub-
jects (19% of overall taught postgraduate intake in University A and 36% of overall taught
postgraduate intake in University B).

Data from the survey relating to students’ reported types and levels of digital technology use
relating to their studies are examined, as well as perceptions relating to the usefulness of this

Henderson et al. 239

technology use. Analysis of the survey data was conducted in a relatively straightforward manner,
acknowledging the limitations of the self-selecting, non-randomized nature of the sample and the
lack of complete measurement of all cases in the selected sample (De Vaus, 2002; Gorard, 2014).
Thus, in light of the growing trend for the ‘appropriate use of numbers’ within educational research,
the analysis of the data set therefore takes the form of frequencies and cross-tabulations (see also
Gorard, 2006). Similarly, analysis of the textual data arising from the open-ended items related to
the perceived usefulness of digital technology took the form of relatively straightforward thematic
analysis. This involved initial readings of all responses to the open-ended survey items to gain an
overall sense of the data. These data were then read again and ‘open-coded’ to produce an initial
code list until, in the opinion of the research team, analysis had reached theoretical saturation.
Although some codes were adapted which directly used the language of the respondents, the major-
ity were researcher-led and analytic. From this basis, the data were then coded in terms of catego-
ries identified with the initial code list directly related to the aims of the study.

Results

Students’ reported uses of digital technologies

As can be seen in Figure 1, survey respondents reported using a variety of digital devices. Nearly
every respondent reported using a personally owned laptop/desktop computer, with most (92.6%)
making use of a personally owned computer for their university studies. The survey also suggested
that smartphones were being used increasingly to support students’ academic studies (68.9% of
respondents who used a smartphone had used it in relation to university studies). Conversely, tab-
lets/iPads were used by fewer students (61.7%), although 80.2% of students who owned one
reported making use of their tablet/iPad for university work.

While (non)use of these technologies was generally comparable across the sample, a few spe-
cific differences were apparent. For example, use of university-provided computers was most
prevalent among students taking science, technology, engineering and mathematics (STEM) sub-
jects (83.3%) as compared, for example, to Medicine (33.3%) or non-STEM subjects (42.9%). Use

Figure 1. Students’ reported use of digital devices during the previous 4 weeks.

240 Active Learning in Higher Education 17(3)

Table 2. Students’ use of ‘official’ digital technology resources in relation to their university studies, and
the perceived usefulness of these in supporting academic work.

Used as part of
university studies

Reported as
‘Very Useful’

Use library online resources to find information 99.6 73.9
Library website 97.4 48.2
Learning Management System 97.8 56.0
E-books or e-textbooks 84.3 39.7
Other university websites 78.0 13.6
Software specific to my study area 42.5

20.6

Simulations or educational games 46.5 16.0

Data are percentage of sample responding to each survey item.

of university computers was also the preserve of international/overseas students (73.5% as com-
pared to 37.0% of home/domestic students).

Of course, these general levels of digital device use need to be seen in light of what these
devices were being used for. Here, the distinction can be made between ‘official’ digital resources
and practices (i.e. those provided and/or mandated by universities) and ‘unofficial’ digital resources
and practices (i.e. resources that were not part of university-provided systems and services). In
terms of ‘official’ digital resources, the survey data confirmed the prominence of online library
resources and learning management systems (see Table 2). These data also highlight the growing
use of e-books and e-textbooks (reported by 84.3% of respondents). Use of all these ‘official’
resources was remarkably consistent throughout the sample, regardless of subject discipline, mode
of study or other individual characteristics measured in the survey.

More variation was apparent, however, with regard to use of what could be classed as ‘non-
official’ digital resources. As can be seen in Table 3, nearly all respondents reported making use of
general Internet search engines (such as Google) and specialized academic search services (such as
Google Scholar and Web of Science). Other prevalent practices included viewing subject-related
content on video sharing websites such as YouTube and finding information related to their univer-
sity studies on Wikipedia.

