Education and Information Technologies (2023) 28:6287–6320

https://doi.org/10.1007/s10639-022-11430-9

The impact of smartphone use on learning effectiveness:

A case study of primary school students

Jen Chun Wang1 · Chia‑Yen Hsieh2 · Shih‑Hao Kung1

Received: 21 February 2022 / Accepted: 24 October 2022 / Published online: 11 November 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
2022

Abstract
This study investigated the effects of smartphone use on the perceived academic per-
formance of elementary school students. Following the derivation of four hypoth-
eses from the literature, descriptive analysis, t testing, one-way analysis of vari-
ance (ANOVA), Pearson correlation analysis, and one-way multivariate ANOVA
(MANOVA) were performed to characterize the relationship between smartphone
behavior and academic performance with regard to learning effectiveness. All coef-
ficients were positive and significant, supporting all four hypotheses. We also used
structural equation modeling (SEM) to determine whether smartphone behavior is
a mediator of academic performance. The MANOVA results revealed that the stu-
dents in the high smartphone use group academically outperformed those in the low
smartphone use group. The results indicate that smartphone use constitutes a poten-
tial inequality in learning opportunities among elementary school students. Finally,
in a discussion of whether smartphone behavior is a mediator of academic perfor-
mance, it is proved that smartphone behavior is the mediating variable impacting
academic performance. Fewer smartphone access opportunities may adversely affect
learning effectiveness and academic performance. Elementary school teachers must
be aware of this issue, especially during the ongoing COVID-19 pandemic. The
findings serve as a reference for policymakers and educators on how smartphone use
in learning activities affects academic performance.

Keywords Smartphone use · Learning effectiveness · Human–computer interface ·

Media in education · Elementary education

* Chia‑Yen Hsieh
banduna@nptu.edu.tw
Extended author information available on the last page of the article

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1 Introduction

The advent of the Fourth Industrial Revolution has stimulated interest in edu-
cational reforms for the integration of information and communication technol-
ogy (ICT) into instruction. Smartphones have become immensely popular ICT
devices. In 2019, approximately 96.8% of the global population had access to
mobile devices with the coverage rate reaching 100% in various developed coun-
tries (Sarker et al., 2019). Given their versatile functions, smartphones have been
rapidly integrated into communication and learning, among other domains, and
have become an inseparable part of daily life for many. Smartphones are per-
ceived as convenient, easy-to-use tools that promote interaction and multitask-
ing and facilitate both formal and informal learning (Looi et al., 2016; Yi et al.,
2016). Studies have investigated the impacts of smartphones in education. For
example, Anshari et al. (2017) asserted that the advantages of smartphones in
educational contexts include rich content transferability and the facilitation of
knowledge sharing and dynamic learning. Modern students expect to experience
multiple interactive channels in their studies. These authors also suggested incor-
porating smartphones into the learning process as a means of addressing inap-
propriate use of smartphones in class (Anshari et al., 2017). For young children,
there are differences in demand and attributes and some need for control depend-
ing upon the daily smartphone usage of the children (Cho & Lee, 2017). To
avoid negative impacts, including interference with the learning process, teach-
ers should establish appropriate rules and regulations. In a study by Bluestein
and Kim (2017) on the use of technology in the classroom they examined three
themes: acceptance of tablet technology, learning excitement and engagement,
and the effects of teacher preparedness and technological proficiency. They sug-
gested that teachers be trained in application selection and appropriate in-class
device usage. Cheng et al. (2016) found that smartphone use facilitated English
learning in university students. Some studies have provided empirical evidence of
the positive effects of smartphone use, whereas others have questioned the inte-
gration of smartphone use into the academic environment. For example, Hawi
and Samaha (2016) investigated whether high academic performance was pos-
sible for students at high risk of smartphone addiction. They provided strong evi-
dence of the adverse effects of smartphone addiction on academic performance.
Lee et al. (2015) found a negative correlation between smartphone addiction and
learning in university students. There has been a lot of research on the effective-
ness of online teaching, but the results are not consistent. Therefore, this study
aims to further explore the effects of independent variables on smartphone use
behavior and academic performance.
The COVID-19 pandemic has caused many countries to close schools and
suspend in-person classes, enforcing the transition to online learning. Car-
rillo and Flores (2020) suggested that because of widespread school closures,
teachers must learn to manage the online learning environment. Online courses
have distinct impacts on students and their families, requiring adequate tech-
nological literacy and the formulation of new teaching or learning strategies

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(Sepulveda-Escobar & Morrison, 2020). Since 2020, numerous studies have

been conducted on parents’ views regarding the relationship of online learning,
using smartphones, computers, and other mobile devices, with learning effective-
ness. Widely inconsistent findings have been reported. For instance, in a study by
Hadad et al. (2020), two thirds of parents were opposed to the use of smartphones
in school, with more than half expressing active opposition (n = 220). By con-
trast, parents in a study by Garbe et al. (2020) agreed to the school closure policy
and allowed their children to use smartphones to attend online school. Given the
differences in the results, further scholarly discourse on smartphone use in online
learning is essential.
Questions remain on whether embracing smartphones in learning systems facili-
tates or undermines learning (i.e., through distraction). Only a few studies have been
conducted on the impacts of smartphone use on academic performance in elemen-
tary school students (mostly investigating college or high school students). Thus,
we investigated the effects of elementary school students’ smartphone use on their
academic performance.

2 Literature review

Mobile technologies have driven a paradigm shift in learning; learning activities can
now be performed anytime, anywhere, as long as the opportunity to obtain informa-
tion is available (Martin & Ertzberger, 2013).
Kim et al. (2014) focused on identifying factors that influence smartphone adop-
tion or use. Grant and Hsu (2014) centered their investigation on user behavior,
examining the role of smartphones as learning devices and social interaction tools.
Although the contribution of smartphones to learning is evident, few studies have
focused on the connection between smartphones and learning, especially in ele-
mentary school students. The relationship between factors related to learning with
smartphones among this student population is examined in the following sections.

2.1 Behavioral intentions of elementary school students toward smartphone use

Children experience rapid growth and development during elementary school and
cultivate various aspects of the human experience, including social skills formed
through positive peer interactions. All these experiences exert a substantial impact
on the establishment of self-esteem and a positive view of self. Furthermore, stu-
dents tend to maintain social relationships by interacting with others through vari-
ous synchronous or asynchronous technologies, including smartphone use (Guo
et al., 2011). Moreover, students favor communication through instant messaging,
in which responses are delivered rapidly. However, for this type of interaction, stu-
dents must acquire knowledge and develop skills related to smartphones or related
technologies which has an impact on social relationships (Kang & Jung, 2014; Park
& Lee, 2012).

