M OBILE S OCIAL M EDIA U SAGE AND ACADEMIC
P ERFORMANCE
arXiv:2004.01392v1 [cs.CY] 3 Apr 2020
A P REPRINT
Fausto Giunchiglia
Department of Information Engineering
and Computer Science
University of Trento
Trento, Italy
fausto.giunchiglia@unitn.it
Mattia Zeni
Department of Information Engineering
and Computer Science
University of Trento
Trento, Italy
mattia.zeni.1@unitn.it
Elisa Gobbi
Department of Sociology and Social Research
University of Trento
Trento, Italy
elisa.gobbi@unitn.it
Enrico Bignotti
Department of Information Engineering
and Computer Science
University of Trento
Trento, Italy
enrico.bignotti@unitn.it
Ivano Bison
Department of Sociology and Social Research
University of Trento
Trento, Italy
ivano.bison@unitn.it
June 29, 2020
A BSTRACT
Among the general population, students are especially sensitive to social media and smartphones because of their pervasiveness. Several studies have shown that there is a negative correlation between
social media and academic performance since they can lead to behaviors that hurt students’ careers,
e.g., addictedness. However, these studies either focus on smartphones and social media addictedness or rely on surveys, which only provide approximate estimates. We propose to bridge this gap by
i) parametrizing social media usage and academic performance, and ii) combining smartphones and
time diaries to keep track of users’ activities and their smartphone interaction. We apply our solution
on the 72 students participating in the SmartUnitn project, which investigates students’ time management and their academic performance. By analyzing the logs of social media apps on students’
smartphones while they are studying and attending lessons, and comparing them to students’ credits
and grades, we can provide a quantitative and qualitative estimate of negative and positive correlations. Our results show the negative impact of social media usage, distinguishing different influence
patterns of social media on academic activities and underline the need to take it into account and
control the smartphone usage in academic settings.
Keywords Social media · Academic performance · Smartphone · Time diaries
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1 Introduction
Social media have penetrated in the everyday life of Internet users, and the increasing pervasiveness of smartphones
is only strengthening this phenomenon. Nowadays, these two technologies are intertwined, since smartphones are
becoming more and more pervasive, especially in the student population.
In the literature, there is overwhelming evidence of the negative impact of social media [Paul et al., 2012], especially Facebook [Junco, 2012, Meier et al., 2016], and smartphone usage on academic performance [Lepp et al., 2015,
Samaha and Hawi, 2016]. These technologies combined lead to a series of behaviours that cause students to dedicate
more time to them than actually studying. Usually, surveys are used to identify correlations between students’ behaviour and their academic performances, coupled with scales that understand different metrics of students’ behaviour.
However, using surveys often fails to fully capture these behaviors. In fact, [Lee et al., 2017] finds that users underestimate their usage time by 40% than reported, while [Boase and Ling, 2013], although focusing only on SMS and calls,
notes that self-reports suffer from low criterion validity and lead especially to overreporting usage, in contrast with
[Lee et al., 2017]. Furthermore, [Andrews et al., 2015] notes that users show great lack of awareness of the frequency
with which they check their phone. On the other hand, works studying smartphone usage and social media tend to
focus on addictedness [Lee et al., 2017, 2014]. These studies investigate students since it is a sample of population
very susceptible to smartphone penetration, but they do not attempt to correlate smartphone usage patterns to academic
life. Thus there is a gap between this work on addictedness and sociological surveys on academic performance, since
the former could be exploited to effectively corroborate the latter with usage logs.
We propose to bridge this gap via a systematic approach consisting in defining new metrics for representing social
media usage and using smartphones to both track app usage and administer time diaries [Sorokin and Berger, 1939],
a well known sociological tool for understanding people’s time use. This innovative coupling allows us to isolate the
time of specific activities related to academic performance and provide new insights on behavioural correlations.
We apply this approach on a subset of data about social media apps from the SmartUnitn project, which aims at
correlating the time management of students and their academic performances — this also covers how they manage
their time when interacting with social media. We extract social media usage from students’ smartphones during
specific activities related to university life, i.e., studying and attending classes, and compare it with respect to their
GPA and credits obtained. Results show that there is a negative correlation between the use of social media and
academic performance, with different patterns depending on the activity.
The remainder of this paper is organized as follows. Section 2 illustrates the state of the art, and the main issues with
respect to sociological surveys and studies on addictedness. Section 3 describes our proposed solution, while Section
4 explains the dataset it is based on, i.e., SmartUnitn. Section 5 and Section 6 show our results on the correlation
between social media usage and academic performance. Finally, Section 7 concludes the paper.
2 Literature Review and Hypothesis
Studies using smartphones on students to understand the link between addictedness and usage usually divide them
in two groups (potential addicts and non addicts) based the the Smartphone Addiction Scale [Kwon et al., 2013],
which consists of ten items in a six-point Likert-type scale (1 = “I absolutely disagree", 6 = “I absolutely agree").
[Lee et al., 2014] used it on 95 students and then had them install the SmartLogger software to record specific events
related to users’ interactions with their phones, e.g., touch, text input and active/inactive events. The extracted patterns
indicate that addict risk groups tended to spend more time on their apps, focusing on those that gave them instant
gratifications, e.g., entertainment. Similarly, [Lee et al., 2017] had 35 students download an application that monitored
their smartphone usage for 6 weeks. Results show that while messenger apps were the most used applications for both
groups, addicts strongly preferred social media applications.
Students are also the main sample investigated in reality mining [Eagle and Pentland, 2006]. In terms of social media,
the Copenhagen Networks Study [Stopczynski et al., 2014] is currently collecting data on 1,000 students by coupling
smart phone data with face-to-face interactions and Facebook usage; however, they do not consider these data in
relation to students’ academic performance. In terms of academic performance, the SmartGPA study [Wang et al.,
2015] used data from the Student Life study [Wang et al., 2014], which analyzed the impact of workload on several
mental and physical aspects of students’ life, e.g. mood, and sociability, of a class of 48 students across a 10 week
term, to show that there is evidence of a link between the students’ GPA and their behaviour. However, [Wang et al.,
1
This paper is an extended version of [Giunchiglia et al., 2017b], presented at the 9th International Conference on Social Informatics (SocInfo 2017).
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2015] did not consider social media usage to analyze their impact on students’ career, although this type of information
was collected.
