The impact of media multitasking on learning

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DOI: 10.1080/17439884.2010.537664
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Abstract
While multitasking is not a new concept, it has received increasing attention in recent years with the development of new media and technologies. Recent trends appear to suggest that multitasking is on the rise among the younger generation. The purpose of the study is to determine if students obtain more or less information in multitasking conditions. We examined the relationships of multitasking to attention, cognitive load and media with 130 college student participants. In this study, participants were given a timed (16 minutes) reading comprehension test in three conditions: Silence (only reading), Background multitasking (reading with a non-tested video shown simultaneously), and Test multitasking (reading with a tested video shown simultaneously) conditions. Our findings indicated that: (1) participants in the Background condition performed as well as those in the Silence condition, and (2) when participants were tested on their video comprehension, the group in the Test condition performed significantly better than the group in the Background condition. The results of this study suggest that cognitive load plays an important role in determining how much information is retained when students perform more than one task at a time.
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The impact of media multitasking on
learning
Jennifer Lee a , Lin Lin a & Tip Robertson a
a Department of Learning Technologies, College of Information,
University of North Texas, Denton, Texas, USA
Available online: 29 Jun 2011
To cite this article: Jennifer Lee, Lin Lin & Tip Robertson (2011): The impact of media multitasking
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Learning, Media and Technology
2011, 1–11, iFirst article
ISSN 1743-9884 print/ISSN 1743-9892 online
© 2011 Taylor & Francis
DOI: 10.1080/17439884.2010.537664
http://www.informaworld.com
The impact of media multitasking on learning
Jennifer Lee*, Lin Lin and Tip Robertson
Department of Learning Technologies, College of Information, University of North
Texas, Denton, Texas, USA
Taylor and FrancisCJEM_A_537664.sgm
(Received 23 September 2009; accepted 2 November 2010)
10.1080/17439884.2010.537664Learning, Media and Technology1743-9884 (print)/1743-9892 (online)Original Article2011Taylor & Francis00000000002011JenniferLeeJennifer.Lee@unt.edu
While multitasking is not a new concept, it has received increasing
attention in recent years with the development of new media and
technologies. Recent trends appear to suggest that multitasking is on the
rise among the younger generation. The purpose of the study is to
determine if students obtain more or less information in multitasking
conditions. We examined the relationships of multitasking to attention,
cognitive load and media with 130 college student participants. In this
study, participants were given a timed (16 minutes) reading comprehension
test in three conditions: Silence (only reading), Background multitasking
(reading with a non-tested video shown simultaneously), and Test
multitasking (reading with a tested video shown simultaneously)
conditions. Our findings indicated that: (1) participants in the Background
condition performed as well as those in the Silence condition, and (2) when
participants were tested on their video comprehension, the group in the Test
condition performed significantly better than the group in the Background
condition. The results of this study suggest that cognitive load plays an
important role in determining how much information is retained when
students perform more than one task at a time.
Keywords: multitasking; cognitive load theory; schema construction;
interactivity; learning
Introduction
In libraries and classrooms across the many college campuses, it is common
to see students performing multiple tasks at the same time while completing
their assignments or studying for a test. Some claim that multitasking does not
interfere with students’ studying habits (Jenkins et al. 2006; Prensky 2006;
Small and Vorgan 2009). ‘Millennials’ might even argue that multitasking
actually helps them to concentrate (Roberts, Foehr, and Rideout 2005). Others
believe that we cannot perform more than one task at a time. Since multitask-
ing with technology is a fairly recent phenomenon, researchers are still trying
to determine its impacts on learning. Gardner (2008, 3) notes that multitasking
*Corresponding author. Email: jennifer.lee@unt.edu
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2 J. Lee et al.
is ‘an area of concern to educators, technology leaders, instructional designers
and consumers as it impacts the media environment and shapes the way media
is consumed.’ This is because multitasking does not fit into our current
understanding of how our brains function in a task-rich and time-sensitive
environment.
Media multitasking
Foehr (2006, 2) observes that, ‘much of the multitasking young people do
revolves around media use.’ Vega (2009, 3) defines media multitasking as
‘engaging in multiple media activities simultaneously, including multiple
windows on a single media platform and/or multiple media.’ According to the
Kaiser Family Foundation (2010), 8- to 18-year-olds in the United States
spend 7.5 hours on media daily. The researchers found that young people
packed a total of 10 hours and 45 minutes worth of content media into 7.5
hours of media use.