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Karikoski and Soikkeli (2013) averred that smartphone use promotes human-to-
human interaction both through verbal conversation and through the transmission of
textual and graphic information, and cn stimulate the creation and reinforcement of
social networks. Park and Lee (2012) examined the relationship between smartphone
use and motivation, social relationships, and mental health. The found smartphone
use to be positively correlated with social intimacy. Regarding evidence supporting
smartphone use in learning, Firmansyah et al. (2020) concluded that smartphones
significantly benefit student-centered learning, and they can be used in various
disciplines and at all stages of education. They also noted the existence of a myriad
smartphone applications to fulfill various learning needs. Clayton and Murphy
(2016) suggested that smartphones be used as a mainstay in classroom teaching,
and that rather than allowing them to distract from learning, educators should help
their students to understand how smartphones can aid learning and facilitate civic
participation. In other words, when used properly, smartphones have some features
that can lead to better educational performance. For example, their mobility can
allow students access to the same (internet-based) services as computers, anytime,
anywhere (Lepp et al., 2014). Easy accessibility to these functionalities offers
students the chance to continuously search for study-related information. Thus,
smartphones can provide a multi-media platform to facilitate learning which cannot
be replaced by simply reading a textbook (Zhang et al., 2014). Furthermore, social
networking sites and communication applications may also contribute to the sharing
of relevant information. Faster communication between students and between
students and faculty may also contribute to more efficient studying and collaboration
(Chen et al., 2015). College students are more likely to have access to smartphones
than elementary school students. The surge in smartphone ownership among college
students has spurred interest in studying the impact of smartphone use on all aspects
of their lives, especially academic performance. For example, Junco and Cotton
(2012) found that spending a fair amount of time on smartphones while studying had
a negative affect on the university student’s Grade Point Average (GPA). In addition,
multiple studies have found that mobile phone use is inversely related to academic
performance (Judd, 2014; Karpinski et al., 2013). Most research on smartphone
use and academic performance has focused on college students. There have few
studies focused on elementary school students. Vanderloo (2014) argued that the
excessive use of smartphones may cause numerous problems for the growth and
development of children, including increased sedentary time and reduced physical
activity. Furthermore, according to Sarwar and Soomro (2013), rapid and easy access
to information and its transmission may hinder concentration and discourage critical
thinking and is therefore not conducive to children’s cognitive development.
To sum up, the evidence on the use of smartphones by elementary school students
is conflicting. Some studies have demonstrated that smartphone use can help ele-
mentary school students build social relationships and maintain their mental health,
and have presented findings supporting elementary students’ use of smartphones in
their studies. Others have opposed smartphone use in this student population, con-
tending that it can impede growth and development. To take steps towards resolving
this conflict, we investigated smartphone use among elementary school students.

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In a study conducted in South Korea, Kim (2017) reported that 50% of their
questionnaire respondents reported using smartphones for the first time between
grades 4 and 6. Overall, 61.3% of adolescents reported that they had first used
smartphones when they were in elementary school. Wang et al. (2017) obtained
similar results in an investigation conducted in Taiwan. However, elementary
school students are less likely to have access to smartphones than college stu-
dents. Some elementary schools in Taiwan prohibit their students from using
smartphones in the classroom (although they can use them after school). On the
basis of these findings, the present study focused on fifth and sixth graders.
Jeong et al. (2016), based on a sample of 944 respondents recruited from 20
elementary schools, found that people who use smartphones for accessing Social
Network Services (SNS), playing games, and for entertainment were more likely
to be addicted to smartphones. Park (2020) found that games were the most com-
monly used type of mobile application among participants, comprised of 595
elementary school students. Greater smartphone dependence was associated with
greater use of educational applications, videos, and television programs (Park,
2020). Three studies in Taiwan showed the same results, that elementary school
students in Taiwan enjoy playing games on smartphones (Wang & Cheng, 2019;
Wang et al., 2017). Based on the above, it is reasonable to infer that if elemen-
tary school students spend more time playing games on their smartphones, their
academic performance will decline. However, several studies have found that
using smartphones to help with learning can effectively improve academic perfor-
mance. In this study we make effort to determine what the key influential factors
that affect students’ academic performance are.
Kim (2017) reported that, in Korea, smartphones are used most frequently-
from 9 pm to 12 am, which closely overlaps the corresponding period in Taiwan,
from 8 to 11 pm In this study, we not only asked students how they obtained
their smartphones, but when they most frequently used their smartphones, and
who they contacted most frequently on their smartphones were, among other
questions. There were a total of eight questions addressing smartphone behav-
ior. Recent research on smartphones and academic performance draws on self-
reported survey data on hours and/or minutes of daily use (e.g. Chen et al.,
2015; Heo & Lee, 2021; Lepp et al., 2014; Troll et al., 2021). Therefore, this
study also uses self-reporting to investigate how much time students spend using
smartphones.
Various studies have indicated that parental attitudes affect elementary school
students’ behavioral intentions toward smartphone use (Chen et al., 2020; Daems
et al., 2019). Bae (2015) determined that a democratic parenting style (character-
ized by warmth, supervision, and rational explanation) was related to a lower likeli-
hood of smartphone addiction in children. Park (2020) suggested that parents should
closely monitor their children’s smartphone use patterns and provide consistent
discipline to ensure appropriate smartphone use. In a study conducted in Taiwan,
Chang et al. (2019) indicated that restrictive parental mediation reduced the risk of
smartphone addiction among children. In essence, parental attitudes critically influ-
ence the behavioral intention of elementary school students toward smartphone use.
The effect of parental control on smartphone use is also investigated in this study.

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Another important question related to student smartphone use is self-control.

Jeong et al. (2016) found that those who have lower self-control and greater stress
were more likely to be addicted to smartphones. Self-control is here defined as the
ability to control oneself in the absence of any external force, trying to observe
appropriate behavior without seeking immediate gratification and thinking about the
future (Lee et al., 2015). Those with greater self-control focus on long-term results
when making decisions. People are able to control their behavior through the con-
scious revision of automatic actions which is an important factor in retaining self-
control in the mobile and on-line environments. Self-control plays an important role
in smartphone addiction and the prevention thereof. Previous studies have revealed
that the lower one’s self-control, the higher the degree of smartphone dependency
(Jeong et al., 2016; Lee et al., 2013). In other words, those with higher levels of self-
control are likely to have lower levels of smartphone addiction. Clearly, self-control
is an important factor affecting smartphone usage behavior.
Reviewing the literature related to self-control, we start with self-determination
theory (SDT). The SDT (Deci & Ryan, 2008) theory of human motivation dis-
tinguishes between autonomous and controlled types of behavior. Ryan and Deci
(2000) suggested that some users engage in smartphone communications in response
to perceived social pressures, meaning their behavior is externally motivated. How-
ever, they may also be intrinsically motivated in the sense that they voluntarily use
their smartphones because they feel that mobile communication meets their needs
(Reinecke et al., 2017). The most autonomous form of motivation is referred to as
intrinsic motivation. Being intrinsically motivated means engaging in an activity for
its own sake, because it appears interesting and enjoyable (Ryan & Deci, 2000). Act-
ing due to social pressure represents an externally regulated behavior, which SDT
classifies as the most controlled form of motivation (Ryan & Deci, 2000). Individu-
als engage in such behavior not for the sake of the behavior itself, but to achieve a
separable outcome, for example, to avoid punishment or to be accepted and liked by
others (Ryan & Deci, 2006). SDT presumes that controlled and autonomous motiva-
tions are not complementary, but “work against each other” (Deci et al., 1999, p.
628). According to the theory, external rewards alter the perceived cause of action:
Individuals no longer voluntarily engage in an activity because it meets their needs,
but because they feel controlled (Deci et al., 1999). For media users, the tempta-
tion to communicate through the smartphone is often irresistible (Meier, 2017).
Researchers who have examined the reasons why users have difficulty controlling
media use have focused on their desire to experience need gratification, which pro-
duces pleasurable experiences. The assumption here is that users often subcon-
sciously prefer short-term pleasure gains from media use to the pursuit of long-term
goals (Du et al., 2018). Accordingly, self-control is very important. Self-control here
refers to the motivation and ability to resist temptations (Hofmann et al., 2009). Dis-
positional self-control is a key moderator of yielding to temptation (Hofmann et al.,
2009). Ryan and Deci (2006) suggested that people sometimes perform externally
controlled behaviors unconsciously, that is, without applying self-control.
Sklar et al. (2017) described two types of self-control processes: proactive and
reactive. They suggested that deficiencies in the resources needed to inhibit temp-
tation impulses lead to failure of self-control. Even when impossible to avoid a