In the sociological community, studies show that there is a negative relation between social media usage and academic
performance. For instance, [Rosen et al., 2013] investigated the behaviour and settings of study for 263 students of
different levels of education, i.e., middle school, high school, and university. Observers controlled students for 15 min
and recorded their on-task and off-task behavior every minute. On average, students became distracted in less than
6 minutes before switching to technological distractions such as social media and texting. [Junco, 2012] focused on
how Facebook use is related to academic performance, by surveying 1839 college students on their use of Facebook
and then comparing it to their GPA. The results indicate that a negative correlation exists between time spent on
Facebook and GPA. Overall, it appears that social media provide students with immediate pleasure in comparison of
other activities such as studying or attending lessons [Jacobsen and Forste, 2011].
Much like social media, using smartphones also negatively affects academic performance [Al-Barashdi et al., 2015];
indeed, social media are becoming more and more synonymous in usage with smartphones. In fact, [Jeong et al.,
2016] notices that social networks can be used to predict smartphone addiction in users due to their pervasiveness
and connectivity. These features of smartphones lead to multitasking [Lepp et al., 2015], i.e., the use of social media
while doing something else, which is detrimental to the time dedicated to academic activities. [Lepp et al., 2015]
conducted a survey on US college students, analyzing their notions of self efficacy and self regulation, i.e., how well
they believe that they can attain their goals and how they can regulate and control themselves, when using smartphones.
Those students with low self regulation turned out to be the one whose usage of smartphones affects their academic
performance the most. In terms of demographics, [Al-Barashdi et al., 2015] suggests that gender and field of study
may act as addiction predictors. From their review of the literature, it appears that males and humanities students tend
to be more susceptible to smartphone addiction.
However, some research highlights how surveys used to establish these correlations may be unreliable, leading to
an approximation of actual usage [Lee et al., 2017, Boase and Ling, 2013, Andrews et al., 2015]. One reason is that
surveys are based on aggregate data from ‘stylized" questions [Juster and Stafford, 1985], e.g., “How many times a day
on average do you check your smartphone?" [Gökçearslan et al., 2016], which force users to recalling activities and
finding an appropriate form of averaging [Kan and Pudney, 2008]. On the other hand, works relying on smartphone
data for analyzing usage tend to focus on addictedness on its own [Lee et al., 2017, 2014] or do not correlate usage
patterns to academic performance [Wang et al., 2015].
3 Social media usage and academic performance
Our proposed solution consists of two elements: i) defining metrics for capturing the smartphone usage patterns in
terms of social media, illustrated in Section 3.1, and ii) employing together time diaries and smartphones to establish
the correlation between social media usage and academic performance, described in Section 3.2. This second element
provides the systematic aspect of our approach since it couples the two tools to overcome their respective limitations
and produce more accurate results.
3.1 Parameters for social media usage and academic performance
To represent social media usage and academic performance, we define three different parameters: i) social media, ii)
usage and iii) academic performance.
Social media (applications) are any technology used to share textual, image and audio content. We further divide social
media applications, hence SM, in three categories:
• Social network sites (SNS): online platforms used by people to build social networks or social relations with
other people, e.g., Facebook;
• Instant messaging applications (IM): online chats that offer real-time text transmission over the Internet,
e.g., Whatsapp;
• Browsers (Web): software applications for retrieving, presenting and traversing information on the Internet,
e.g., Chrome
This distinction allows to capture the fact that each type requires different usage patterns and threatens students’
performances accordingly Junco [2012], Lepp et al. [2015]. For instance, people use SNS for a longer period of time
than IM Meier et al. [2016] and both negatively affect students’ performance, while browsers may be used to access
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both academic and non academic topics, e.g., going on Youtube for entertainment purposes vs going on Wikipedia for
studying.
To represent and evaluate the usage of social media, we distinguish between three types of interactions between
students and their smartphone applications:
1. S̄: the average number of occurrences of social media app usage, i.e., sessions of students checking social
media.;
2. D̄: the average time of social media app usage (in seconds), i.e., the duration of the social media sessions,
namely where any social media app is running ;
¯ the average time in between app usage (in seconds), namely when there is known human interaction
3. I:
(swiping/typing) with an app, i.e., the duration of the inactivity of the phone
Notice that S̄ and D̄ extend and provide further structure to the notion of frequency from Andrews et al. [2015], which
only accounts for the former without considering its duration as parameter.
We represent academic performance with two measures:
• Grade Point Average (GPA): the average of grade points a students obtained during a semester. It represents
the qualitative dimension of academic performance, since it refers to the how well students perform;
• Credito Formativo Universitario (CFU): course credits obtained by students for each exam taken. They represent the quantitative dimension of academic performance, since they refer to the progress of their university
career.
Additionally, socio-demographic variables must be accounted for; in this work, following Al-Barashdi et al. [2015],
students’ faculties (scientific and humanities) are treated as socio-demographic variables to predict the effect of social
media on academic performance.
3.2 Time diaries via smartphones
In terms of sociological survey tools, we employ time diaries. Time diaries are logs where respondents are asked to
detail how they allocated their time during the day Sorokin and Berger [1939]. These logs are tables divided in time
intervals of 10 or 15 minutes Romano [2008], and provide information of time use in terms of activities performed,
locations visited and people encountered during the 24-hour period Hellgren [2014].
Although time diaries also suffer from issues of reliability as all self-reports do Kan and Pudney [2008], they provide
the following advantages:
1. Since respondents have to keep a log of their activities, time diaries allow us to acquire information not only
on the average amount of time spent on different activities during a day, but also the duration and frequency
of each activity, together with their sequences;
2. Time diaries provide a systematic tool for also understanding spatial and social relations of users, which
enriches and widens our scope of research;
3. Respondents do not need to provide average estimates in time diaries, which lessens the cognitive load while
completing them Kan and Pudney [2008], while also reducing the possible mismatch between these answers
given and the actual usage of smartphones.
In this work, we employ a time diary shown in previous work Giunchiglia et al. [2017a], which asks users three
questions: i) “What are you doing?", i.e., activities like “shopping", ii) “Where are you?", i.e., places like “home", and
iii) “Who is with you?", i.e., social relations like “family". The possible answers are a list of pre-defined labels, which
minimizes coding, adapted from the ATUS time use survey Shelley [2005].
