Screen time v/s SGPA: A Study
Faiz Khatib, Roll no. 149, UID: 212897
Aim: To study correlation between the screen time and SGPA across two semesters.
Rationale: According to popular opinion and the research supporting it, excess screen-time is seen to have
a negative impact on one’s academic performance. This plumet in academic performance may be linked to
the deterioration of one’s mental health due to excess screen-time and device usage. According to a
population based study in 2018, it was observed that increased hours of screentime was linked to effects
of lower psychological well-being, like decreased sense of curiosity, insomnia, distractibility, depression,
less emotional stability, etc. (1). These factors also would hinder with the academic study process of the
individual, thus invariably affecting their academic scores. A meta-analysis of 30 studies found that
students who spent more than two hours a day on screens, suffered a decline in academic achievement
owing to decrease in concentration (2). There is quite a lot of evidence gathered through research which
suggests that screen time and media usage has a significant impact on cognition, thus affecting academic
performance . This is because excessive screentime has dire effects on the neurobiology of our nervous
system. The blue light released from devices mimic daylight and thus suppresses the release of melatonin;
gaming and social media also desentesizes the brain’s reward dopamine system, causing one to use more
of the device for stimulation, thus paving way for addiction.
A study performed by National Institutes of Health revealed there to be a thinning in the brain’ cortex of
children with a screen-time of more than 7 hours per day, with the cortex playing a pivotal role in attention
span, concentration, learning, memory, language, etc. Thus, after careful literature review, we
hypothesised that the drop/rise in screen time per individual across two semesters would have an impact
on their SGPA’S.
Methodology: In our study, a sample of second year and third year students of St. Xaviers College, Mumbai
were asked to fill out a survey questionnaire. This questionnaire consist of 4 questions each for SY
students and TY students divided into two sets. SY participants were to enter their SGPA’s acquired in the
2nd and 3rd semester ESE, along with a rough estimate of their screen time during the two week period prior
to that ESE exam. TY participants on the other hand were asked to enter their SGPA’s acquired in the 3rd
and 4th semester ESE instead, and a rough estimate of their screen time during the two week period prior to
that ESE exam. The purpose of collecting data for two subsequent semesters from every participant was to
minimise variables like difference in IQ, social background, etc. that would’ve existed as factors affecting
the SGPA, had the comparison been drawn from different individuals. In order to make it easier for the
participants to enter a rough estimate, and also to streamline the data collected, the estimated screen time
was asked to be chosen from a set of specific time ranges, which were 1-3 hours, 4-6 hours, 7-9 hours and
10+ hours. This survey was taken sometime around 11 pm in the foyer and woods area of St Xaviers
College, Mumbai. Since we deliberately filtered only SY and TY students into our study out of all the
students present on campus, it was a non probability purposive sampling. Also since majority of our sample
size was primarily acquired from the woods area of our own college campus owing to ready and accessible
availability, the sampling done was also a convenient type of sampling. It is important to note that the
consent of each participant was taken before filling. A sample size of 65 individuals was gathered, who’s
data points were divided into two data sets, the first one being for the screen time and respective SGPA
acquired in semester 2 (SY) and semester 4 (TY) (exam given in 2022), and the second one being for
semester 3 (SY) and 5 (TY) (exam given in 2023). Each of these data sets were divided into 4 groups on the
basis of the 4 screen-time ranges slotted, with each group including the SGPA’s for that particular screen
time range; less than an hour (n1=0, n2=1), 1-3 hours (n1= 10, n2=12), 4-6 hours (n1=23, n2=33), 7-9 hours
(n1=12, n2=10), and 10+ hours (n1=8, n2=9) . A total of three ANOVA tests was performed on 3 data sets.
An ANOVA test using excel ad-ins was performed on the 4 aforementioned sets of each of the two data sets,
through which a statistical result was obtained. We calculated the difference in screen time between two data
sets and assorted those differences obtained into seven sets; difference= -4 (n=1), difference=-3 (n=9),
difference=-2 (n=1), difference=0 (where n= 47), difference=2 (n=1), difference=3 (n=5), difference= 5
(n=1). Each of these sets included the difference in SGPA’s linked to that difference in screen time for each
individual between the two semesters. A third ANOVA test using excel ad-in’s was then performed on these
7 sets for estimating f and p values, through which a statistical conclusion was derived.