The extent of students’ engagement in these ‘non-official’ practices differed across the sample.
For example, in terms of subject-related differences, students studying STEM subjects made nota-
bly more use of a range of different technologies. For example, 83.3% of STEM students reported
making use of free/open resources and content, as compared to just over half of students in each of
the other subject areas. Subject-specific software was used by 77.8% of STEM students (as com-
pared, for example, with 34.0% of Education students). STEM students also reported notably
higher levels of social media use related to their academic studies. For example, 64.7% of STEM
students reported using Twitter (compared to 31.3% of non-STEM students) and 88.3% using
Facebook for collaboration with other students (compared with 57.1% of non-STEM students).
The one unofficial technology use not dominated by STEM students was the use of web-based
documents to collaborate with other students. Here, Business students were most likely to be col-
laborating with other students through collaborative Wiki/Google Docs (87.5%) as compared, for
example, with students following non-STEM students (46.9%).

Aside from subject areas, other differences across the sample were less consistent. For example,
levels of Wikipedia use were highest for students achieving ‘High Distinction’ grades (95.2%), as
opposed to Distinction grades (85.0%), Credit grades (81.8%) or Pass grades (77.8%). This pattern

Henderson et al. 241

was reversed for Facebook and open/free resources, where use was more prevalent among students
achieving lower grades. Elsewhere, international/overseas students were more likely to be using
social media than domestic/home students, that is, Twitter (used by 62.5% of international and
41.0% of domestic students), Facebook (89.6% as opposed to 63.3%) and collaborative Wiki/
Google Docs (79.2% as opposed to 58.7%).

Students’ perceived usefulness of digital technologies

The survey data also explored students’ perceived usefulness of these digital resources and prac-
tices. As Tables 2 and 3 show, among the most ‘useful’ digital resources were using general Internet
search engines (such as Google), making use of university Learning Management Systems and
using library online resources. Also rated highly were academic search services such as Google
Scholar and Web of Knowledge and viewing subject-related videos on content sharing websites
such as YouTube.

The perceived usefulness of these technologies varied across the survey sample. Again, the
most prominent differences were found in terms of respondents’ subject of study. For example,
students enrolled on Business courses were most likely to report Wikipedia (34.6%) and collabora-
tive Wiki/Google Docs (53.6%) as ‘very useful’. Facebook was reported as ‘very useful’ by 32.7%
of medicine students as compared, for example, to 13.3% of STEM students. Conversely, Twitter
was reported as ‘very useful’ by 13.3% of students enrolled on non-STEM courses, in comparison
with 2.8% of medicine students and none of the respondents enrolled on STEM courses.

Alongside these subject-related differences, respondents studying on a fulltime basis were more
likely to perceive e-books and e-textbooks as ‘very useful’ (47.5% of fulltime students as com-
pared to 27.0% of part-time students). Similar patterning was apparent with Wikipedia (reported as
‘very useful’ by 31.9% of fulltime and 13.2% of part-time respondents) and Facebook (34.5%,
8.9%). Elsewhere, female students were notably more likely to perceive collaboration with peers

Table 3. Students’ use of ‘non-official’ digital technology resources in relation to their university studies,
and the perceived usefulness of these in enhancing learning.

Used as part of
university studies
Reported as
‘Very Useful’

Use Internet search engines to find information 100.0 69.2
Search for papers/journals on non-university provided
scholarly websites (e.g. Google Scholar, Web of Science)

95.6 53.7

Watch or listen to audio recordings or videos about your
subject/discipline (e.g. YouTube, Vimeo)

91.6 40.4

Finding information through Wikipedia 85.7 24.5
Web-based citation/bibliography tools 79.9 36.1
Use social networking sites for working with other students
on your courses (e.g. Facebook)

68.9 27.1

Use web-based document for working with other students on
your courses (e.g. Google Docs, Wikispaces)

63.0 32.7

Freely available courses and educational content from outside
of my university (e.g. i-Tunes U, Khan Academy, OERs)

57.4 22.0

Twitter 45.6 6.8

OERs: Open Educational Resources.
Data are percentage of sample responding to each survey item.

242 Active Learning in Higher Education 17(3)

through Facebook as ‘very useful’ (33.9%) as opposed to male students (9.1%). However, this was
the only clear gender-related difference across the data set.

The nature of the use and usefulness of students’ digital practices

In order to further contextualize these data, a concluding open-ended section of the survey asked
respondents to nominate and justify the digital resources and practices that they found to be ‘most
useful’ during their university studies. From the nominated examples, 10 distinct digital practices
were identified and coded. These data provide further insight into what taught postgraduate stu-
dents were using digital technologies for and the meanings that students attached to these prac-
tices. As can be seen in Table 4, the most prominent practices related to the logistics of university
study, that is, organizing schedules and fulfilling course requirements, being able to engage with
university studies on a ‘remote’ and/or mobile basis and the broad issue of managing time and
time-saving.