Smartphones can enhance time diaries by administering them to users, which then are able to answer them in (almost)
real time, in addition to performing sensor collection, e.g., GPS, Bluetooth, call logs, and running applications, among
others. These two functionalities of smartphones can be exploited to match any given triple of reported activity,
location, and social relation with the status of the smartphone as a proxy of the actual user behavior. Our approach
allows us to compare social media usage and academic performance, by matching those activities that are directly
linked to academic outcomes, i.e., studying and attending lessons, together with the running applications, which
include SM, at the time of the answer.
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4 Methods
We validate our proposed solution on the data from the SmartUnitn project, which belongs to a family of projects called
SMARTRAMS2 that leverages on smartphones to extract behavioural patterns from people and develop systems that
assist users in their everyday life. The SmartUnitn project aims at investigating how students’ time allocations affects
their academic performance.
The project relies on the i-Log mobile application [Zeni et al., 2014, Giunchiglia et al., 2017a] to provide the two
functionalities needed from smartphones in our approach:
• Data collection: i-Log is designed to collect data from multiple sensors simultaneously, both hardware (e.g.,
GPS, accelerometer, gyroscope, among others) and software (e.g., in/out calls, applications running on the
device). A dedicated backend infrastructure manages the tasks of synchronizing and storing the streams of
data from the smartphones.
• Time diaries: i-Log can administer the time diary from [Giunchiglia et al., 2017a] as a question composed
of three sub-question on activities, locations and social relations of students every 30 minutes. Every triple of
questions can be answered within 150 minutes from its notification, with a maximum of 5 questions stacked
in queue, otherwise it expires and treated as null. Questions appear as a silent notifications in order to avoid
bothering students and disrupt their activities too much.
Based on two years of internal testing, i-Log has been designed to ii) be modular and adapt to each smartphone
model, especially in terms of sensing strategies for both smartphones and their internal sensors (which can greatly vary
among different models) ii) to consume as little battery as possible, by devising sensor-dedicated energy consumption
strategies and delegating all computation server-side, and iii) to ensure users’ privacy from data collection to its
analysis.
4.1 Participants
In this project, 72 students were selected from the ones enrolled at the University of Trento in the academic year
2015-2016 and in particular only those who fulfilled three specific criteria: i) to have filled three university surveys in
order to obtain their socio-demographic data, shown in Table 1, and other characteristics, e.g., psychological and time
use related; ii) to attend lessons during the period of our project in order to describe their daily behavior during the
university experience, and iii) to have an Android smartphone with an Android version 5.0.2 or higher.
Table 1: Socio-demographics of students from our project
Gender
Departments
Scholarship
Male
Female Scientific Humanities True
False
61.1% 39.9%
56.9%
43.1%
37.5% 62.5%
The students were asked to attend an introductory presentation where they are presented with the aims of the project
and how to use the application. If they wished to participate, after the presentation they signed a consent form, and
then installed i-Log on their own smartphones. Users were informed about all aspects of the management of their
personal information concerning privacy, from data collection to storage to processing. Furthermore, before starting
the data collection, we obtained the approval from the ethical committee of our university.
4.2 Data collection
The project lasted two weeks. In the first one, students used i-Log to answer to time diaries while also having their
data collected; during the second week, they were only required to have the application running for the collection of
data.
The SmartUnitn dataset amounts to 110 Gb, containing behavioural data from smartphones merged with sociodemographic characteristics of students obtained both through surveys and academic performance data provided by
the University of Trento.
4.3 Measures
In terms of our parameters, the SmartUnitn dataset provides the following data:
2
See http://trams.disi.unitn.it for more information
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Figure 1: Usage parameters of social media apps (SM) while studying
(a) Sessions
Mon
Tue
Wed
Thu
Fri
Sat
Sun
(b) Duration
Mon
Tue
Wed
Thu
Fri
Sat
Sun
10
5
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
0
102
101
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
10−1
• Social media: There 957 different applications across all students, and, within 32 SM apps, social networks
are the most represented with 11 apps.
• Usage: Because of the Android operating system design, any application in the foreground keeps being
logged for up to an hour, while i-Log collects running applications and the time at which their running
every 5 seconds (on average). To obtain a more realistic understanding of usage, screen status information
was added to filter out application logs recorded while students were not actually interacting with their phone.
This operation results in a dataset of 135322 applications logging events covering the 7 days of the experiment
during which the time diaries were administered.
• Academic performance: Information about GPA and CFU is provided from the University of Trento and it
concerns the final performance of students at the end of their first academic year (September 2016).
Notice that the overall sample considered is 67 students and not 72 due to unexpected incompatibility of some students’
smartphone operating system in providing the correct logs for running applications and for one outlier case in terms
of number of CFU, most likely due to incorrect recording of that information from the administration, which made it
impossible to understand his total amount of credits. Even though this student has logs of his apps usage, our analysis
ignore these data.
5 Quantifying social media usage
We quantify two dimensions of the parameters for understanding the usage behaviour of students with respect to social
¯ temporal distribution of the usage, which allows us to identify temporal patterns, in Section
media, i.e., S̄, D̄, and I:
5.1, and its average mean in Section 5.2.
5.1 Distribution
Figure 1a and Figure 1b report the distribution of S̄ and D̄ of social media usage during study, while Figure 2a and
Figure 2b illustrate the distribution of the same parameters for attending lessons. These figures show 1 hour timeslots
for every hour of the day and every day of the week, on the x and y axis respectively. Slots of empty spaces (in white)
mean that no students was either studying or attending classes on that day during the specific time interval, while the
darker the shade of colour the higher the number of students using apps at that time. Notice that we do not show I¯
because the distribution was entirely random.
Figure 1a shows S̄ while students reported that they were studying. Notice that the empty spaces concentrate during
nighttime, as expected. Apart from few slots where there is a noticeable increase of usage, distribution through days
and hours is mostly uniform. This means that students use social media while studying regardless of the time of the
episode. Figure 1b represents the D̄ of all social media apps use during study period. This parameter increases in two
different portions of the day: i) during nighttime, especially between midnight and 3AM, and ii) early in the morning
between 7AM and 9AM. Conversely, a decrease of D̄ is underlined by the lighter area between 9AM and 6PM during
weekdays.