Young teens seem to embrace multitasking as a way of life (Foehr 2006).
For example, many teenagers send text messages throughout the day while they
are engaged in school and social activities. American teens sent an average of
3146 text messages a month in 2009 (Entner 2010). According to Madden and
Lenhart (2009), one in three teens between the ages of 16 and 17 admitted to
texting while driving. It is not surprisingly that ‘activities that require focused
attention, such as reading, are declining among American youth’ (Levine,
Waite, and Bowman 2007, 560). While society in general appears to have
embraced the necessity of multitasking, the cost of multitasking remains
unclear. Gardner (2008, 3) noted that the phenomenon of multitasking appears
to be counter-intuitive to the principles of information processing. Do we
acquire more or less information in a multitasking learning environment? Do
we learn better by focusing on one task at a time?
Cognitive load theory
Psychologists and neuroscientists have long been interested in the limits of
human information processing in terms of attention. The issue has generated
more interests recently because multitasking has dominated many facets of our
lives. We are particularly interested in the multitasking practice among the
younger generation. Foehr (2006, 2) observes that ‘although no research has
focused specifically on the effects of media multitasking on teens and on their
environment, conventional wisdom and brain research support the idea that
there are limits to how much our brains can process at once.’
In 1956, George Miller conducted various experiments on how accurately
people remembered numbers. Miller (1956, 95) found that his subjects gener-
ally remembered seven digits when tested on their working memory. He
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Learning, Media and Technology 3
believed that his subjects were limited in the amount of information that they
were ‘able to receive, process, and remember.’ Miller’s theory on how our
brains handled information paved the way for other researchers who were
interested in the limitations associated with human cognitive processing abili-
ties and the effects on learning. From 1960s to 1990s, various models of the
working memory were proposed including Atkinson and Shiffrin’s multi-store
model (1968), Baddeley and Hitch’s model of working memory (1975) and,
more recently, Sweller’s cognitive load theory (1988).
Cognitive load theory emerged in the instructional design community to
address the limitations of our cognitive processing abilities when it comes to
learning. Cognitive load is defined as a ‘multidimensional construct represent-
ing the load that performing a particular task imposes on the learner’s cogni-
tive system’ (Paas and van Merriënboer 1994, 122). Paas, Renkl and Sweller
(2003, 2) describe cognitive load theory (CLT) as focusing on ‘techniques for
managing working memory load in order to facilitate the changes in long-term
memory association with schema construction and automation.’
Cognitive load theorists believe that we store knowledge as schemas in our
long-term memory (Sweller, van Merriënboer, and Paas 1998; van Merriënboer
and Ayres 2006). Schema construction plays an important role in freeing up
the limited resources in our working memory. van Merriënboer and Ayres
(2006) suggest that we can reduce highly complex schemas to simpler elements
through practice and repetitions. Over time, familiarity with a task through
repetition helps lessen the cognitive load (Sweller, van Merriënboer, and Paas
1998, 252–3). On the other hand, learning a new task has the opposite effect.
It places additional load on the working memory.
We employed two assumptions from the CLT to design experiments that
allowed us to investigate whether students obtain more or less information in
multitasking conditions. First, CLT assumes that we can only process a limited
number of elements in our working memory. Second, every task generates a
cognitive cost on the working memory in terms of cognitive load. Sweller
(1988) believes that problems that require ‘a large number of items to be stored
in short-term memory may contribute to an excessive cognitive load’ (265).
A complex task places a heavier cognitive load on the working memory
than a simple task. When multiple tasks compete for the same resources, we
strain the limits of our working memory.
Sweller, van Merriënboer and Paas (1998) noted that the ease with which
information is processed is a primary concern for working memory. There are
three types of cognitive load that affect learning: extraneous, germane and
intrinsic (Paas, Renkl, and Sweller, 2003, 2). Extraneous cognitive load inter-
feres with learning since it places additional burden on the working memory
that does not contribute to knowledge acquisition. Intrinsic cognitive load is a
part of the learning material or activity that cannot be altered. It is the amount
of working memory required for the learner to interpret the learning material
or activity that is presented. Paas, Renkl and Sweller (2003, 2) describes
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4 J. Lee et al.
intrinsic cognitive load as ‘a base load that is irreducible’ and it gets allocated
before the other two categories of cognitive load.