Graphs obtained:
Results:
1) Data collected for 2nd and 4th semester from SY and Tys respectively:
(worksheet ANOVA set A result)
SUMMARY
Groups
1-3 hours
4-6 hours
7-9 hours
10+ hours
Count
10
23
12
8
ANOVA
Source of
Variation
Between Groups
Within Groups
SS
8.506494
197.2687
Total
205.7752
Sum
74.75
194.51
96.17
65.74
df
4
60
Average
7.475
8.456957
8.014167
8.2175
MS
2.126624
3.287811
Variance
3.382806
1.161068
3.466481
1.657307
F
0.64682
P-value
0.631298
F crit
2.525215
64
2) Data collected for 3nd and 5th semester from SY and Tys respectively:
SUMMARY
Groups
Less than 1
hour
1-3 hours
4-6 hours
7-9 hours
10+ hours
Count
1
12
33
10
9
Sum
Average
Variance
8
83.9
262.85
76.19
69.6
8
6.991667
7.965152
7.619
7.733333
#DIV/0!
6.172288
2.453782
3.205832
2.75
ANOVA
Source of
Variation
Between Groups
Within Groups
SS
8.506494
197.2687
Total
205.7752
df
4
60
64
MS
2.126624
3.287811
F
0.64682
P-value
0.631298
F crit
2.525215
3) Analysis of the difference in the SGPA and screen time over these semesters:
SUMMARY
Groups
Difference=-4
Difference=-3
Difference=-2
Difference=0
Difference=2
Difference=3
Difference=5
Count
1
9
1
47
1
5
1
ANOVA
Source of Variation
Between Groups
Within Groups
SS
10.20622
49.70416
Total
59.91038
Sum
1
-4.88
-3.17
-22.5
0
-3.81
-1
df
6
58
Average
1
-0.54222
-3.17
-0.47872
0
-0.762
-1
Variance
#DIV/0!
1.083869
#DIV/0!
0.767433
#DIV/0!
1.43282
#DIV/0!
MS
1.701036
0.856968
F
1.984946
P-value
0.082513
F crit
2.259605
64
Conclusion: Based on the results obtained, we see that the F critical is greater than the F calculated. The P
value is greater than 0.05 in all three ANOVA tests performed , and it indicates that there is no correlation
between screen time and SGPA. What we’d hypothesised at the beginning of this study after literature
review wasn’t obtained by the study, since difference in screen time didn’t have much of an impact on the
SGPA’s across two semesters in the sample size collected. This can be due to certain fallacies in the
technique of data collection, like for example depending on rough estimates as reliable data points for our
research. Replacing this with more fool proof methods like actually deriving data of SGPA from report
cards, and screentime through phone logs would’ve helped in curating more accurate data, thus yielding
more precise results. Also, the specifics of the context and content that increased the screen time, remain
unknown. Activities such as internet browsing and applications that involve learning, could have also been
used with respect to educational purposes, and will evidently give an outcome of a higher screen time too.
Plus, there were quite a few papers found on similar studies where there also seemed to be no relationship
between screen time and academic performance (6, 7), also hinting at the ambiguity of the proposed
hypothesis.
References:
1) https://jamanetwork.com/journals/jamapediatrics/fullarticle/2751330?guestAccessKey=f02523bb1adb-4566-8f9f02bab8189b69&utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&u
tm_content=tfl&utm_term=092319
2) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214874/
3) https://blog.innerdrive.co.uk/screen-time-academic-performance
4) https://www.goguardian.com/blog/does-screen-time-impact-childrenslearning#:~:text=One%20study%20conducted%20by%20the,thinning%20of%20their%20brains'%20
cortex.
5) https://www.potsdam.edu/studentlife/wellness/counseling-center/what-does-screen-time-do-mybrain#:~:text=Screen%20time%20overloads%20the%20sensory,little%20demands%20become%20b
ig%20ones.
6) https://ohsmagnet.com/30616/news/scrolling-for-answers-on-screentime/#:~:text=There%20is%20no%20correlation%20between,those%20with%20a%20lower%20GP
A
7) https://www.researchgate.net/publication/338157426_A_study_on_correlation_between_screen_
time_duration_and_school_performance_among_primary_school_children_at_Tamil_Nadu_India