In the University of Trento, lessons are concentrated during working days from 8AM to 8PM. Instance of classes
during the weekend and outside this range are most likely outliers due to mistakes in reporting or possibly classes of
non-academic subjects. Figure 2a shows that, similarly to Figure 1a, the use of social media app ignores any actual
time interval, although Friday appears to have a higher number of students checking SM apps. Figure 2b shows
the D̄ of checking social media apps during lessons. The pattern that emerges suggests that students tend to stay
connected longer early in the morning and then returning to similar D̄ levels as the day goes by, especially during
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Figure 2: Usage parameters of social media apps (SM) while attending lessons
(a) Sessions
(b) Duration
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Mon
Tue
Wed
Thu
Fri
Sat
Sun
10
5
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
102
101
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
10−1
Friday afternoon. Overall, in terms of temporal distribution, all parameters tend to be uniformly distributed during
the whole week, be they during studying or while attending lessons. This could be interpreted as an instance of the
multitasking [Lepp et al., 2015] behaviour permeating the everyday life of students.
5.2 Average mean
We propose an analysis of the mean values of S̄, D̄, and I¯ for all apps, focusing on SM apps, with respect to activities
in general, Table 2, while students were studying, Table 2b, and attending lessons, Table 2c. For each table, rows
represent the type of activity (general, studying or attending lessons), the type of apps considered (all apps, SM apps
as a whole, SNS, IM, and Web) and their respective parameters, while columns represent the parameters mean usage
values (Mean), their standard deviation (Sd) and the number of students (N).
Table 2: All social media usage with respect to our variables (parameters and apps) during:
(b) Studying
General
SM
SNS
IM
Web
Sd
798,31
50,79
136
360,50
22,65
143,65
149,28
96,28
103,21
282,58
20,24
155,24
64,18
40,46
68,92
N
67
67
67
67
67
67
67
66
66
67
67
67
67
60
60
All
SM
Study
All
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
Mean
1975,55
47,67
236,37
664,25
69,13
157,80
160,91
140,25
79,57
440
43,77
180,89
63,43
98,71
57,21
SNS
IM
Web
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
Mean
296,37
44,52
198,64
108,44
70,57
121,69
23,91
121,37
94,99
73,74
49,86
140,30
10,79
93,04
58,57
Sd
228,99
20,88
148,06
96,94
34,13
97,38
32,21
100,29
224,91
70,52
30,57
127,88
16,06
60,98
104,83
(c) Attending lessons
N
67
64
64
67
64
64
67
57
57
67
64
64
67
49
49
All
SM
Lesson
(a) General activities
SNS
IM
Web
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
Mean
269,97
36,23
167,07
87,71
57,03
134,21
19,76
117,50
66,65
57,22
36,65
144,80
10,73
90,38
87,82
Sd
176,64
17,29
122,23
67,37
28,82
203,93
26,28
111,11
87,27
48,98
25,74
200,07
14,31
73,59
280,39
N
67
66
66
67
65
65
67
57
57
67
65
65
67
51
51
For general activities, S̄ appears to be the most relevant parameter both for all apps and SM apps (1975.55, SD 798.31
and 664.25, SD 360.50), followed by I¯ and D̄. Within SM apps, IM are the most checked type of apps, with S̄ being
almost 4 times the other apps (440, SD 282.58), but also the one with the highest value for I¯ (180.89, SD 155,24), while
SNS sessions last the longest (D̄ of 120.25). This general pattern is also true for reported usage of smartphones both
while studying and attending lessons, although with some differences. Firstly, SM apps are checked more frequently
and for longer periods of time while studying than during lessons (higher values of S̄ and D̄ for SM and each app
type). Notice that in the case of D̄ of SNS and Web the values are nonetheless very close, unlike IM, with 49.86, SD
30.57 for studying vs 36.65, SD 25.74 for attending lessons. Secondly, while I¯ is lower during studying in terms of
SM (121.69, SD 97.38), its values for IM apps are almost equal: for study the mean is 140.30 seconds (SD 127.88)
and for lesson is 144.80 seconds (SD 200.07). Overall, these findings suggest the following:
• On average, students check SM apps more frequently and for longer periods while studying than attending
lessons (higher S̄ and D̄), but while in class these sessions are more done in a longer window of time (higher
¯
I).