Sweller, van Merriënboer and Paas (1998) believe that germane cognitive
load contributes to the schema construction. It plays a critical role in the learn-
ing process. Paas, Renkl and Sweller (2003, 1) argue that, ‘information varies
on a continuum from low to high interactivity.’ Low interactivity elements are
‘materials that can be understood and learned individually without consider-
ation of any other elements.’ When we learn a new task, we have to process
the information consciously. For example, a teenage driver who is learning how
to drive a car behaves differently than an experienced driver. We expect the
young driver to focus his sole attention on driving and thus expend more cogni-
tive resources than an older driver with decades of experience. As we gain more
driving experience, driving becomes more automated and thereby allowing us
to use less cognitive resources.
Paas, Renkl and Sweller (2003) believe that we can learn high interactivity
elements individually. Unlike low interactivity elements, they argue that we
have to master high interactivity elements together to understand the informa-
tion. For example, air traffic controllers provide instructions to pilots, clear
flights, monitor flight conditions and track multiple flights at the same time.
While it is possible to understand the different elements of the job, traffic
controllers must master all the elements in order to ensure the safety of the
commercial and private planes.
Despite the growing body of research and attention on multitasking, studies
on how it impacts learning habits have been far and few in between. Fried
(2008) found that the level of laptop use in the classroom correlates negatively
to student learning and overall course performance. Levine, Waite and
Bowman (2007) reported that the amount of time students spent using instant
messages was significantly related to more distractibility for academic read-
ing, while amount of time spent reading books was negatively related to
distractibility. Kirschner and Karpinski (2010) found that students, who spent
more time on Facebook, have lower grade point averages than non-users. In
another study, Fox, Rosen and Crawford (2009) examined the reading
comprehension scores of students who were using instant messaging and read-
ing at the same time. They reported that participants who spent more time on
instant messaging scored lower on their reading scores.
Methods
Participants
We invited undergraduate students from eight courses in the College of
Education at a major southern university in the United States to participate in
the study. A total of 137 students volunteered to participate in the study.
Seven responses out of the 137 responses (137 participants) were not used
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Learning, Media and Technology 5
because of incomplete answers. Therefore, data analysis was based on
responses from 130 participants. The majority of the participants were female
(90.7%) while the rest (9.3%) were male students. The large number of the
female participants in the study represents proportionately the large number of
female students in the College of Education. The mean age of the participants
was 23.9 years.
Design and procedures
In our experiment, we selected two reading sets and developed questions
based on them. In each reading set, we selected one article on the subject of
science, history and politics. In the first reading set (Reading Set 1), we
included an article about dinosaur discovery (science), a civil war prisoner
camp (history) and the 2008 presidential nominee, Barack Obama (politics).
In the second reading set (Reading Set 2), we included an article on an astro-
nomical event (science), the prosecution of an American spy (history) and the
2008 presidential nominee, John McCain (politics). We used articles that were
of similar length, format and level of difficulty. The articles were college-level
texts that students would typically read in their core education courses.
We created six questions for each article with varying levels of difficulty
to accompany the text. We recruited a small group of students to read the arti-
cles and answer the questions before we used them the study. In addition to
the articles, we also selected two short videos for the study. The first video was
a documentary on drunk-driving, while the second video was a situational
comedy (sitcom). We edited both videos so that they were 16 minutes in
length and developed a set of questions for each video presentation.
In the study, we setup three conditions. The conditions were: (1) reading in
silence (Silence), (2) reading with informational video playing in the back-
ground (Background), and (3) reading with informational video playing that
contains testable information (Test). Table 1 describes the setup for each of the
three conditions.
Participants were randomly assigned two out of three experimental condi-
tions so that no one condition was favoured. Participants in Group A were
tested under two conditions: Silence and Background. Participants in Group B
were tested under Silence and Test conditions. In Group C, participants were
tested under Background and Test conditions. In each of the conditions, they
were instructed to read three articles and answer six questions at the end of
article. Those in the Background and Test conditions had to answer questions
related to the videos that were shown. Table 2 shows the configuration of the
experiments.
At the beginning of each session, we distributed the reading materials and
instructed the participants on what they needed to do. Each session contained
two experimental conditions (Table 2). We employed the same data collection
procedure for both experimental conditions.