Tellingly, practices explicitly related to learning were reported less frequently. The most promi-
nent learning-related practice was using digital technologies to ‘research information’. While less
frequently reported, ‘reviewing, replaying and revising’ digitally recorded learning materials –
most notably the viewing and listening of class recordings also featured in the data, as did the
practice of ‘looking elsewhere’ for supplementary materials to corroborate or clarify what had been
learnt at university. All these logistic and learning ‘benefits’ were cited consistently across the
sample, with little recurrent patterning between different groups.

Discussion

These data present a mixed picture of postgraduate students’ engagements with digital technology.
On one hand, digital technology is clearly an integral element of the contemporary taught post-
graduate experience – particularly mandated ‘official’ digital systems and services such as online
library resources and learning management systems. Clearly, digital technologies are an essential
element of the core academic practices of researching and retrieving information, preparing and
producing assignments, reports and other forms of coursework. It is now difficult to imagine being
a university student without these technologies. Beyond these ‘basics’, our data also highlighted
the widespread practice of engaging with teaching and learning materials in video form. Alongside
recordings of their own university lectures, many students were also turning to video sharing web-
sites such as YouTube to find external video content to supplement their studies. This clearly marks
an additional way that digital technologies have found a place in the everyday practices of post-
graduate life.

On the other hand, the take-up of other technologies often talked about in terms of the digitiza-
tion of higher education appears to be more varied and inconsistent. For example, notwithstanding
their high public profile, iPads and other tablet computers did not appear to be core academic tools
for the majority of students. In addition, while Facebook was clearly a commonly used communi-
cation tool, other ‘big name’ social media were less prevalent. For example, Twitter was not
reported as a prevalent part of university studies. While nearly half the sample reported trying to
use Twitter for academic purposes, only 16.5% of these students found it ‘useful’ or ‘very useful’.
Similarly, while the majority of students used social media sites such as YouTube, contrary to
popular assumptions in the academic literature, it was for the passive consumption of content
rather than creation or participation practices. Also, students’ use of Wikipedia was not the ubiqui-
tous practice that is often assumed, with our data showing variations by subject of study and aca-
demic performance in students’ use of Wikipedia as an information source for their academic work.

Henderson et al. 243

Indeed, our data suggest that taught postgraduates’ digital technology use was patterned along
distinct lines. In particular were differences between subject areas and the basis of students’
candidature, that is, whether they were domestic or international and/or studying on a fulltime or
part-time basis. Such differences highlight that digital technology use is not a ubiquitous (or
even consistent) presence among what is a diverse and divergent current postgraduate student
population.

In making sense of these findings, it is perhaps helpful to view taught postgraduates’ engage-
ment with digital technologies from the two different perspectives that emerged from the open-
ended survey data – what was classed as ‘logistics’ and ‘learning’. The logistical aspects of study
refer to the day-to-day ‘work’ of being a taught postgraduate student. In this sense, much of the

Table 4. Cited reasons for digital technology usefulness in relation to students’ university studies.

Practice Description Digital devices/practices most
cited in relation to this factor

Percentage

Organizing
and managing
the logistics of
studying

Managing schedules and timetables;
fulfilling deadlines and course
requirements; ‘keeping in the loop’
regarding university and course
information and news

Learning management system
as repository of resources
and information

47.0

Flexibility
of place and
location

Flexibility of location; ability to engage
‘remotely’ with academic work off-
campus; engaging at a distance and not
having to be ‘present’; being able to be
mobile, portability of university work

Library databases and library
websites; laptop computers

45.1

Researching
information

Researching information for assignments;
quantity and quality of information access

Library databases and library
websites

32.8

Time-saving Saving student time; quicker processes;
more immediate outcomes; convenient
scheduling of activities

Writing notes/word-
processing; library databases
and library websites; online
assignment submission

31.6

Supporting
basic tasks

‘Easier’ writing of assignments; ‘easier’
and ‘helpful’ information management and
retrieval of resources

Writing notes/word-
processing; general Internet
search engines (e.g. Google)

20.6

Reviewing,
replaying and
revising

Catching up on missed material;
repeating viewing of materials to improve
understanding