• Within SM apps for both studying and attending lessons, IM apps are the most checked but with the longest
window of time in between sessions, while SNS apps are the ones with the highest duration of usage.
7
Table 3: Correlations of all apps and social media apps, with academic performance, based on overall activities plus studying and attending lessons
SNS
SM
All
Web
IM
General
SM
All
ECTS
Study
SNS
SM
All
Web
IM
SNS
IM
Web
Sci.
-0.2 (38)
-0.0 (38)
0.36* (38)
-0.26 (38)
0.07 (38)
0.09 (38)
-0.46** (38)
0.2 (38)
-0.19 (38)
-0.16 (38)
-0.05 (38)
0.15 (38)
0.2 (38)
0.21 (34)
0.2 (34)
-0.17 (38)
-0.09 (37)
0.4* (37)
-0.26 (38)
0.02 (36)
0.3 (36)
-0.49** (38)
0.23 (32)
0.11 (32)
-0.19 (38)
0.02 (36)
0.46** (36)
0.03 (38)
-0.15 (26)
0.19 (26)
0.1 (38)
0.05 (37)
0.44** (37)
-0.02 (38)
-0.15 (36)
0.24 (36)
-0.34* (38)
-0.11 (31)
0.21 (31)
0.1 (38)
-0.04 (36)
0.23 (36)
0.12 (38)
-0.03 (29)
0.27 (29)
F
0.05 (28)
-0.01 (28)
0.1 (27)
-0.01 (27)
-0.26 (27)
0.17 (27)
-0.03 (27)
-0.08 (26)
0.02 (26)
-0.01 (27)
-0.21 (27)
0.22 (27)
0.01 (27)
-0.15 (24)
-0.0 (24)
0.14 (27)
-0.06 (27)
0.11 (27)
0.07 (27)
-0.29 (26)
0.24 (26)
0.08 (27)
-0.09 (23)
0.03 (23)
0.04 (27)
-0.22 (26)
0.29 (26)
0.08 (27)
-0.3 (20)
-0.13 (20)
-0.05 (27)
-0.14 (27)
0.33 (27)
-0.01 (27)
-0.47* (26)
0.28 (26)
-0.16 (27)
-0.55** (22)
0.2 (22)
0.07 (27)
-0.2 (26)
0.22 (26)
0.04 (27)
-0.09 (21)
0.45* (21)
M
-0.2 (40)
-0.02 (40)
0.27 (40)
-0.25 (40)
0.1 (40)
-0.02 (40)
-0.41** (40)
0.05 (40)
-0.26 (40)
-0.16 (40)
-0.02 (40)
0.06 (40)
0.2 (40)
0.05 (36)
0.18 (36)
-0.06 (40)
-0.2 (38)
0.36* (38)
-0.11 (40)
0.08 (38)
0.15 (38)
-0.22 (40)
0.29 (34)
0.01 (34)
-0.06 (40)
0.04 (38)
0.33* (38)
0.07 (40)
-0.1 (29)
0.16 (29)
0.07 (40)
-0.08 (39)
0.32* (39)
-0.03 (40)
-0.11 (39)
0.16 (39)
-0.3 (40)
-0.08 (35)
-0.02 (35)
0.08 (40)
-0.01 (39)
0.15 (39)
0.12 (40)
-0.18 (30)
0.22 (30)
GPA
Sci.\F
0.33 (10)
-0.25 (10)
0.13 (10)
0.05 (10)
-0.13 (10)
0.19 (10)
-0.29 (10)
0.06 (10)
-0.42 (10)
0.11 (10)
0.06 (10)
0.22 (10)
0.14 (10)
0.26 (9)
-0.19 (9)
0.34 (10)
-0.05 (10)
-0.15 (10)
-0.07 (10)
-0.19 (10)
0.27 (10)
-0.46 (10)
0.08 (9)
0.25 (9)
-0.0 (10)
-0.0 (10)
0.22 (10)
-0.14 (10)
-0.07 (7)
-0.5 (7)
0.4 (10)
-0.01 (10)
0.39 (10)
0.19 (10)
-0.38 (9)
0.59 (9)
-0.33 (10)
-0.38 (8)
0.08 (8)
0.38 (10)
0.04 (9)
0.5 (9)
0.04 (10)
0.34 (7)
0.75 (7)
Hum.\F
0.11 (16)
-0.11 (16)
-0.07 (15)
0.04 (15)
-0.36 (15)
0.02 (15)
0.08 (15)
-0.42 (14)
-0.26 (14)
0.03 (15)
-0.75** (15)
0.1 (15)
-0.11 (15)
-0.5 (13)
0.05 (13)
-0.04 (15)
-0.12 (15)
-0.02 (15)
0.12 (15)
-0.62* (15)
0.08 (15)
0.09 (15)
-0.38 (13)
0.32 (13)
0.12 (15)
-0.79*** (15)
0.26 (15)
-0.05 (15)
-0.54 (13)
-0.01 (13)
-0.18 (15)
-0.5 (15)
0.15 (15)
0.21 (15)
-0.45 (15)
0.02 (15)
0.02 (15)
-0.84*** (12)
0.21 (12)
0.2 (15)
-0.42 (15)
-0.04 (15)
0.33 (15)
-0.5 (12)
0.4 (12)
Sci.\M
-0.41 (21)
0.05 (21)
0.41 (21)
-0.49* (21)
0.15 (21)
0.04 (21)
-0.5* (21)
0.2 (21)
-0.28 (21)
-0.4 (21)
-0.08 (21)
0.12 (21)
0.22 (21)
0.23 (18)
0.3 (18)
-0.24 (21)
0.01 (20)
0.52* (20)
-0.31 (21)
0.17 (20)
0.33 (20)
-0.48* (21)
0.34 (18)
0.09 (18)
-0.27 (21)
-0.03 (20)
0.56** (20)
0.13 (21)
-0.12 (16)
0.39 (16)
-0.11 (21)
-0.01 (20)
0.47* (20)
-0.24 (21)
-0.12 (20)
0.23 (20)
-0.37 (21)
-0.03 (17)
0.01 (17)
-0.1 (21)
-0.08 (20)
0.21 (20)
0.08 (21)
-0.32 (15)
0.27 (15)
Hum.\M
-0.05 (14)
-0.15 (14)
-0.02 (14)
-0.02 (14)
-0.05 (14)
-0.2 (14)
-0.26 (14)
-0.24 (14)
-0.09 (14)
0.08 (14)
0.38 (14)
-0.11 (14)
0.1 (14)
-0.68* (13)
-0.22 (13)
0.23 (14)
-0.67* (13)
-0.16 (13)
0.21 (14)
-0.14 (13)
-0.34 (13)
-0.03 (14)
0.28 (12)
-0.25 (12)
0.32 (14)
0.22 (13)
-0.33 (13)
0.12 (14)
-0.26 (10)
-0.39 (10)
0.09 (14)
-0.22 (14)
-0.26 (14)
0.06 (14)
-0.07 (14)
-0.24 (14)
-0.09 (14)
-0.18 (14)
-0.21 (14)
0.12 (14)
0.4 (14)
-0.17 (14)
-0.07 (14)
-0.28 (10)
-0.51 (10)
All
-0.05 (68)
0.07 (68)
0.16 (67)
-0.1 (67)
0.03 (67)
0.07 (67)
-0.19 (67)
0.05 (66)
-0.16 (66)
-0.07 (67)
-0.04 (67)
0.12 (67)
0.19 (67)
-0.0 (60)
0.2 (60)
-0.04 (67)
0.02 (65)
0.26* (65)
-0.08 (67)
-0.04 (64)
0.11 (64)
-0.15 (67)
0.02 (57)
0.0 (57)
-0.05 (67)
-0.08 (64)
0.23 (64)
0.03 (67)
0.09 (49)
0.08 (49)
0.03 (67)
-0.08 (66)
0.4*** (66)
-0.01 (67)
-0.11 (65)
0.2 (65)
-0.21 (67)
-0.13 (57)
0.05 (57)
0.05 (67)
-0.03 (65)
0.18 (65)
0.15 (67)
-0.19 (51)
0.3* (51)
Notes: ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001; Hum.=Humanities; Sci.=Scientific; F=Female; G=Gender; (N)=N of students; the gradient of color represents the result significance.
Hum.