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temptation entirely, self-control can still be made easier if one avoids attending to
the tempting stimulus. For example, young children instructed to actively avoid
paying attention to a gift and other attention-drawing temptations are better able to
resist the temptation than children who are just asked to focus on their task. There-
fore, this study more closely investigates students’ self-control abilities in relation
to smartphone use asking the questions, ‘How did you obtain your smartphone?’ (to
investigate proactivity), and ‘How much time do you spend on your smartphone in a
day?’ (to investigate the effects of self-control).
Thus, the following hypotheses are advanced.

Hypothesis 1: Smartphone behavior varies with parental control.

Hypothesis 2: Smartphone behavior varies based on students’ self-control.

2.2 Parental control, students’ self‑control and their effects on learning

effectiveness and academic performance

Based on Hypothesis 1 and 2, we believe that we need to focus on two factors,

parental control and student self-control and their impact on academic achievement.
In East Asia, Confucianism is one of the most prevalent and influential cultural val-
ues which affect parent–child relations and parenting practice (Lee et al., 2016).
In Taiwan, Confucianism shapes another feature of parenting practice: the strong
emphasis on academic achievement. The parents’ zeal for their children’s education
is characteristic of Taiwan, even in comparison to academic emphasis in other East
Asian countries. Hau and Ho (2010) noted that, in Eastern Asian (Chinese) cultures,
academic achievement does not depend on the students’ interests. Chinese students
typically do not regard intelligence as fixed, but trainable through learning, which
enables them to take a persistent rather than a helpless approach to schoolwork, and
subsequently perform well. In Chinese culture, academic achievement has been tra-
ditionally regarded as the passport to social success and reputation, and a way to
enhance the family’s social status (Hau & Ho, 2010). Therefore, parents dedicate
a large part of their family resources to their children’s education, a practice that is
still prevalent in Taiwan today (Hsieh, 2020). Parental control aimed at better aca-
demic achievement is exerted within the behavioral and psychological domains. For
instance, Taiwan parents tightly schedule and control their children’s time, planning
private tutoring after school and on weekends. Parental control thus refers to “paren-
tal intrusiveness, pressure, or domination, with the inverse being parental support of
autonomy” (Grolnick & Pomerantz, 2009). There are two types of parental control:
behavioral and psychological. Behavioral control, which includes parental regula-
tion and monitoring over what children do (Steinberg et al., 1992), predict positive
psychosocial outcomes for children. Outcomes include low externalizing problems,
high academic achievement (Stice & Barrera, 1995), and low depression. In contrast,
psychological control, which is exerted over the children’s psychological world, is
known to be problematic (Stolz et al., 2005). Psychological control involves strat-
egies such as guilt induction and love withdrawal (Steinberg et al., 1992) and is

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related with disregard for children’s emotional autonomy and needs (Steinberg et al.,
1992). Therefore, it is very important to discuss the type of parental control.
Troll et al. (2021) suggested that it is not the objective amount of smartphone
use but the effective handling of smartphones that helps students with higher trait
self-control to fare better academically. Heo and Lee (2021) discussed the mediating
effect of self-control. They found that self-control was partially mediated by those
who were not at risk for smartphone addiction. That is to say, smartphone addiction
could be managed by strengthening self-control to promote healthy use. In an earlier
study Hsieh and Lin (2021), we collected 41 international journal papers involving
136,491students across 15 countries, for meta-analysis. We found that the average
and majority of the correlations were both negative. The short conclusion here was
that smartphone addiction /reliance may have had a negative impact on learning per-
formance. Clearly, it is very important to investigate the effect of self-control on
learning effectiveness with regard to academic performance.

2.3 Smartphone use and its effects on learning effectiveness and academic

performance

The impact of new technologies on learning or academic performance has been

investigated in the literature. Kates et al. (2018) conducted a meta-analysis of 39
studies published over a 10-year period (2007–2018) to examine potential relation-
ships between smartphone use and academic achievement. The effect of smartphone
use on learning outcomes can be summarized as follows: r =  − 0.16 with a 95% con-
fidence interval of − 0.20 to − 0.13. In other words, smartphone use and academic
achievement were negatively correlated. Amez and Beart (2020) systematically
reviewed the literature on smartphone use and academic performance, observing
the predominance of empirical findings supporting a negative correlation. However,
they advised caution in interpreting this result because this negative correlation was
less often observed in studies analyzing data collected through paper-and-pencil
questionnaires than in studies on data collected through online surveys. Further-
more, this correlation was less often noted in studies in which the analyses were
based on self-reported grade point averages than in studies in which actual grades
were used. Salvation (2017) revealed that the type of smartphone applications and
the method of use determined students’ level of knowledge and overall grades. How-
ever, this impact was mediated by the amount of time spent using such applications;
that is, when more time is spent on educational smartphone applications, the likeli-
hood of enhancement in knowledge and academic performance is higher. This is
because smartphones in this context are used as tools to obtain the information nec-
essary for assignments and tests or examinations. Lin et al. (2021) provided robust
evidence that smartphones can promote improvements in academic performance if
used appropriately.
In summary, the findings of empirical investigations into the effects of smart-
phone use have been inconsistent—positive, negative, or none. Thus, we explore the
correlation between elementary school students’ smartphone use and learning effec-
tiveness with regard to academic performance through the following hypotheses:

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Hypothesis 3: Smartphone use is associated with learning effectiveness with

regard to academic performance.
Hypothesis 4: Differences in smartphone use correspond to differences in learn-
ing effectiveness with regard to academic performance.

Hypotheses 1 to 4 are aimed at understanding the mediating effect of smartphone

behavior; see Fig. 1. It is assumed that smartphone behavior is the mediating vari-
able, parental control and self-control are independent variables, and academic per-
formance is the dependent variable. We want to understand the mediation effect of
this model.
Thus, the following hypotheses are presented.

Hypothesis 5: Smartphone behaviors are the mediating variable to impact the

academic performance.