Recordings (audio/video) of
university classes and lectures

16.6

Communicating
and
collaborating

Asking questions and exchanging
information; working with other
students; sharing ideas; preparing group
work

Facebook and other
social networks; Google
Docs, wikis, collaborative
documents

16.2

Augmenting
university
learning
materials

Watching lectures, tutorials and talks
from outside university; cross-checking
and comparing with other sources; ‘going
elsewhere’

Watching videos from
sources outside university;
Wikipedia

12.3

Seeing
information in
different ways

Visualizing concepts through video,
animation or annotations; allowing real-
time lecturer demonstrations and ‘board
work’ in lectures

Watching videos from
sources outside university

7.5

Cost saving Saving money and expenditure E-readers, online journals and
books

5.9

244 Active Learning in Higher Education 17(3)

engagement with digital technologies reported here could be seen as related to students’ pragmatic
negotiation of their work, that is, the immediate demands of postgraduate study that continue to be
centred on issues of assignments, grades and (non)attendance. The digital technologies that were
most prominent in our data were those that fit closely with the immediate logistical realities of
postgraduate student life, such as the pressures of class attendance and scheduling, participating in
mandatory group activities, reserving library materials, conducting ‘research’ in the form of locat-
ing and retrieving documents, as well as producing and submitting assignments. Thus, it is these
aspects of taught postgraduate courses that come to the fore when students choose (or are com-
pelled) to engage with digital technology.

Alongside this logistical engagement with digital technology comes the use of digital technolo-
gies for learning. Here, much of how digital technologies were being used, and perceived as being
useful, appeared to be shaped by dominant university models of the passive consumption of learn-
ing, rather than any more fluid, networked, connected or creative-driven forms of learning.
Regardless of subject specialization, academic achievement or mode of study, technology-based
learning appeared to take the primary forms of the passive reception of information and instruction,
coupled with the largely individualized practices of researching and producing assignments.

At best, these data suggest that digital technologies are allowing taught postgraduate students to
pursue these modes of learning that are more convenient but not necessarily innovative. The ability
to watch and re-watch videos from around the world or the ability to search vast online databases
of scholarly literature is a clearly valued practice by taught postgraduate students, and therefore
presumably of benefit to them. Nevertheless, most of the dominant digital practices in the day-to-
day lives of the taught postgraduates appear to be those that conform to (and reinforce) instructivist
notions of content, knowledge, pedagogy and learning. If anything, then, these students’ experi-
ences of ‘technology-enhanced learning’ can be described most accurately as involving the passive
consumption of knowledge rather than more active, connected and/or creative practices. This is not
to say that postgraduate scholarship is being ‘dumbed down’ or devalued through digital technol-
ogy use, but neither is it being notably transformed or revolutionized.

Clearly, this study provides only an initial glimpse at what is a complex area of higher educa-
tion. Our data are limited by the self-report nature of the survey method and the self-selecting
nature of the sample. In focusing on the differences in students’ levels of engagement and percep-
tions of usefulness, we have not examined the quality or effectiveness of this engagement. Nor
have we questioned how digital resources fit alongside students’ use of non-digital educational
resources (e.g. books, face-to-face lectures and meetings). Therefore, it is clear that students’ actual
uses and non-uses of the digital technologies during postgraduate studies merit a sustained and far-
reaching programme of future research.

One key issue for this future research would be to explore how the restricted forms of digital
technology use highlighted here ‘fit’ with students’ non-academic engagements with technology
– especially social media applications such as Facebook, Wikipedia and Twitter. The suggestions
of differences along subject area and domestic/international basis also merit closer scrutiny.
Similarly, research should be conducted along more longitudinal lines than the ‘snap-shot’ nature
of the present data set. Repeated collection of data from cohorts of students as they progress
through their taught postgraduate studies would provide a rich and detailed picture of the factors
underlying the take-up of digital-based resources. It is hoped that the study described here has been
able to provide a starting point for such investigation.

Perhaps the key conclusion to be drawn from our data, then, is what is missing from these cur-
rent forms of technological engagement – particularly in comparison with what is known about the

Henderson et al. 245

educational potential of digital technologies. In this sense, universities clearly are not brimming
with cohorts of taught postgraduate students who are making extensive and imaginative use of
digital technology during their studies. While a few of our survey respondents did report participat-
ing in Massive Open Online Courses (MOOCs), using Tumblr and generally conforming to the
description of ‘power users’, far more appeared to fit the more prosaic categories of what Kennedy
et al. (2010) term as ‘ordinary users’ or even ‘basic users’. This is not to say that postgraduate
students are incapable of using technologies in expansive ways. However, they clearly do not per-
ceive the need to do so.

