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Advances in Social Sciences Research Journal – Vol. 10, No. 5
Publication Date: May 25, 2023
DOI:10.14738/assrj.105.14692. Bui, N. H., Cruickshank, M., McAloon, J., & Maguire, J. (2023) The Effects of TV Viewing, Computer and Gaming Console Use on
Academic Achievement: A Longitudinal Study. Advances in Social Sciences Research Journal, 10(5).210-225.
Services for Science and Education – United Kingdom
The Effects of TV Viewing, Computer and Gaming Console Use on
Academic Achievement: A Longitudinal Study
Nghi Hoang Bui
University of Technology Sydney
ORCID 0000-0002-4969-7908
Marilyn Cruickshank
University of Technology Sydney
John McAloon
University of Technology Sydney
Jane Maguire
University of Technology Sydney
ABSTRACT
Background: Early learning experiences are crucial for children’s optimal
development. The rising prevalence of screen time (ST) in children’s lives have
raised concerns regarding their effect on academic outcomes (Gingold et al., 2013).
Aim: This study used a longitudinal design to examine the effects of the amount of
ST on academic achievement. Method and Sample: The “Growing Up in Australia: A
Longitudinal Study in Australia (LSAC)” (Soloff et al., 2005) was the primary data
source for this research and focused on children from this cohort who were 4-5
years of age on enrolment (N=2954). The National Assessment Program for Literacy
and Numeracy (NAPLAN) was used to measure five academic domains: reading,
writing, numeracy, spelling and grammar/punctuation at two time points. Results:
Increased ST at Time 1 (T1) demonstrated a negative association with Year 3
NAPLAN scores in reading (B= -5.94), writing (B= -3.08), numeracy (B= -4.85),
spelling (B= -4.03), and grammar/punctuation (B= -5.74). Increased ST at Time 2
(T2) demonstrated a significant negative association with only Year 5 NAPLAN
scores in grammar/punctuation (B= -2.72). ST at T1 predicted lower scores on the
Year 5 NAPLAN in reading (B= -1.67), writing (B= -2.36), and grammar/punctuation
(B= -1.96). This effect was not observed for numeracy and spelling. Conclusions:
Overall, these findings illustrate that more ST leads to a decreased performance in
academic achievement over time, although this was not present across all domains.
These findings may have implications for educational settings, as technology is
increasingly utilised for educational purposes.
Keywords: screen time, media exposure, academic achievement, learning, academic
performance
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Bui, N. H., Cruickshank, M., McAloon, J., & Maguire, J. (2023) The Effects of TV Viewing, Computer and Gaming Console Use on Academic
Achievement: A Longitudinal Study. Advances in Social Sciences Research Journal, 10(5).210-225.
URL: http://dx.doi.org/10.14738/assrj.105.14692
INTRODUCTION
During the early developmental years, the immature brain is vulnerable as it rapidly develops,
and behavioural habits ingrained early in life have an impact on later development (Knudsen,
2004). The effects of behaviours such as prolonged screen time (ST) has been widely
researched and is recognised as a predictor of academic achievement (Poulain et al., 2018). ST
refers to time spent viewing electronic media, such as television (TV), computers, video game
consoles, smartphones, iPads or tablets (Yilmaz et al., 2015). The American Academy of
Pediatrics (2016a, 2016b) have recommended children aged 2-5 years old spend an average of
one hour per day on screens, and for older children to spend up to two hours per day, and this
is similar to guidelines suggested by the Australian Government (Department of Health, 2014).
Therefore, children engaging in more than two hours of ST per day is considered excessive in
accordance to these health guidelines for this age group. Poulain et al. (2018) suggested that
differences in children’s education and career opportunities become prominent in a child’s
early development. Studies have shown that increased ST, particularly during critical
developmental periods, may have subsequent effects on social interactions with family and
peers, regulation of emotion, behaviour, academic achievement and adaptive functioning
(Thomas & Knowland, 2009). However, the current evidence is ambiguous regarding the effect
of early ST use on later academic achievement in children particularly during primary school.
The effects of ST is particularly important for young children, as there is rapid development and
learning during the early years of life (Knudsen, 2004). Researchers have posited that children
become active viewers of TV by preschool age compared to when they are infants and toddlers
(Kirkorian et al., 2008). Preschool children pay less attention to perceptually salient features,
and are able to attend to informative features including dialogue and narrative (Huston &
Wright, 1983). Engaging with TV inhibits attentional processes and increases impulsive
behaviour, due to the fact that TV utilises frequent movements and rapid images (Anderson et
al., 2001). It is possible that this behaviour, when translated to the classroom, could present as
difficulties with concentration and staying on task.
