research paper about cell phones in school pdf

• Published: 11 November 2022

The impact of smartphone use on learning effectiveness: A case study of primary school students

• Jen Chun Wang 1 ,
• Chia-Yen Hsieh   ORCID: orcid.org/0000-0001-5476-2674 2 &
• Shih-Hao Kung 1  

Education and Information Technologies volume  28 ,  pages 6287–6320 ( 2023 ) Cite this article

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This study investigated the effects of smartphone use on the perceived academic performance of elementary school students. Following the derivation of four hypotheses from the literature, descriptive analysis, t testing, one-way analysis of variance (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 coefficients 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 students in the high smartphone use group academically outperformed those in the low smartphone use group. The results indicate that smartphone use constitutes a potential inequality in learning opportunities among elementary school students. Finally, in a discussion of whether smartphone behavior is a mediator of academic performance, 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.

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1 introduction.

The advent of the Fourth Industrial Revolution has stimulated interest in educational reforms for the integration of information and communication technology (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 countries (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 perceived as convenient, easy-to-use tools that promote interaction and multitasking 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 incorporating smartphones into the learning process as a means of addressing inappropriate use of smartphones in class (Anshari et al., 2017 ). For young children, there are differences in demand and attributes and some need for control depending upon the daily smartphone usage of the children (Cho & Lee, 2017 ). To avoid negative impacts, including interference with the learning process, teachers 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 suggested 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 integration of smartphone use into the academic environment. For example, Hawi and Samaha ( 2016 ) investigated whether high academic performance was possible for students at high risk of smartphone addiction. They provided strong evidence 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 effectiveness 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. Carrillo 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 technological literacy and the formulation of new teaching or learning strategies (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 effectiveness. 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 contrast, 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 facilitates or undermines learning (i.e., through distraction). Only a few studies have been conducted on the impacts of smartphone use on academic performance in elementary 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 information is available (Martin & Ertzberger, 2013 ).

Kim et al. ( 2014 ) focused on identifying factors that influence smartphone adoption 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 elementary 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, students tend to maintain social relationships by interacting with others through various 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, students 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 ).

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 elementary 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, contending that it can impede growth and development. To take steps towards resolving this conflict, we investigated smartphone use among elementary school students.

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 students. 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 commonly 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 elementary 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 performance. 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 frequentlyfrom 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 behavior. 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 (characterized by warmth, supervision, and rational explanation) was related to a lower likelihood 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 influence the behavioral intention of elementary school students toward smartphone use. The effect of parental control on smartphone use is also investigated in this study.

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 conscious 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 distinguishes 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. However, 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 ). Acting due to social pressure represents an externally regulated behavior, which SDT classifies as the most controlled form of motivation (Ryan & Deci, 2000 ). Individuals 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 motivations 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 temptation 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 produces pleasurable experiences. The assumption here is that users often subconsciously 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 ). Dispositional 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 temptation impulses lead to failure of self-control. Even when impossible to avoid a 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. Therefore, 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 values 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 traditionally 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 academic 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 “parental 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 regulation 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 strategies such as guilt induction and love withdrawal (Steinberg et al., 1992 ) and is 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 performance. 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 relationships 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% confidence 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. Furthermore, 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. However, 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 likelihood of enhancement in knowledge and academic performance is higher. This is because smartphones in this context are used as tools to obtain the information necessary 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 smartphone use have been inconsistent—positive, negative, or none. Thus, we explore the correlation between elementary school students’ smartphone use and learning effectiveness with regard to academic performance through the following hypotheses:

Hypothesis 3: Smartphone use is associated with learning effectiveness with regard to academic performance.

Hypothesis 4: Differences in smartphone use correspond to differences in learning 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 variable, parental control and self-control are independent variables, and academic performance is the dependent variable. We want to understand the mediation effect of this model.

Model 1: Model to test the impact of parental control and students’ self-control on academic performance

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 worldwide, 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 pandemic. However, the effectiveness of online learning for young children is limited by their parents’ technological literacy in terms of their ability to navigate learning platforms 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, definitively affects equity in online education. For example, in 2018, 14% of households in 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 studies, 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 ability, knowledge of educational concepts, and technological literacy, the availability of computer equipment and Internet connectivity also exert impacts. This is especially true for elementary school students, who rely on their parents in online learning 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.

3.1 Participants

Participants were recruited through stratified random sampling. They comprised 499 Taiwanese elementary school students (in grades 5 and 6) who had used smartphones 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 providing invalid responses, 499 individuals remained. The questionnaire was developed 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 principal component analysis and oblique rotation. From the exploratory factor analysis, 25 items (15 and 10 items on smartphone behavior and academic performance as constructs, respectively) were extracted and confirmed. According to the 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 questions 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 activities, learning applications, and learning attitudes. Learning activities are when students 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 smartphone 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 (Appendix 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 columns 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), “Reactive 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 smartphone behavior construct was 0.850, whereas that of the academic performance 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 smartphone 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 multivariate 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 mediator of academic performance.

4.1 Descriptive analysis

The descriptive analysis (Table 1 ) revealed that the parents of 71.1% of the participants ( 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. Regarding smartphone use on weekends, 54.1% and 44.1% used their smartphones during 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 proactivity), and ‘How much time do you spend on your smartphone in a day?’ (to investigate the effects of students' self-control). According to the Hsieh and Lin ( 2021 ), and Peng et al. ( 2022 ), addition is defined more than 4 h a day are defined as smartphone 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 dimensions: 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 proportion 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.