-0.2 (30)
0.07 (30)
0.14 (29)
-0.32 (29)
0.11 (29)
0.08 (29)
-0.18 (29)
-0.02 (28)
-0.11 (28)
-0.29 (29)
0.08 (29)
0.12 (29)
-0.06 (29)
-0.15** (26)
0.19 (26)
-0.12 (29)
0.01 (28)
0.29 (28)
-0.16 (29)
-0.14* (28)
0.13 (28)
-0.17 (29)
-0.13 (25)
-0.12 (25)
-0.12 (29)
-0.08 (28)
0.17 (28)
-0.07 (29)
-0.07* (23)
0.21 (23)
-0.26 (29)
-0.17 (29)
0.42 (29)
-0.3 (29)
0.01 (29)
0.11 (29)
-0.21 (29)
-0.15* (26)
0.09 (26)
-0.27 (29)
0.03 (29)
0.08 (29)
-0.01 (29)
-0.37 (22)
0.51 (22)
Sci.
0.02 (38)
-0.06 (38)
0.19* (38)
0.0 (38)
0.0 (38)
0.05 (38)
-0.31** (38)
0.06 (38)
-0.19 (38)
0.06 (38)
-0.04 (38)
0.11 (38)
0.38 (38)
0.09 (34)
0.25 (34)
-0.12 (38)
-0.08 (37)
0.28* (37)
-0.14 (38)
-0.06 (36)
0.18 (36)
-0.41** (38)
0.07 (32)
0.13 (32)
-0.07 (38)
-0.08 (36)
0.3** (36)
0.06 (38)
0.13 (26)
-0.02 (26)
0.24 (38)
0.06 (37)
0.41** (37)
0.17 (38)
-0.11 (36)
0.29 (36)
-0.2* (38)
-0.2 (31)
0.08 (31)
0.25 (38)
-0.01 (36)
0.27 (36)
0.27 (38)
-0.06 (29)
0.36 (29)
F
-0.13 (28)
0.08 (28)
0.14 (27)
-0.07 (27)
-0.28 (27)
0.14 (27)
-0.26 (27)
-0.03 (26)
-0.06 (26)
0.03 (27)
-0.32 (27)
0.18 (27)
0.08 (27)
-0.23 (24)
0.31 (24)
-0.09 (27)
0.15 (27)
0.15 (27)
-0.05 (27)
-0.24 (26)
0.06 (26)
-0.22 (27)
-0.18 (23)
-0.08 (23)
-0.0 (27)
-0.3 (26)
0.06 (26)
0.09 (27)
0.12 (20)
0.15 (20)
-0.25 (27)
-0.25 (27)
0.42 (27)
-0.17 (27)
-0.51* (26)
-0.11 (26)
-0.46 (27)
-0.33** (22)
-0.13 (22)
0.01 (27)
-0.29 (26)
-0.14 (26)
0.05 (27)
-0.34 (21)
0.52* (21)
M
-0.0 (40)
-0.03 (40)
0.21 (40)
-0.1 (40)
0.19 (40)
0.04 (40)
-0.16** (40)
0.09 (40)
-0.17 (40)
-0.11 (40)
0.03 (40)
0.1 (40)
0.26 (40)
0.14 (36)
0.17 (36)
-0.04 (40)
-0.1 (38)
0.37* (38)
-0.11 (40)
0.06 (38)
0.2 (38)
-0.12 (40)
0.12 (34)
0.07 (34)
-0.1 (40)
0.01 (38)
0.35* (38)
-0.01 (40)
0.07 (29)
0.06 (29)
0.17 (40)
0.06 (39)
0.41* (39)
0.07 (40)
0.05 (39)
0.31 (39)
-0.08 (40)
-0.01 (35)
0.2 (35)
0.07 (40)
0.05 (39)
0.29 (39)
0.22 (40)
-0.14 (30)
0.33 (30)
Sci.\F
0.29 (10)
-0.4 (10)
-0.69 (10)
0.23 (10)
-0.7 (10)
-0.37 (10)
-0.23 (10)
-0.64 (10)
-0.33 (10)
0.27 (10)
-0.34 (10)
-0.34 (10)
0.26 (10)
-0.19 (9)
0.06 (9)
0.17 (10)
-0.17 (10)
-0.51 (10)
0.02 (10)
-0.4 (10)
-0.22 (10)
-0.54 (10)
-0.36 (9)
-0.11 (9)
0.09 (10)
-0.33 (10)
-0.18 (10)
-0.05 (10)
0.43 (7)
-0.49 (7)
0.36 (10)
-0.2 (10)
-0.09 (10)
0.27 (10)
-0.56 (9)
-0.36 (9)
-0.3 (10)
-0.52 (8)
-0.07 (8)
0.45 (10)
-0.3 (9)
-0.4 (9)
0.13 (10)
-0.38 (7)
0.43 (7)
Hum.\F
-0.31 (16)
0.05 (16)
0.2 (15)
-0.33 (15)
-0.02 (15)
0.1 (15)
-0.37 (15)
0.08 (14)
-0.22 (14)
-0.17 (15)
-0.3** (15)
0.19 (15)
-0.09 (15)
-0.23 (13)
0.44 (13)
-0.45 (15)
0.26 (15)
0.33 (15)
-0.33 (15)
-0.12* (15)
0.25 (15)
-0.39 (15)
-0.08 (13)
0.18 (13)
-0.21 (15)
-0.2*** (15)
0.24 (15)
-0.05 (15)
-0.07 (13)
0.43 (13)
-0.62 (15)
-0.41 (15)
0.64 (15)
-0.49 (15)
-0.35 (15)
0.0 (15)
-0.55 (15)
-0.35*** (12)
-0.17 (12)
-0.26 (15)
-0.23 (15)
0.01 (15)
0.13 (15)
-0.25 (12)
0.57 (12)
Sci.\M
-0.24 (21)
0.06 (21)
0.48 (21)
-0.3* (21)
0.19 (21)
0.15 (21)
-0.32* (21)
0.08 (21)
-0.2 (21)
-0.27 (21)
0.04 (21)
0.23 (21)
0.28 (21)
0.08 (18)
0.38 (18)
-0.11 (21)
0.04 (20)
0.57* (20)
-0.18 (21)
0.17 (20)
0.44 (20)
-0.33* (21)
0.23 (18)
0.14 (18)
-0.17 (21)
0.02 (20)
0.61** (20)
0.19 (21)
0.1 (16)
0.1 (16)
-0.07 (21)
0.16 (20)
0.65* (20)
-0.15 (21)
-0.01 (20)
0.44 (20)
-0.23 (21)
-0.13 (17)
0.1 (17)
-0.08 (21)
0.02 (20)
0.43 (20)
0.1 (21)
-0.13 (15)
0.52 (15)
Hum.\M
-0.0 (14)
-0.11 (14)
-0.04 (14)
-0.25 (14)
0.31 (14)
-0.09 (14)
0.12 (14)
-0.03 (14)
-0.02 (14)
-0.38 (14)
0.25 (14)
-0.05 (14)
-0.07 (14)
-0.0* (13)
-0.18 (13)
0.06 (14)
-0.29* (13)
0.21 (13)
-0.06 (14)
-0.08 (13)
0.12 (13)
0.01 (14)
-0.06 (12)
-0.24 (12)
-0.06 (14)
0.01 (13)
0.23 (13)
-0.17 (14)
0.1 (10)
-0.02 (10)
0.06 (14)
0.07 (14)
0.14 (14)
-0.14 (14)
0.31 (14)
0.22 (14)
0.43 (14)
0.01 (14)
0.31 (14)
-0.28 (14)
0.26 (14)
0.14 (14)
-0.1 (14)
-0.48 (10)
0.33 (10)
PREPRINT
Hum.