2.4 Effects of the COVID‑19 pandemic on smartphone use for online learning

According to 2020 statistics from the United Nations Educational, Scientific and
Cultural Organization (UNESCO), since the start of the COVID-19 pandemic, full
or partial school closures have affected approximately 800 million learners world-
wide, more than half of the global student population. Schools worldwide have
been closed for 14 to 22 weeks on average, equivalent to two thirds of an academic
year (UNESCO, 2021). Because of the pandemic, instructors have been compelled
to transition to online teaching (Carrillo & Flores, 2020). According to Tang et al.
(2020), online learning is among the most effective responses to the COVID-19 pan-
demic. However, the effectiveness of online learning for young children is limited by
their parents’ technological literacy in terms of their ability to navigate learning plat-
forms and use the relevant resources. Parents’ time availability constitutes another
constraint (Dong et al., 2020). Furthermore, a fast and stable Internet connection,
as well as access to devices such as desktops, laptops, or tablet computers, defini-
tively affects equity in online education. For example, in 2018, 14% of households in

Fig. 1  Model 1: Model to test the impact of parental control and students’ self-control on academic per-
formance

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the United States lacked Internet access (Morgan, 2020). In addition, the availability
and stability of network connections cannot be guaranteed in relatively remote areas,
including some parts of Australia (Park et al., 2021). In Japan, more than 50% of
3-year-old children and 68% of 6-year-old children used the Internet in their stud-
ies, but only 21% of households in Thailand have computer equipment (Park et al.,
2021).
In short, the COVID-19 pandemic has led to changes in educational practices.
With advances in Internet technology and computer hardware, online education
has become the norm amid. However, the process and effectiveness of learning in
this context is affected by multiple factors. Aside from the parents’ financial abil-
ity, knowledge of educational concepts, and technological literacy, the availability
of computer equipment and Internet connectivity also exert impacts. This is espe-
cially true for elementary school students, who rely on their parents in online learn-
ing more than do middle or high school students, because of their short attention
spans and undeveloped computer skills. Therefore, this study focuses on the use of
smartphones by elementary school students during the COVID-19 pandemic and its
impact on learning effectiveness.

3 Methods

3.1 Participants

Participants were recruited through stratified random sampling. They comprised

499 Taiwanese elementary school students (in grades 5 and 6) who had used smart-
phones for at least 12 months. Specifically, the students advanced to grades 5 or 6
at the beginning of the 2018–2019 school year. Boys and girls accounted for 47.7%
and 52.3% (n = 238 and 261, respectively) of the sample.

3.2 Data collection and measurement

In 2020, a questionnaire survey was conducted to collect relevant data. Of the 620
questionnaires distributed, 575 (92.7%) completed questionnaires were returned.
After 64 participants were excluded because they had not used their smartphones
continually over the past 12 months and 14 participants were excluded for pro-
viding invalid responses, 499 individuals remained. The questionnaire was devel-
oped by one of the authors on the basis of a literature review. The questionnaire
content can be categorized as follows: (1) students’ demographic characteristics,
(2) smartphone use, (3) smartphone behavior, and (4) learning effectiveness. The
questionnaire was modified according to evaluation feedback provided by six
experts. Exploratory and confirmatory factor analyses were conducted to test the
structural validity of the questionnaire. Factor analysis was performed using prin-
cipal component analysis and oblique rotation. From the exploratory factor analy-
sis, 25 items (15 and 10 items on smartphone behavior and academic performance
as constructs, respectively) were extracted and confirmed. According to the

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results of the exploratory factor analysis, smartphone behavior can be classified

into three dimensions: interpersonal communication, leisure and entertainment,
and searching for information. Interpersonal communication is defined as when
students use smartphones to communicate with classmates or friends, such as in
response to questions like ‘I often use my smartphone to call or text my friends’.
Leisure and entertainment mean that students spend a lot of their time using their
smartphones for leisure and entertainment, e.g. ‘I often use my smartphone to
listen to music’ or ‘I often play media games with my smartphone’. Searching for
information means that students spend a lot of their time using their smartphones
to search for information that will help them learn, such as in response to ques-
tions like this ‘I often use my smartphone to search for information online, such
as looking up words in a dictionary’ or ‘I will use my smartphone to read e-books
and newspapers online’.
Academic performance can be classified into three dimensions: learning activi-
ties, learning applications, and learning attitudes. Learning activities are when stu-
dents use their smartphones to help them with learning, such as in response to a
question like ‘I often use some online resources from my smartphone to help with
my coursework’. Learning applications are defined as when students apply smart-
phone software to help them with their learning activities, e.g. ‘With a smartphone,
I am more accustomed to using multimedia software’. Learning attitudes define the
students’ attitudes toward using the smartphone, with questions like ‘Since I have
had a smartphone, I often find class boring; using a smartphone is more fun’ (This is
a reverse coded item). The factor analysis results are shown in the appendix (Appen-
dix Tables 10, 11, 12, 13 and 14). It can be seen that the KMO value is higher than
0.75, and the Bartlett’s test is also significant. The total variance explained for
smartphone behavior is 53.47% and for academic performance it is 59.81%. These
results demonstrate the validity of the research tool.
In this study, students were defined as "proactive" if they had asked their parents
to buy a smartphone for their own use and "reactive" if their parents gave them a
smartphone unsolicited (i.e. they had not asked for it). According to Heo and Lee
(2021), students who proactively asked their parents to buy them a smartphone gave
the assurance that they could control themselves and not become addicted, but if
they had been given a smartphone (without having to ask for it), they did not need
to offer their parents any such guarantees. They defined user addiction (meaning low
self-control) as more than four hours of smartphone use per day (Peng et al., 2022).
A cross-tabulation of self-control results is presented in Table 2, with the col-
umns representing “proactive” and “reactive”, and the rows showing “high self-
control” and “low self-control”. There are four variables in this cross-tabulation,
“Proactive high self-control” (students promised parents they would not become
smartphone addicts and were successful), “Proactive low self-control” (assured their
parents they would not become smartphone addicts, but were unsuccessful), “Reac-
tive high self-control”, and “Reactive low self-control”.
Regarding internal consistency among the constructs, the Cronbach’s α values
ranged from 0.850 to 0.884. According to the guidelines established by George
and Mallery (2010), these values were acceptable because they exceeded 0.7. The
overall Cronbach’s α for the constructs was 0.922. The Cronbach’s α value of the

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smartphone behavior construct was 0.850, whereas that of the academic perfor-
mance construct was 0.884.

3.3 Data analysis

The participants’ demographic characteristics and smartphone use (expressed as

frequencies and percentages) were subjected to a descriptive analysis. To examine
hypotheses 1 and 2, an independent samples t test (for gender and grade) and one-
way analysis of variance (ANOVA) were performed to test the differences in smart-
phone use and learning effectiveness with respect to academic performance among
elementary school students under various background variables. To test hypothesis
3, Pearson’s correlation analysis was conducted to analyze the association between
smartphone behavior and academic performance. To test hypothesis 4, one-way mul-
tivariate ANOVA (MANOVA) was employed to examine differences in smartphone
behavior and its impacts on learning effectiveness. To test Hypothesis 5, structural
equation modeling (SEM) was used to test whether smartphone behavior is a media-
tor of academic performance.

4 Results

4.1 Descriptive analysis

The descriptive analysis (Table 1) revealed that the parents of 71.1% of the partici-
pants (n = 499) conditionally controlled their smartphone use. Moreover, 42.5% of
the participants noted that they started using smartphones in grade 3 or 4. Notably,
43.3% reported that they used their parents’ old smartphones; in other words, almost
half of the students used secondhand smartphones. Overall, 79% of the participants
indicated that they most frequently used their smartphones after school. Regard-
ing smartphone use on weekends, 54.1% and 44.1% used their smartphones dur-
ing the daytime and nighttime, respectively. Family members and classmates (45.1%
and 43.3%, respectively) were the people that the participants communicated with
the most on their smartphones. Regarding bringing their smartphones to school,
53.1% of the participants indicated that they were most concerned about losing their
phones. As for smartphone use duration, 28.3% of the participants indicated that
they used their smartphones for less than 1 h a day, whereas 24.4% reported using
them for 1 to 2 h a day.