Primary school-aged children are attracted to ST due to its visually stimulating and auditory
effects in comparison to their school-related tasks that require active intellectual stimulation
(Dumuid et al., 2017; Shin, 2004). ST, in particular to watching movies/TV shows or engaging
in games, may require less mental effort compared to learning activities, such as reading. One
hypothesis is that as a child’s attention to screens increases, the less likely they are to put the
mental effort into acquiring academic skills (Shin, 2004). Children will choose to engage in ST,
as it is visually entertaining and easy to understand. Overall, it appears that children have a
preference towards any amount of ST as it requires less mental effort and consequently affects
important attention processes and influences children’s ability to engage with academic
learning.
Time displacement theory proposes that time spent on screens may reduce time spent on
intellectually demanding activities, such as homework, studying, or reading for leisure (Shin,
2004; Syväoja et al., 2013) and lead to poor academic achievement. Sufficient sleep is known to
facilitate a healthy lifestyle and optimise academic achievement, and time on screens may affect
time spent sleeping (Faught et al., 2017; Syväoja et al., 2013). ST prior to sleep is linked to
increased arousal and disrupted melatonin production, and affects attention and concentration
levels, which are critical for engagement with learning (Kubota et al., 2002).
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There is limited work examining the impact of the relationship between ST on academic
achievement within the under six age group and the majority of studies examine adolescent
cohorts despite the early childhood to primary school period being a more sensitive time for
learning and development (Thomas & Knowland, 2009). In current prospective studies that
focused on adolescent cohorts (Johnson et al., 2007; Nelson et al., 2006; Poulain et al., 2018),
greater TV viewing was associated with poor homework completion, lower grades, negative
attitudes towards school and long-term academic failure (Johnson et al., 2007). Nelson et al.
(2004) and Poulain et al. (2018) confirmed these findings, and further considered other
lifestyle behaviours, such as sleep and physical activity.
Interestingly, one longitudinal study followed children from five years to 19 years of age and
found that increased TV viewing of educational content at age five was associated with higher
grades in adolescence for boys. This trend was also identified in girls, however it was not a
statistically significant finding (Anderson et al., 2001). In contrast, girls who were exposed to
violent content on TV at age five were associated with lower grades in adolescence, where no
significant findings were observed for boys. It appears that ST that is informative, age- appropriate, and educational may facilitate learning and academic success, compared to ST that
is purely for entertainment purposes or contains violent content (Anderson et al., 2001;
Kirkorian et al., 2008). However, discrepant findings were observed and further highlighting
the inconsistency of results.
Most of the previous longitudinal studies have measured academic achievement through
grades rather than a standardised assessment tool (Anderson et al., 2001; Johnson et al., 2007;
Nelson et al., 2006; Poulain et al., 2018). In addition, Faught et al. (2017) conducted a
prospective study of primary school-aged children and showed that children who met ST health
recommendations were more likely to perform at an expected age level for writing exams.
Grasby and Coventry (2016) highlight the societal importance of the acquisition of both literacy
and numeracy skills. As academic achievement encompasses a variety of domains, such as
numeracy, writing, reading, spelling and language, it is unclear what the effects of ST is on such
domains.
The evidence investigating the relationship between ST and academic achievement is
conflicting and often derived from analytic cross-sectional studies. For instance, researchers
have reported that increased engagement with ST disrupts academic activities, and has
negative consequences for academic achievement (Aguilar et al., 2015; Howie et al., 2020;
Sharif & Sargent, 2006). In a systematic review of 232 studies, the authors identified 35 studies
that focused on the relation between sedentary behaviour, referred to as range of behaviours
with low energy expenditure performance (e.g. sitting, watching TV, playing video games), and
academic performance (Tremblay et al., 2011). Within this subset, 32 of the 35 studies were
cross-sectional and the remaining three studies were longitudinal, and only one study
examined children under six years of age. The majority of cross-sectional studies reported that
school-aged children exposed to more than two hours of ST daily, were more likely to perform
poorly academically, however 10 of these studies did not report a significant relationship.
Moreover, the longitudinal studies found that children watching more than an hour of TV daily
was associated with attention difficulties in their adolescence. In this review, the definition of
academic achievement varied across studies and was not well harmonised. These findings
highlight the discrepancy between studies and the need for longitudinally designed studies
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Bui, N. H., Cruickshank, M., McAloon, J., & Maguire, J. (2023) The Effects of TV Viewing, Computer and Gaming Console Use on Academic
Achievement: A Longitudinal Study. Advances in Social Sciences Research Journal, 10(5).210-225.
URL: http://dx.doi.org/10.14738/assrj.105.14692
with consistent definitions of academic achievement to permit comparison of these effects
between studies and over time.