0.01 (30)
-0.04 (30)
-0.02 (29)
-0.02 (29)
-0.23 (29)
0.01 (29)
-0.1 (29)
-0.29 (28)
-0.16 (28)
0.03 (29)
-0.01 (29)
0.06 (29)
0.01 (29)
-0.59** (26)
-0.02 (26)
0.11 (29)
-0.33 (28)
-0.02 (28)
0.16 (29)
-0.41* (28)
-0.17 (28)
0.02 (29)
-0.01 (25)
-0.03 (25)
0.22 (29)
-0.29 (28)
-0.12 (28)
0.07 (29)
-0.43* (23)
-0.17 (23)
-0.06 (29)
-0.37 (29)
0.0 (29)
0.12 (29)
-0.26 (29)
-0.13 (29)
-0.0 (29)
-0.45* (26)
-0.08 (26)
0.13 (29)
0.02 (29)
-0.11 (29)
0.11 (29)
-0.32 (22)
0.29 (22)
A
Lesson
8
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
S̄
D̄
Ī
All
-0.09 (68)
0.01 (68)
0.17 (67)
-0.16 (67)
-0.04 (67)
0.06 (67)
-0.26* (67)
0.01 (66)
-0.18 (66)
-0.1 (67)
-0.06 (67)
0.12 (67)
0.13 (67)
-0.03 (60)
0.12 (60)
0.02 (67)
-0.12 (65)
0.23 (65)
-0.03 (67)
-0.07 (64)
0.14 (64)
-0.11 (67)
0.17 (57)
-0.01 (57)
-0.01 (67)
-0.06 (64)
0.29* (64)
0.07 (67)
-0.18 (49)
0.05 (49)
0.02 (67)
-0.11 (66)
0.31* (66)
-0.02 (67)
-0.21 (65)
0.16 (65)
-0.24 (67)
-0.24 (57)
0.07 (57)
0.08 (67)
-0.05 (65)
0.14 (65)
0.09 (67)
-0.15 (51)
0.22 (51)
- J UNE 29, 2020
A
PREPRINT
- J UNE 29, 2020
6 Social media usage vs GPA and CFU
Table 3 shows how S̄, D̄ and I¯ are correlated to students’ CFU and GPA by using Pearson’s correlation.
In Table 3 the darker the color of the cells whose parameters, considering columns and rows, obtain a significant value
with respect to the correlation coefficient, the higher the value significance (i.e., higher p value). Rows represent S̄,
D̄ and I¯ for the combination of application type and activities from the Section 5. Columns represent the sociodemographic variables considered and the academic performance indexes, GPA and CFU. The socio-demographics are
gender, faculties (distinguishing between scientific and humanities), and the combination of the two, i.e., male and
female students from either faculties.
We expect a negative correlation in an increase of S̄ and D̄, since they would imply more smartphone usage and hence
less time dedicated to academic activities. Conversely, we expect a higher value of I¯ to be positively correlated with
academic activity, since it would indicate less time dedicated to smartphones.
6.1 Significant values of social media usage
¯ Columns indicate
Table 4a, Table 4b and Table 4c summarize the occurrence of significant values for S̄, D̄, and I.
the amount of significant values, divided according to their p value, and their total amount, while rows represent the
type of activity (general, studying or attending lessons), the type of apps considered (all apps, SM apps as a whole,
SNS, IM, and Web), their respective parameters, and their sum accounting for both the amount of values and their
significance.
Table 4a shows that during general activities S̄ and D̄ have a relatively close amount of significant correlations (9 and
6, respectively), while I¯ has only 2. Moreover, S̄ of SNS is especially significant, reaching 7 values (4 with p < .01).
If we look at the same vales for studying and attending lessons, Table 4b and Table 4c respectively, studying provides
more occurrences than attending lessons (28 vs 21), but with similar occurrences of values per significance.
Table 4: Number of significant value occurrences from our variables in:
Web
Sum
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
0
0
2
0
2
0
2
7
9
0
4
0
4
4
0
0
4
0
2
7
9
0
2
0
2
4
10
14
28
All
SM
SNS
IM
Web
Sum
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
Total
IM
0
0
0
0
0
0
0
0
2
0
0
2
0
0
4
4
0
0
0
0
2
0
4
6
p < .001
SNS
0
2
7
9
0
4
0
4
2
0
0
2
0
0
3
3
0
2
0
2
2
8
10
20
p < .01
SM
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
p < .05
All
Total
0
0
2
2
2
0
0
2
7
0
0
7
0
0
0
2
0
4
0
4
9
6
2
17
p < .001
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Study
0
0
0
0
0
0
0
0
4
0
0
4
0
2
0
2
0
2
0
2
4
4
0
8
p < .01
Sum
0
0
2
2
2
0
0
2
3
0
0
3
0
0
0
0
0
2
0
2
5
2
2
9
(c) Attending lessons
p < .05
Web
Total
IM
p < .001
SNS
p < .01
General
SM
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
S̄
D̄
Ī
Total
p < .05
All
(b) Studying
Lesson
(a) General activities
0
0
5
5
0
2
0
2
2
6
0
8
0
0
0
0
0
0
3
3
2
8
8
18
0
0
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
8
8
0
2
0
2
2
6
0
8
0
0
0
0
0
0
3
3
2
8
11
21
Moreover, I¯ is the parameter with the most occurrences of significant values for both activities, with a total of 25,
followed by D̄ with 18 and finally S̄, only 6. Within SM, IM provides the most significant values for studying,
¯ however, there are no IM values for lessons, which means that IM provide no correlations in this
concentrated on I;
case. On the other hand, SNS provide the most values for establishing correlations in lessons, especially for D̄.