4.2 Smartphone behavior varies with parental control and based on students’

self‑control

We used the question ‘How did you obtain your smartphone?’ (to investigate proac-
tivity), and ‘How much time do you spend on your smartphone in a day?’ (to inves-
tigate the effects of students’ self-control). According to the Hsieh and Lin (2021),

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 Table 1  Descriptive analysis results
Demographic variable Variable Number Percentage (%)

Gender Male 238 47.7

Female 261 52.3
Grade Fifth 244 48.9
Sixth 255 51.1
What are your parents’ attitudes toward controlling your smartphone use (Parental Strict 46 9.2
control) Conditional 355 71.1
None 98 19.6
When did you first start using a smartphone? Before elementary school 96 19.2
Grades 1 to 2 118 23.6
Grades 3 to 4 212 42.5
Grades 5 to 6 73 14.6
How did you obtain your smartphone? My parents bought it for me 184 36.9
My parents gave me their old smartphone 216 43.3
Education and Information Technologies (2023) 28:6287–6320

I asked my parents to buy it for me 35 7

I asked my parents, and they gave me their old smartphone 64 12.8
When do you use your smartphone the most often? Before school 8 1.6
After school 394 79
At school 97 19.4
At what time of day do you most frequently use your smartphone on weekends? Daytime 270 54.1
Nighttime 220 44.1
All day 9 1.8
6299

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 Table 1  (continued)
6300

Demographic variable Variable Number Percentage (%)

13
Who do you contact the most often on your smartphone? Family member(s) 225 45.1
Teacher(s) 2 0.4
Classmate(s) 221 44.3
Others 51 10.2
Do you usually bring your smartphone to school? Yes 26 5.2
No 393 78.8
Depends on the situation 80 16
What is your greatest concern about bringing your smartphone to school? I’m not concerned about this 73 14.6
That I’ll lose it 265 53.1
That it’ll be confiscated 64 12.8
That it’ll be compared with my classmates’ smartphones 6 1.2
That my classmates will use it 36 7.2
Other 55 11
What is your greatest concern about smartphone use? I’m not concerned about this 212 42.5
Signal interference 124 24.8
An increase in my phone bill 16 3.2
Effects of electromagnetic radiation 32 6.4
Negative effects on my studies 78 15.6
Other 37 7.4
Education and Information Technologies (2023) 28:6287–6320
 Table 1  (continued)
Demographic variable Variable Number Percentage (%)

How much time do you spend on your smartphone in a day? Less than 1 h 141 28.3
1 to 2 h 122 24.4
2 to 3 h 80 16
3 to 4 h 58 11.6
More than 4 h 98 19.6
Self-control Proactive high self-control 278 55.7
Reactive high self-control 66 13
Proactive low self-control 122 24.4
Reactive low self-control 34 6.8
Education and Information Technologies (2023) 28:6287–6320
6301

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6302 Education and Information Technologies (2023) 28:6287–6320

Table 2  Cross-tabulation of self-control ability

Students who asked their parents to buy Students whose parents gave them
them a smartphone a smartphone without being asked

Using a smartphone for Proactive high self-control Reactive high self-control

less than 4 h a day
Using a smartphone for Proactive low self-control Reactive low self-control
more than 4 h a day

and Peng et al. (2022), addition is defined more than 4 h a day are defined as smart-
phone addiction (meaning that students have low self-control).
Table 2 gives the cross-tabulation results for self-control ability. Students who
asked their parents to buy a smartphone, but use it for less than 4 h a day are defined
as having ‘Proactive high self-control’; students using a smartphone for more than
4 h a day are defined as having ‘Proactive low self-control’. Students whose parents
gave them a smartphone but use them for less than 4 h a day are defined as having
‘Reactive high self-control’; students given smart phones and using them for more
than 4 h a day are defined as having ‘Reactive low self-control’; others, we define as
having moderate levels of self-control.
Tables 3–5 present the results of the t test and analysis of covariance (ANCOVA)
on differences in the smartphone behaviors based on parental control and students’
self-control. As mentioned, smartphone behavior can be classified into three dimen-
sions: interpersonal communication, leisure and entertainment, and information
searches. Table 3 lists the significant independent variables in the first dimension of
smartphone behavior based on parental control and students’ self-control. Among
the students using their smartphones for the purpose of communication, the propor-
tion of parents enforcing no control over smartphone use was significantly higher
than the proportions of parents enforcing strict or conditional control (F = 11.828,
p < 0.001). This indicates that the lack of parental control over smartphone use leads
to the participants spending more time using their smartphones for interpersonal
communication.

Table 3  Significant independent variables (Parental control and Self-control) in the first dimension
(interpersonal communication) of smartphone use
Independent variable Variable Number Mean SD F value A posteriori
comparison

Parental control (1) Strict 46 3.13 1.17 11.82*** 3 > 1,2

(2) Conditional 355 3.51 0.99
(3) None 98 3.93 0.84
Self-control (1) Proactive high self-control 278 3.47 0.05 18.88*** 1,3,4 > 2
(2) Reactive high self-control 66 2.95 0.11
(3) Proactive low self-control 122 3.98 0.08
(4) Reactive low self-control 34 3.91 1.16
***
p < .001

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For the independent variable of self-control, regardless of whether students had

proactive high self-control, proactive low self-control or reactive low self-control,
significantly higher levels of interpersonal communication than reactive high self-
control were reported (F = 18.88, p < 0.001). This means that students effectively
able to control themselves, who had not asked their parents to buy them smart-
phones, spent less time using their smartphones for interpersonal communication.
However, students with high self-control but who had asked their parents to buy
them smartphones, would spend more time on interpersonal communication (mean-
ing that while they may not spend a lot of time on their smartphones each day, the
time spent on interpersonal communication is no different than for the other groups).
Those without effective self-control, regardless of whether they had actively asked
their parents to buy them a smartphone or not, would spend more time using their
smartphones for interpersonal communication.
Table 4 displays the independent variables (parental control and students’ self-
control) significant in the dimension of leisure and entertainment. Among the stu-
dents using their smartphones for this purpose, the proportion of parents enforcing
no control over smartphone use was significantly higher than the proportions of par-
ents enforcing strict or conditional control (F = 8.539, p < 0.001). This indicates that
the lack of parental control over smartphone use leads to the participants spending
more time using their smartphones for leisure and entertainment.
For the independent variable of self-control, students with proactive low self-con-
trol and reactive low self-control reported significantly higher use of smartphones
for leisure and entertainment than did students with proactive high self-control and
reactive high self-control (F = 8.77, p < 0.001). This means that students who cannot
control themselves, whether proactive or passive in terms of asking their parents to
buy them a smartphone, will spend more time using their smartphones for leisure
and entertainment.
Table 5 presents the significant independent variables in the dimension of infor-
mation searching. Significant differences were observed only for gender, with
a significantly higher proportion of girls using their smartphones to search for