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6 J. Lee et al.
First, we collected demographic information from them. Next, we gave the
participants Reading Set 1 or Reading Set 2 and asked them to answer 18 (six
for each article) multiple-choice questions related to the articles, under one of
the three conditions – Silence, video Background or video Test condition. If it
was a video condition, we asked the participants to answer six questions on the
video. At the end of each experiment, we debriefed the participants. The entire
procedure took approximately 50 minutes. The order of the set of reading
materials (Reading Set 1 or 2) was systematically randomized, as were the
order of the videos (documentary first, sitcom second, or vice versa) so as to
not privilege any one format.
Results and findings
We awarded participants three points if they answered a question correctly.
We deducted one point for an incorrect answer. We did not award any point
to questions that were unanswered. We timed the tests so that participants
would have to deal with the time constraint as a limiting factor. In each reading
Table 1. Experiment descriptions for the three conditions.
Condition Experiment description
Silence Participants were instructed to read and answer 18 multiple-choice
questions regarding the three articles.
Background Participants were instructed to read and answer 18 multiple-choice
questions regarding the three articles. In addition to the reading
task, the group was assigned a second task. The group watched a
video while they performed their reading tasks. A situational
comedy or a documentary was shown to the participants to mimic
a scenario where a student would read and watch TV or video at
the same time. The participants were told that they could ignore
the video if they chose to do so, although they were asked to
answer six questions related to the video afterwards.
Test Participants were instructed to read and answer 18 multiple-choice
questions regarding the three articles. A situational comedy or a
documentary was shown to this group. Unlike under the
Background condition, the participants were instructed that they
would be tested on the information related to the video.
Table 2. Number of participants by paired conditions.
Participants and session groupings Number of participants
Group A – Silence and Background 30
Group B – Silence and Test 35
Group C – Background and Test 65
Total 130
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Learning, Media and Technology 7
set, participants could score up to 54 points with the three articles (a total of
18 questions). If a video was shown, an additional 18 points (six questions for
a video) could be scored. We only compared participants’ reading comprehen-
sion performance under three different conditions. Scores for the video condi-
tion were excluded to prevent score inflation for that group. Table 3 provides
a summary of how participants performed under each condition.
We used paired t-tests to determine if the mean scores of the reading tests
given under each of the three conditions differed significantly. First, we found
that there was no significant difference in reading scores for students who
were in the Silence and Background conditions (Group A). There did not
appear to be any significant difference in the participant scores in the Silence
and Background conditions, t(29) = 0.611, p > 0.05.
Next, we compared the scores for participants who were in Group B
(Silence and Test conditions). There did not appear to be any significant
difference in the participant scores in the Silence and Test conditions, t(34) =
1.142, p > 0.05.
Lastly, when we compared how participants scored in the Background
condition against the Test condition (Group C), there was a significant differ-
ence in the reading scores of the two groups. We found that participants scored
better in reading comprehension under the Background condition (M = 13.55,
SE = 0.448) than in the Test condition (M = 12.48, SE = 0.388), t(64) = 2.168,
p < 0.05, r = 0.26.
Discussion
In a multigenerational study conducted by Carrier et al. (2009), the researchers
found that Millennials (born between 1982 and 2001) were spending more
time than Generation X (born between 1965 and 1976) and Baby Boomers
(born between 1946–1964) on media-related activities like web surfing,
texting and video games. Millennials were more likely to multitask compared
with the previous generations. This phenomenon has led some researchers to
suggest that the brains of our current generation are adapting to the technology
revolution in ways that are different than Baby Boomers and Generation X
(Small and Vorgan 2009).
Whether we agree or disagree with the idea that the technology has trans-
formed the brains of our youths, one pivotal issue has emerged: How do we
Table 3. Paired samples’ t-test.
Paired conditions MSD tdf Sig
Group A – Silence and Background 0.333 2.987 0.611 29 0.546
Group B – Silence and Test 0.914 4.736 1.142 34 0.261
Group C – Background and Test 1.077 4.005 2.168 64 0.034
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8 J. Lee et al.
teach a generation of technologically savvy students that has a distinct prefer-
ence for media multitasking? If anything, the innocuous sight of students surfing
the web on their laptops with music playing in the background while working
on their assignments appears to reinforce the notion that we can do many things
at the same time. However, the findings in this study paint a more complex
picture of the multitasking behaviour in the learning environments.