Overall, these findings suggest that our parameters for usage and social media plus the time diary answers for academic
activities allow us to effectively underline different patterns of SM app influence. Moreover:
• While studying, the average duration of usage of IM apps (D̄ with negative p values) is the most harmful for
academic performance; however, the longer students avoid them (I¯ with positive p values) the higher their
performances.
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• The average duration of usage (D̄ with negative p values) and the average occurrences of checking (S̄ with
negative p values) SNS while attending lessons negatively affect students’ academic performance
6.2 Significant values for CFU and GPA
Table 5 and Table 6 show the total occurrences of significant values between our variables and the CFU and GPA, i.e.,
33 for both. Columns indicate the type of variable considered: all, faculty (humanities and scientific), gender (females
and males), the combination of the two (females and males in scientific and humanities faculties) and their sum. Rows
represent the amount of significant values, divided according to their p value, and their total amount.
On average, the influence of SM apps both on CFU and GPA appears to be stronger for scientific students than for
students from humanities (7 vs 4), while gender differences seem to be less important, being almost equally distributed
in our sample. In addition, distinguishing within each faculty suggests that being either a male student enrolled
in a scientific faculty or being a female from humanities are the most “at risk" groups of a decrease of academic
performance.
Hum.
Sci.
F
M
Sci.\F
Hum.\F
Sci.\M
Hum.\M
Sum
p < .05
p < .01
p < .001
Tot
All
Table 5: Number of significant correlations for CFU
CFU
3
0
0
3
3
1
0
4
2
5
0
7
2
1
0
3
3
1
0
4
0
0
0
0
1
1
2
4
5
1
0
6
2
0
0
2
21
10
2
33
Hum.
Sci.
F
M
Sci.\F
Hum.\F
Sci.\M
Hum.\M
Sum
p < .05
p < .01
p < .001
Tot
All
Table 6: Number of significant correlations for GPA
GPA
1
1
1
3
3
1
0
4
3
4
0
7
2
1
0
3
3
1
0
4
0
0
0
0
1
1
2
4
5
1
0
6
2
0
0
2
20
10
3
33
Table 5 shows that without considering any demographics, there is very little correlation between social media apps
usage and CFU. There are three positive occurrences, i.e., S̄ of SNS usage in general (-.26, p < 0.05), I¯ of IM apps
while studying (0.29, p < 0.05)and I¯ of all the apps during lessons (0.28, p < 0.05). Taking into account students’
field of study, while for humanities the D̄ parameter is related to a lower number of CFU, for scientific students S̄ and
I¯ of SM apps tends to be more associated with increased CFU. Considering gender, SM negatively affect CFU from
females especially during lessons: D̄ of SM (-0.47, p < 0.05) and SNS (-0.55, p < 0.01) plus I¯ of browser apps (0.45,
p < 0.05); this effect is even stronger for females in humanities, with D̄ of SM reaching -0.84 (p < 0.001). For males,
the pattern is less clear but the parameter that has a stronger negative correlation on CFU is S̄ of SNS during general
activities (-0.41, p < 0.01).
Table 6 shows that, if we control for GPA without including demographics, we find the same trend of CFU results:
I¯ of all apps while both studying (0.26, p < 0.05) and attending lessons (0.40, p < 0.001), and specifically I¯ of
Web apps during lessons, are positively associated with their GPA. Taking into account students’ field of study, app
usage significantly affects GPA while studying, with stronger effects for scientific students than humanities. Moreover,
scientific students’ GPA increases if they have higher I¯ for all the apps (0.28, p < 0.05) and for IM apps (0.30,
p < 0.05) and it decreases with higher level of S̄ for SNS apps (-0.41, p < 0.01) while studying. Once again, also
for GPA, the negative influence of social media app usage for females occurs while attending lessons. Indeed, D̄ of
social media apps in general (-0.51, p < 0.05) and of SNS in particular (-0.33, p < 0.01) affects females performance
especially while they are in the classroom. Overall, these findings suggest that:
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• There appear to be no major differences between males and female in terms of correlation of SM app usage
and academic performance.
• Academic performance of scientific students is more affected by their SM usage than students from humanities. While this is an interesting finding, its causes are unclear and require further research.
• S̄ and D̄ are always correlated with lower performance both for GPA and for CFU while longer inactive
¯ are positively associated with them.
periods (I)
7 Conclusions and Limitations
In this paper, we proposed to overcome the current limitations of the state of the art in linking students’ usage of
social media on smartphones by coupling smartphones and time diaries, to then be able to match reports of time use
with actual logs of SM apps. Based on the sample from the SmartUnitn project, we could corroborate the finding of
sociological literature by using three parameters that pinpointed behavioural patterns that could either hurt academic
performance, e.g., constantly messaging while studying or staying on SNS while in class, or improve it, e.g., limiting
IM usage.
Our results seems to confirm our hypothesis that controlling the use of social media during studying or lesson attending
is more informative with respect to their association with students’ academic performance than considering their
general use without activity distinction, in addition to their faculties.
Nonetheless, there two main limitations in our work. The first one is the relatively small window of time considered,
i.e., two weeks, compared to other studies in computational social sciences, e.g., 10 weeks in SmartGPA [Wang et al.,
2015] and almost one year in the Copenhagen Networks Study Karpinski et al. [2013]. However, notice that one week
of time diaries is considerably more than the usual amount of days recorded in sociology, which is usually limited
to two days (one weekday and one weekend) [Romano, 2008], and thus allowed us a bigger time window to extract
patterns from. The second one is the number of students in our sample. While it is considerably smaller than other
studies in sociology, e.g., 263 students in [Rosen et al., 2013] and 1839 students in [Junco, 2012], our sample is still
is larger than other works in the area of computational social sciences, e.g., 48 students in SmartGPA [Wang et al.,
2015] and 35 students in [Lee et al., 2017]. Although we are aware of the possible limitations of this study in terms
of sample and window of time covered, they will be addressed in the next iteration of SmartUnitn to be carried out in
March of 2018, by also detailing more the type of students’ faculty, given how relevant it is in light of our results.
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