Table 4  Significant independent variables (Parental control and Self-control) in the second dimension
(leisure and entertainment) of smartphone behavior
Independent variable Variable Number Mean SD F value A posteriori
comparison

Parental control (1) Strict 46 3.11 0.87 8.53*** 3 > 1,2

(2) Conditional 355 3.37 0.65
(3) None 98 3.59 0.67
Self-control (1) Proactive high self-control 278 3.31 0.69 8.77*** 3,4 > 1,2
(2) Reactive high self-control 66 3.19 0.76
(3) Proactive low self-control 122 3.62 0.57
(4) Reactive low self-control 34 3.59 0.59

SD standard deviation
***
p < .001

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Table 5  Independent variables (Parental control and Self-control) in the third dimension (information searches) of smartphone behavior
Independent variable Variable Number Mean SD t value/F value A posteriori comparison

Gender Male (boys) 238 3.52 0.96 − 3.979*** girls > boys
Female (girls) 261 3.74 0.88
Parental control (1) Strict 46 3.77 1.05 0.72
(2) Conditional 355 3.60 0.92
(3) None 98 3.67 0.88
Self-control (1) Proactive high self-control 278 3.63 0.94 0.35
(2) Reactive high self-control 66 3.62 1.00
(3) Proactive low self-control 122 3.62 0.86
(4) Reactive low self-control 34 3.67 0.91

SD standard deviation
***
p < .001
Education and Information Technologies (2023) 28:6287–6320
Education and Information Technologies (2023) 28:6287–6320 6305

information (t =  − 3.979, p < 0.001). Parental control and students’ self-control had
no significance in the dimension of information searching. This means that the par-
ents’ attitudes towards control did not affect the students’ use of smartphones for
information searches. This is conceivable, as Asian parents generally discourage
their children from using their smartphones for non-study related activities (such as
entertainment or making friends), but not for learning-related activities. It is also
worth noting that student self-control was not significant in relation to searching for
information. This means that it makes no difference whether or not students have
self-control in their search for learning-related information.
Four notable results are presented as follows.
First, a significantly higher proportion of girls used their smartphones to search
for information. Second, if smartphone use was not subject to parental control, the
participants spent more time using their smartphones for interpersonal communi-
cation and for leisure and entertainment rather than for information searches. This
means that if parents make the effort to control their children’s smartphone use, this
will reduce their children’s use of smartphones for interpersonal communication and
entertainment. Third, student self-control affects smartphone use behavior for inter-
personal communication and entertainment (but not searching for information). This
does not mean that they spend more time on their smartphones in their daily lives, it
means that they spend the most time interacting with people while using their smart-
phones (For example, they may only spend 2–3 h a day using their smartphone. Dur-
ing those 2–3 h, they spend more than 90% of their time interacting with people and
only 10% doing other things), which is the fourth result.
These results support hypotheses 1 and 2.

4.3 Pearson’s correlation analysis of smartphone behavior and academic

performance

Table 6 presents the results of Pearson’s correlation analysis of smartphone behavior

and academic performance. Except for information searches and learning attitudes,
all variables exhibited significant and positively correlations. In short, there was a
positive correlation between smartphone behavior and academic performance. Thus,
hypothesis 3 is supported.

Table 6  Pearson’s correlation analysis of smartphone use and academic performance

Variable Interpersonal Leisure and Information Smartphone
communication entertainment searches behavior

Learning activities .369** .342** 176** .382**

Learning applications .436** .435** .472** .565**
Learning attitudes .286** .330** .027 .286**
Academic performance .486** .493** .321** .557**
**
p < .01

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4.4 Analysis of differences in the academic performance of students

with different smartphone behaviors

Differences in smartphone behavior and its impacts on learning effectiveness with

regard to academic performance were examined through. In step 1, cluster analysis
was conducted to convert continuous variables into discrete variables. In step 2, a
one-way MANOVA was performed to analyze differences in the academic perfor-
mance of students with varying smartphone behavior. Regarding the cluster analysis
results (Table 7), the value of the change in the Bayesian information criterion in
the second cluster was − 271.954, indicating that it would be appropriate to group
the data. Specifically, we assigned the participants into either the high smartphone
use group or the low smartphone use group, comprised of 230 and 269 participants
(46.1% and 53.9%), respectively.
The MANOVA was preceded by the Levene test for the equality of variance,
which revealed nonsignificant results, F(6, 167,784.219) = 1.285, p > 0.05. Thus, we
proceeded to use MANOVA to examine differences in the academic performance of
students with differing smartphone behaviors (Table 8). Between-group differences
in academic performance were significant, F(3, 495) = 44.083, p < 0.001, Λ = 0.789,
η2 = 0.211, power = 0.999. Subsequently, because academic performance consists of
three dimensions, we performed univariate tests and an a posteriori comparison.
Table 9 presents the results of the univariate tests. Between-group differences
in learning activities were significant, (F[1, 497] = 40.8, p < 0.001, η2 = 0.076,
power = 0.999). Between-group differences in learning applications were also signif-
icant (F[1, 497] = 117.98, p < 0.001, η2 = 0.192, power = 0.999). Finally, differences

Table 7  Cluster analysis results Number of BIC BIC change Ratio Ratio of distance
Clusters of BIC measures
change

1 1073.416
2 801.463 − 271.954 1.000 2.397
3 709.753 − 91.710 .337 1.880
4 678.418 − 31.335 .115 1.378
5 665.887 − 12.531 .046 1.120
6 658.695 − 7.192 .026 1.052
7 653.697 − 4.998 .018 1.142
8 653.969 .272 − .001 1.674
9 669.141 15.172 − .056 1.179
10 687.669 18.528 − .068 1.178
11 709.026 21.357 − .079 1.018
12 730.666 21.640 − .080 1.088
13 753.570 22.904 − .084 1.140
14 778.241 24.671 − .091 1.009
15 803.025 24.785 − .091 1.057

BIC Bayesian information criterion

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Table 8  Multivariate analysis of variance results

Effect Λ F Hypothesis df Error df p value ηp2 Observed ­powerc

Intercept .041 3893.541 3 495 < .001*** .959 .999

Group .789 44.083 3 495 < .001*** .211 .999

Df degrees of freedom
***
p < .001

between the groups in learning attitudes were significant (F[1, 497] = 23.22,
p < 0.001, η2 = 0.045, power = 0.998). The a posteriori comparison demonstrated
that the high smartphone use group significantly outperformed the low smartphone
use group in all dependent variables with regard to academic performance. Thus,
hypothesis 4 is supported.

4.5 Smartphone behavior as the mediating variable impacting academic

performance

As suggested by Baron and Kenny (1986), smartphone behavior is a mediating vari-

able affecting academic performance. We examined the impact through the follow-
ing four-step process:

Step 1. The independent variable (parental control and students’ self-control)

must have a significant effect on the dependent variable (academic performance),
as in model 1 (please see Fig. 1).
Step 2. The independent variable (parental control and students’ self-control)
must have a significant effect on the mediating variable (smartphone behaviors),
as in model 2 (please see Fig. 2).
Step 3. When both the independent variable (parental control and student self-
control) and the mediator (smartphone behavior) are used as predictors, the
mediating variable (smartphone behavior) must have a significant effect on the
dependent variable (academic performance), as in model 3 (please see Fig. 3).
Step 4. In model 3, the regression coefficient of the independent variables (paren-
tal control and student self-control) on the dependent variables must be less than
in mode 1 or become insignificant.