Our findings support the argument that we retain less information when we
perform more than one task at a time. Participants in the Silence condition
performed significantly better than the participants in the Test condition.
Researchers have long argued that our ability to perform simultaneous tasks is
limited (Broadbent 1957; Pashler 1994; Fisch 2000; Lang 2001; Kirschner and
Karpinski 2010). Kirschner and Karpinski (2010) believe that we can only
perform multiple tasks when these tasks are automated. Tasks that require
focussed attention like studying suffer when students engaged in other activities
at the same time.
We expected similar results in the Silence versus Background conditions.
Interestingly, the reverse was true. There was no significant difference in read-
ing comprehension scores among participants in the Silence condition and
Background condition. More recently, some researchers have argued that new
media and technology have changed the way we retain and process informa-
tion (Small and Vorgan 2009). Small and Vorgan (2009, 25) believe that ‘the
bombardment of digital stimulation on developing minds has taught them to
respond faster, but they encode information differently than the older minds
do.’ Technology may very well be the catalyst in changing our ability to
handle multiple source of information.
When we compared the reading scores of those in the Background–Test
condition, we found that participants scored better in the Background condition
rather than the Test condition. We hypothesize that the Test condition carried
a greater cognitive load than the Background condition. As a result of higher
cognitive load, participants likely used greater cognitive resources in order to
perform the primary and secondary tasks simultaneously. The increased
consumption of cognitive resources may have attributed to the lower reading
test scores for those in the Test condition. In other words, the performance of
the primary task (reading) suffered when the secondary task required higher
cognitive load than just casual attention. This finding is consistent with the
work of Yeung, Jin and Sweller (1998) who found that extraneous cognitive
load can and do interfere with learning.
How does this impact academic performance, which we are interested in?
Sweller, van Merriënboer and Paas (1998) believe that low-element interactivity
tasks consume less cognitive capital than high-element interactivity tasks
because they involve fewer elements in working memory. Based on our findings,
we hypothesize that multitasking is possible when a low-element interactivity
task is coupled with another low-element interactivity task or a high-interactivity
task with a low-interactivity task (as in Background multitasking condition). It
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Learning, Media and Technology 9
is far easier for a student to listen to a lecture with low interactivity elements
in the background (soft music or chatting) as opposed to sitting in one with high
interactivity elements (solving complex mathematical equations at the same
time). However, these configurations need to be further tested in future studies
before any claim of effectiveness can be levied.
One of the limitations of the study was the fact that our participants were
recruited from the pre-service teacher population. We cannot generalize the
findings to a broader population without replicating the study with other
groups of students. Since over 90% of the participants were females, future
studies should include a more balanced gender representation because of
possible gender differences in multitasking habits. Moreover, in real life,
students are free to select their preferred media while completing a reading
activity assigned by their instructors. In contrast, the media selection for this
study was predetermined.
Conclusion
In the study, we examined if students acquired more or less information in a
multitasking learning environment. Our findings have important implications
for students and educators alike. We believe that multitasking interferes with
knowledge acquisition. It generates extraneous cognitive load that burdens the
working memory. Students perform better when they focus on one task at a
time especially when they are learning new materials inside and outside the
classroom. For students with a strong preference for multitasking outside the
classroom, coupling media use with activities that are considered low interac-
tive elements may help reduce extraneous cognitive load. Educators should
consider limiting student media use when introducing new materials in class.
There is an unquestionable (and even urgent) need for more studies to be
conducted as we deal with the impact of multitasking habits on our society.
This research gap is especially critical as schools and colleges find ways to
work with students whose multitasking behaviours are both voluntary and
prized.
Notes on contributors
Jennifer Lee is a doctoral candidate at the Department of Learning Technologies,
University of North Texas. Her research interests include distributed learning, new
media and technologies, and the scholarship of teaching and learning.
Lin Lin is an Assistant Professor in the Department of Learning Technologies,
University of North Texas. Her research interests include instructional technology,
cognition, psychology, and new media.
Tip Robertson recently received his PhD from the Department of Learning Technolo-
gies, University of North Texas. His research interests include individual and team
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10 J. Lee et al.
performance improvement, dyadic and small-group relationships, and the cognitive
and learning processes of young adults, particularly within the context of work/life
and school/life balance.
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