This suggests a number of possible implications for educators in postgraduate education to
consider – not least how the digital technology practices of these students might be extended and
expanded. Our data suggest that the more expansive forms of technology-based learning often
celebrated in discussions of educational technology are clearly not occurring spontaneously.
Instead, postgraduate students’ academic uses of technologies could be seen to follow the largely
restrictive expectations that continue to pervade higher education with regard to knowledge, the
development of what constitutes knowledge and understanding, and what counts as postgraduate
learning, study and scholarship (see Selwyn, 2014). As such, perhaps the main implication of the
data presented in this article is that current discussion of ‘digital higher education’, ‘twenty-first
century learning’ and the like needs to be balanced by a renewed attention to the non-digital struc-
tures and contexts of higher education. Our findings certainly support arguments for reorienting the
content of syllabi, nature of assessment and forms of engagement and learning more forcefully and
unambiguously around technology-based practices.

Also, it seems clear that teachers, tutors and other university staff members have key active
roles to play in stimulating, supporting and sustaining ‘best practice’ uses of the technologies that
do not appear to currently be core elements of the taught postgraduate experience. Although these
students are successful, experienced university learners, this does not mean that they will autono-
mously engage with new learning opportunities. Instead, these are students who could well benefit
from increased teacher orchestration and co-ordination of technology-based education. In this
sense, university educators need to be reminded that they play an important role in influencing and
supporting learners’ ‘self-directed’ digital activities (see Littlejohn et al., 2013). Therefore, educa-
tors working on postgraduate courses need to provide an initial impetus for the collaborative activi-
ties that underpin much contemporary technology-based learning. As Crook puts it, teachers at all
levels of education play a key role in ‘arranging the furniture’ of technology-based learning – pro-
viding a ‘good core’ and ‘initial governance and impetus’ to extend student use of digital technol-
ogy beyond the routine completion of coursework and fulfilling other course requirements. Digital
technology is clearly an important element of postgraduate education. However, more work is
required from within universities if taught postgraduate provision is to be genuinely enhanced
along digital lines.

Acknowledgements

The authors would like to thank the other members of the research team: Rachel Aston, Kevin Larkin and
Vicky Smart.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publi-
cation of this article.

246 Active Learning in Higher Education 17(3)

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publica-
tion of this article: This paper arises from a research project funded by the Australian Government Office of
Learning and Teaching (award number SP13-3243).

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Author biographies

Michael Henderson is an Associate Professor in the Faculty of Education, Monash University, Australia. His
expertise lies in the use of Internet-based technologies and in teaching and learning in schools and higher
education contexts. Recent work has focused on the areas of instructional design and pedagogy in online
learning; the role of identity in mediating teacher pedagogy and student learning; and teaching and learning
with social networks. He is the co-editor of Teaching and digital technologies (Cambridge University Press,
2016). Address: Faculty of Education, Monash University, Wellington Road, Clayton, Melbourne, VIC 3800,
Australia. [email: michael.henderson@monash.edu]

Glenn Finger is a Professor in the Arts, Education and Law Group at Griffith University, Australia. He has
extensively researched, published, and provided consultancies in creating transformational stories of the use
of Information and Communication Technologies (ICT) to enhance learning. He is the lead author of
Transforming Learning with ICT: Making IT Happen (Pearson, 2007). Address: Arts, Education & Law
Group, Griffith University, Gold Coast campus, Griffith University, QLD 4222, Australia. [email: g.finger@
griffith.edu.au]

Neil Selwyn is a Professor in the Faculty of Education, Monash University, Australia. His research and teach-
ing focuses on the place of digital media in everyday life and the sociology of technology (non)use in educa-
tional settings. Recent and forthcoming books include Is Technology Good for Education? (Polity, 2016) and
Distrusting Educational Technology (Routledge, 2014). Address: Faculty of Education, Monash University,
Wellington Road, Clayton, Melbourne, VIC 3800, Australia. [email: neil.selwyn@monash.edu]

mailto:michael.henderson@monash.edu

mailto:g.finger@griffith.edu.au

mailto:neil.selwyn@monash.edu

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