As can be seen in Fig. 1, parental control and student self-control are observed
variables, and smartphone behavior is a latent variable. "Strict" is set to 0,
which means "Conditional", with "None" compared to "Strict". “Proactive high
self-control” is also set to 0. From Fig. 1 we find that the independent variables
have a significant effect on the dependent variable. The regression coefficient
of parental control is 0.176, t = 3.45 (p < 0.01); the regression coefficient of stu-
dents’ self-control is 0.218, t = 4.12 (p < 0.001), proving the fit of the model (Chi
Square = 13.96**, df = 4, GFI = 0.989, AGFI = 0.959, CFI = 0.996, TLI = 0.915,

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Table 9  Univariate analysis results
Dependent variable SS df MS F p value ηp2 Observed ­powera A posteriori comparisons

Learning activities Contrast 27.53 1 27.53 40.80 < .001*** .076 1.000
Error 335.31 497 .675 High group >
Learning applications Contrast 73.95 1 73.95 117.98 < .001*** .192 1.000 Low group
Error 311.53 497 .627
Learning attitudes Contrast 20.93 1 20.93 23.22 < .001*** .045 .998
Error 448.08 497 .902

SS sum of squares; df degrees of freedom; MS mean square

***
p < .001
Education and Information Technologies (2023) 28:6287–6320
Education and Information Technologies (2023) 28:6287–6320 6309

Fig. 2  Model 2: Model to test the impact of parental control and students’ self-control on smartphone
behavior

Fig. 3  Model 3: Both independent variables (parental control and student self-control) and mediators
(smartphone behavior) were used as predictors to predict dependent variables

RMSEA = 0.051, SRMR = 0.031). Therefore, the test results for Model 1 are in
line with the recommendations of Baron and Kenny (1986).
As can be seen in Fig. 2, the independent variables have a significant effect
on smartphone behaviors. The regression coefficient of parental control is 0.166,
t = 3.11 (p < 0.01); the regression coefficient of students’ self-control is 0.149,
t = 2.85 (p < 0.01). The coefficients of the model fit are: Chi Square = 15.10**,
df = 4, GFI = 0.988, AGFI = 0.954, CFI = 0.973, TLI = 0.932, RMSEA = 0.052,

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6310 Education and Information Technologies (2023) 28:6287–6320

SRMR = 0.039. Therefore, the results of the test of Model 2 are in line with the
recommendations of Baron and Kenny (1986).
As can be seen in Fig. 3, smartphone behaviors have a significant effect on
the dependent variable. The regression coefficient is 0.664, t = 10.2 (p < 0.001).
The coefficients of the model fit are: Chi Square = 91.04**, df = 16, GFI = 0.958,
AGFI = 0.905, CFI = 0.918, TLI = 0.900, RMSEA = 0.077, SRMR = 0.063. There-
fore, the results of the test of Model 3 are in line with the recommendations of Baron
and Kenny (1986).
As can be seen in Fig. 4, the regression coefficient of the independent variables
(parental control and student self-control) on the dependent variables is less than
in model 1, and the parental control variable becomes insignificant. The regression
coefficient of parental control is 0.013, t = 0.226 (p > 0.05); the path coefficient of
students’ self-control is 0.155, t = 3.07 (p < 0.01).
To sum up, we prove that smartphone behavior is the mediating variable to
impact the academic performance. Thus, hypothesis 5 is supported.

5 Discussion

This study investigated differences in the smartphone behavior of fifth and sixth
graders in Taiwan with different background variables (focus on parental control and
students’ self-control) and their effects on academic performance. The correlation

Fig. 4  Model 4: Model three’s regression coefficient of the independent variables (parental control and
student self-control) on the dependent variables

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Education and Information Technologies (2023) 28:6287–6320 6311

between smartphone behavior and academic performance was also examined.

Although smartphones are being used in elementary school learning activities, rela-
tively few studies have explored their effects on academic performance. In this study,
the proportion of girls who used smartphones to search for information was signifi-
cantly higher than that of boys. Past studies have been inconclusive about gender
differences in smartphone use. Lee and Kim (2018) observed no gender differences
in smartphone use, but did note that boys engaged in more smartphone use if their
parents set fewer restrictions. Kim et al. (2019) found that boys exhibited higher
levels of smartphone dependency than girls. By contrast, Kim (2017) reported that
girls had higher levels of smartphone dependency than boys did. Most relevant stud-
ies have focused on smartphone dependency; comparatively little attention has been
devoted to smartphone behavior. The present study contributes to the literature in
this regard.
Notably, this study found that parental control affected smartphone use. If the
participants’ parents imposed no restrictions, students spent more time on leisure
and entertainment and on interpersonal communication rather than on information
searches. This is conceivable, as Asian parents generally discourage their children
from using their smartphones for non-study related activities (such as entertainment
or making friends) but not for learning-related activities. If Asian parents believe
that using a smartphone can improve their child’s academic performance, they
will encourage their child to use it. Parents in Taiwan attach great importance to
their children’s academic performance (Lee et al., 2016). A considerable amount of
research has been conducted on parental attitudes or control in this context. Hwang
and Jeong (2015) suggested that parental attitudes mediated their children’s smart-
phone use. Similarly, Chang et al. (2019) observed that parental attitudes mediated
the smartphone use of children in Taiwan. Our results are consistent with extant evi-
dence in this regard. Lee and Ogbolu (2018) demonstrated that the stronger chil-
dren’s perception was of parental control over their smartphone use, the more fre-
quently they used their smartphones. The study did not further explain the activities
the children engaged in on their smartphones after they increased their frequency of
use. In the present study, the participants spent more time on their smartphones for
leisure and entertainment and for interpersonal communication than for information
searches.
Notably, this study also found that students’ self-control affected smartphone use.
Regarding the Pearson’s correlation analysis of smartphone behavior and aca-
demic performance, except for information searches and learning attitudes, all the
variables were significantly positively correlated. In other words, there was a posi-
tive correlation between smartphone behavior and academic performance. In their
systematic review, Amez and Beart (2020) determined that most empirical results
provided evidence of a negative correlation between smartphone behavior and aca-
demic performance, playing a more considerable role in that relationship than the
theoretical mechanisms or empirical methods in the studies they examined. The dis-
crepancy between our results and theirs can be explained by the between-study vari-
ations in the definitions of learning achievement or performance.
Regarding the present results on the differences in the academic performance of stu-
dents with varying smartphone behaviors, we carried out a cluster analysis, dividing the