Further support for the ambiguity in the field concludes that ST has also shown positive
associations with academic achievement (Jackson et al., 2011; Syväoja et al., 2013) or had no
association at all (Munasib & Bhattacharya, 2010). Among these studies, there were differences
regarding the collection of data on ST including Jackson et al. (2011) who specifically measured
internet use and videogame playing, whereas Syväoja et al. (2013) had children self-report on
their ST and Munasib and Bhattacharya (2010) only collected data on TV. Interestingly, Jackson
et al. (2011) findings were only present among children who had below average reading skills.
Given these findings, further research would be useful to investigate the long-term effects of ST
on academic achievement. Another methodological concern in the literature is that the
grouping of ages of children vary between studies. Therefore, in this study reported here,
children aged 3-4 years old were considered as preschool-aged and those that were aged 4-5
years of age were referred to as primary school-aged, as it is the average age of entry for formal
schooling in Australia.
STUDY AIMS
This study used a longitudinal design in children aged 4-5 years of age to examine the effects of
the amount of ST, particular to TV viewing, computer and gaming console use, on academic
achievement as measured by the National Assessment Program for Literacy and Numeracy
(NAPLAN) on five domains. These domains included: reading, writing, numeracy, spelling and
grammar/punctuation.
It is hypothesised that:
1. Increased ST will significantly predict a medium-term negative effect on academic
achievement, such that ST at T1 will demonstrate lower scores on the Year 3 NAPLAN
and ST at T2 will demonstrate lower scores on Year 5 NAPLAN.
2. Increased ST will significantly predict a long-term negative effect on academic
achievement, such that ST at T1 will demonstrate lower scores on the Year 5 NAPLAN.
METHOD
Primary data was obtained from two large publicly available retrospective datasets. These two
datasets included: “Growing Up in Australia: A Longitudinal Study in Australia (LSAC)” conducted
by the Australian Government Department of Social Services, Australian Institute of Family
Studies and the Australian Bureau of Statistics (Soloff et al., 2005) and the National Assessment
Program for Literacy and Numeracy (NAPLAN). The study investigated the experiences of a
nationally representative sample of Australian children and their families at specific points
across their lifespan. The LSAC began in 2004 with two cohorts: B-cohort (i.e. birth cohort,
children born between March 2003 and February 2004) and K-cohort (i.e. kindergarten cohort,
children between 4-5 years old in 2004; Soloff et al., 2005). Participants were recruited from
the Medicare enrolment database and selected was based on a two-stage cluster sampling
(Soloff et al., 2005). Data was obtained from self-report questionnaires, and face-to-face or
telephone interviews with the child, their parents or carers, and their teachers (Soloff et al.,
2005). The NAPLAN is a standardised national curriculum assessment undertaken by
Australian students in Grade 3, 5, 7 and 9 (Australian Curriculum Assessment and Reporting
Authority, 2013).
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Participants
The present study investigated data from Waves 3 and 5 of the B-cohort (referred to as Time 1
and Time 2; T1 and T2). T1 refers to data collected in 2008-09 where children were 4-5 years
old, whereas T2 was collected in 2011-12 where the same children were 8-9 years old. The
LSAC data from Waves 3 and 5 was merged with academic achievement data from the NAPLAN.
Data from the Year 3 NAPLAN test was collected in 2011-13 and Year 5 results were collected
in 2013-15.
Initially, 3,714 children and their families were recruited. Participants who completed all or
partially completed the NAPLAN tests were retained for the final sample, where 750
participants were removed due to incomplete data on the NAPLAN. The final sample consisted
of 2,954 children and their families (Child: Mage = 4.25, SD = 0.44; Parent: Mage = 35.76, SD = 5.08).
The gender distribution was approximately equal for children (M=50.6%, F=49.4%), whereas
the majority of parents completing the survey were female (97.8%). The majority of parents
were born in Australia or New Zealand (82.7%), employed part-time (43.1%), and reported an
individual income of less than $25,999 per annum (53.1%) and the highest level of education
achieved was Certification (26.6%). Ethics approval was obtained from an Australian Ethics
Committee (Gray & Sanson, 2005). See Table 1 for demographic characteristics of the sample
at T1.
Table 1: Demographic characteristics of the sample at T1 collected in 2008-09
(N=2954)
Characteristics %(N)
Child Gender
Male
Female
50.6(1494)
49.4(1460)
Parent Gender
Male
Female
2.2(65)
97.8(2889)
Parent’s Country of Birth
Australia & New Zealand
Pacific Islands
North-West Europe
South and East Europe
Africa
Middle East
East Asia
South and Central Asia
The Americas
Missing
82.7(2442)
.6(19)
5(147)
.5(15)
.9(28)
1.1(32)
2.7(81)
1.6(47)
1.1(32)
3.8(111)
Highest Education Level of Parent
Certificate
Advanced diploma/diploma
Bachelor degree
Graduate diploma/certificate
Postgraduate degree
Other
Missing
26.6(787)
10.2(301)
22.2(657)
7.9(232)
8.1(239)
1.9(56)
23.1(682)