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participants into a high smartphone use group and a low smartphone use group. Subse-
quent MANOVA revealed that the high smartphone use group academically outper-
formed the low smartphone use group; significant differences were noted in the academic
performance of students with different smartphone behaviors. Given the observed cor-
relation between smartphone behavior and academic performance, this result is not unex-
pected. The findings on the relationship between smartphone behavior and academic per-
formance can be applied to smartphone use in the context of education.
Finally, in a discussion of whether smartphone behavior is a mediator of academic per-
formance, it is proved that smartphone behavior is the mediating variable impacting aca-
demic performance. Our findings show that parental control and students’ self-control can
affect academic performance. However, the role of the mediating variable (smartphone
use behavior) means that changes in parental control have no effect on academic achieve-
ment at all. This means that smartphone use behaviors have a full mediating effect on
parental control. It is also found that students’ self-control has a partial mediating effect.
Our findings suggest that parental attitudes towards the control of smartphone use and
students’ self-control do affect academic performance, but smartphone use behavior has
a significant mediating effect on this. In other words, it is more important to understand
the children’s smartphone behavior than to control their smartphone usage. There have
been many studies in the past exploring the mediator variables for smartphone use addic-
tion and academic performance. For instance, Ahmed et al. (2020) found that the mediat-
ing variables of electronic word of mouth (eWOM) and attitude have a significant and
positive influence in the relationship between smartphone functions. Cho and Lee (2017)
found that parental attitude is the mediating variable for smartphone use addiction. Cho
et al. (2017) indicated that stress had a significant influence on smartphone addiction,
while self-control mediates that influence. In conclusion, the outcomes demonstrate that
parental control and students’ self-control do influence student academic performance in
primary school. Previous studies have offered mixed results as to whether smartphone
usage has an adverse or affirmative influence on student academic performance. This
study points out a new direction, thinking of smartphone use behavior as a mediator.
In brief, the participants spent more smartphone time on leisure and entertain-
ment and interpersonal communication, but the academic performance of the high
smartphone use group surpassed that of the low smartphone use group. This result
may clarify the role of students’ communication skills in their smartphone use.
As Kang and Jung (2014) noted, conventional communication methods have been
largely replaced by mobile technologies. This suggests that students’ conventional
communication skills are also shifting to accommodate smartphone use. Elementary
students are relatively confident in communicating with others through smartphones;
thus, they likely have greater self‐efficacy in this regard and in turn may be bet-
ter able to improve their academic performance by leveraging mobile technologies.
This premise requires verification through further research. Notably, high smart-
phone use suggests the greater availability of time and opportunity in this regard.
Conversely, low smartphone use suggests the relative lack of such time and oppor-
tunity. The finding that the high smartphone use group academically outperformed
the low smartphone use group also indicates that smartphone accessibility consti-
tutes a potential inequality in the learning opportunities of elementary school stu-
dents. Therefore, elementary school teachers must be aware of this issue, especially

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in view of the shift to online learning triggered by the COVID-19 pandemic, when
many students are dependent on smartphones and computers for online learning.

6 Conclusions and implications

This study examined the relationship between smartphone behavior and academic perfor-
mance for fifth and sixth graders in Taiwan. Various background variables (parental con-
trol and students’ self-control) were also considered. The findings provide new insights
into student attitudes toward smartphone use and into the impacts of smartphone use on
academic performance. Smartphone behavior and academic performance were corre-
lated. The students in the high smartphone use group academically outperformed the low
smartphone use group. This result indicates that smartphone use constitutes a potential
inequality in elementary school students’ learning opportunities. This can be explained as
follows: high smartphone use suggests that the participants had sufficient time and oppor-
tunity to access and use smartphones. Conversely, low smartphone use suggests that the
participants did not have sufficient time and opportunity for this purpose. Students’ aca-
demic performance may be adversely affected by fewer opportunities for access. Dispari-
ties between their performance and that of their peers with ready access to smartphones
may widen amid the prevalent class suspension and school closure during the ongoing
COVID-19 pandemic.
This study has laid down the basic foundations for future studies concerning the
influence of smartphones on student academic performance in primary school as the
outcome variable. This model can be replicated and applied to other social science
variables which can influence the academic performance of primary school students
as the outcome variable. Moreover, the outcomes of this study can also provide
guidelines to teachers, parents, and policymakers on how smartphones can be most
effectively used to derive the maximum benefits in relation to academic performance
in primary school as the outcome variable. Finally, the discussion of the mediating
variable can also be used as the basis for the future projects.

7 Limitations and areas of future research

This research is significant in the field of smartphone functions and the student
academic performance for primary school students. However, certain limitations
remain. The small number of students sampled is the main problem in this study. For
more generalized results, the sample data may be taken across countries within the
region and increased in number (rather than limited to certain cities and countries).
For more robust results, data might also be obtained from both rural and urban cent-
ers. In this study, only one mediating variable was incorporated, but in future stud-
ies, several other psychological and behavioral variables might be included for more
comprehensive outcomes. We used the SEM-based multivariate approach which
does not address the cause and effect between the variables, therefore, in future
work, more robust models could be employed for cause-and-effect investigation
amongst the variables.

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Appendix 1 Factor analysis results

Table 10  KMO and Bartlett’s Construct name KMO Bartlett’s Test df p Value
Test of Sphericity

Smartphone behavior .846 2198.32*** 105 < .001

Academic performance .731 1128.61*** 45 < .001

Table 11  Total variance explained of smartphone behavior

Construct name Total Variance Explained
Eigenvalues % of Variance Cumulative %

Interpersonal communication 4.40 29.33 29.33

Leisure and entertainment 2.27 15.13 44.47
Information searches 1.35 9.00 53.47

Table 12  Total variance Construct name Total Variance Explained

explained of academic
performance Eigenvalues % of Variance Cumulative %

Learning activities 2.93 29.30 29.30

Learning applications 2.94 19.41 47.71
Learning attitudes 1.11 11.09 59.81

Table 13  Factor loading of Item No Communalities factor

smartphone behavior
1 2 3

1 .466 .695
2 .527 .739
3 .357 .561
4 .631 .693
5 .504 .666
6 .718 .513
7 .490 610
8 .540 .601
9 .383 .618
10 .358 .522
11 .557 .766
12 .512 .768
13 .740 .805
14 .671 .792
15 .568 .748

13
Education and Information Technologies (2023) 28:6287–6320 6315

Table 14  Factor loading of Item No Communalities factor

academic performance
1 2 3

1 .568 .522
2 .651 .731
3 .491 .575
4 .675 .842
5 .726 .873
6 .535 .663
7 .585 .588
8 .653 .730
9 .505 .832

Acknowledgements The authors would like to express their gratitude to the school participants in the
study.

Author contributions Kung and Wang conceived of the presented idea. Kung, Wang and Hsieh devel-
oped the theory and performed the computations. Kung and Hsieh verified the analytical methods. Wang
encouraged Kung and Hsieh to verify the numerical checklist and supervised the findings of this work.
All authors discussed the results and contributed to the final manuscript.

Funding The work done for this study was financially supported by the Ministry of Science and Technol-
ogy of Taiwan under project No. MOST 109–2511-H-017–005.

Data availability The datasets generated during and/or analysed during the current study are available
from the corresponding author upon request.

Declarations
Conflict of interest The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.

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Authors and Affiliations

Jen Chun Wang1 · Chia‑Yen Hsieh2 · Shih‑Hao Kung1

Jen Chun Wang
jcwang@nknu.edu.tw
Shih‑Hao Kung
gsh-1@yahoo.com.tw
1
Department of Industry Technology Education, National Kaohsiung Normal University, 62,
Shenjhong Rd., Yanchao District, Kaohsiung 82446, Taiwan
2
Department of Early Childhood Education, National PingTung University, No.4‑18, Minsheng
Rd., Pingtung City, Pingtung County 900391, Taiwan

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