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 smartphones, 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 (meaning 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 students using their smartphones for this purpose, the proportion of parents enforcing no control over smartphone use was significantly higher than the proportions of parents 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-control 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 information searching. Significant differences were observed only for gender, with a significantly higher proportion of girls using their smartphones to search for 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 parents' 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 communication 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 interpersonal 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 smartphones (For example, they may only spend 2–3 h a day using their smartphone. During 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.

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 performance 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, η 2  = 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, η 2  = 0.076, power = 0.999). Between-group differences in learning applications were also significant ( F [1, 497] = 117.98, p  < 0.001, η 2  = 0.192, power = 0.999). Finally, differences between the groups in learning attitudes were significant ( F [1, 497] = 23.22, p  < 0.001, η 2  = 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 variable affecting academic performance. We examined the impact through the following 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 (parental control and student self-control) on the dependent variables must be less than in mode 1 or become insignificant.

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

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

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 students’ 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, 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, 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. Therefore, 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).

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

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 between smartphone behavior and academic performance was also examined. Although smartphones are being used in elementary school learning activities, relatively few studies have explored their effects on academic performance. In this study, the proportion of girls who used smartphones to search for information was significantly 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 studies 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 smartphone use. Similarly, Chang et al. ( 2019 ) observed that parental attitudes mediated the smartphone use of children in Taiwan. Our results are consistent with extant evidence in this regard. Lee and Ogbolu ( 2018 ) demonstrated that the stronger children’s perception was of parental control over their smartphone use, the more frequently 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 academic performance, except for information searches and learning attitudes, all the variables were significantly positively correlated. In other words, there was a positive 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 academic performance, playing a more considerable role in that relationship than the theoretical mechanisms or empirical methods in the studies they examined. The discrepancy between our results and theirs can be explained by the between-study variations in the definitions of learning achievement or performance.

Regarding the present results on the differences in the academic performance of students with varying smartphone behaviors, we carried out a cluster analysis, dividing the participants into a high smartphone use group and a low smartphone use group. Subsequent MANOVA revealed that the high smartphone use group academically outperformed the low smartphone use group; significant differences were noted in the academic performance of students with different smartphone behaviors. Given the observed correlation between smartphone behavior and academic performance, this result is not unexpected. The findings on the relationship between smartphone behavior and academic performance can be applied to smartphone use in the context of education.

Finally, in a discussion of whether smartphone behavior is a mediator of academic performance, it is proved that smartphone behavior is the mediating variable impacting academic 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 achievement 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 addiction and academic performance. For instance, Ahmed et al. ( 2020 ) found that the mediating 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 entertainment 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 better able to improve their academic performance by leveraging mobile technologies. This premise requires verification through further research. Notably, high smartphone use suggests the greater availability of time and opportunity in this regard. Conversely, low smartphone use suggests the relative lack of such time and opportunity. The finding that the high smartphone use group academically outperformed the low smartphone use group also indicates that smartphone accessibility constitutes a potential inequality in the learning opportunities of elementary school students. Therefore, elementary school teachers must be aware of this issue, especially 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 performance for fifth and sixth graders in Taiwan. Various background variables (parental control 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 correlated. 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 opportunity to access and use smartphones. Conversely, low smartphone use suggests that the participants did not have sufficient time and opportunity for this purpose. Students’ academic performance may be adversely affected by fewer opportunities for access. Disparities 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 centers. In this study, only one mediating variable was incorporated, but in future studies, 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.

Data availability

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

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

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

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Wang, J.C., Hsieh, CY. & Kung, SH. The impact of smartphone use on learning effectiveness: A case study of primary school students. Educ Inf Technol 28 , 6287–6320 (2023). https://doi.org/10.1007/s10639-022-11430-9

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Mobile phones: The effect of its presence on learning and memory

Clarissa Theodora Tanil

Department of Psychology, Sunway University, Selangor, Malaysia

Min Hooi Yong

Associated data.

All relevant data are within the manuscript.

Our aim was to examine the effect of a smartphone’s presence on learning and memory among undergraduates. A total of 119 undergraduates completed a memory task and the Smartphone Addiction Scale (SAS). As predicted, those without smartphones had higher recall accuracy compared to those with smartphones. Results showed a significant negative relationship between phone conscious thought, “how often did you think about your phone”, and memory recall but not for SAS and memory recall. Phone conscious thought significantly predicted memory accuracy. We found that the presence of a smartphone and high phone conscious thought affects one’s memory learning and recall, indicating the negative effect of a smartphone proximity to our learning and memory.

Introduction

Smartphones are a popular communication form worldwide in this century and likely to remain as such, especially among adolescents [ 1 ]. The phone has evolved from basic communicative functions–calls only–to being a computer-replacement device, used for web browsing, games, instant communication on social media platforms, and work-related productivity tools, e.g. word processing. Smartphones undoubtedly keep us connected; however, many individuals are now obsessed with them [ 2 , 3 ]. This obsession can lead to detrimental cognitive functions and mood/affective states, but these effects are still highly debated among researchers.

Altmann, Trafton, and Hambrick suggested that as little as a 3-second distraction (e.g. reaching for a cell phone) is adequate to disrupt attention while performing a cognitive task [ 4 ]. This distraction is disadvantageous to subsequent cognitive tasks, creating more errors as the distraction period increases, and this is particularly evident in classroom settings. While teachers and parents are for [ 5 ] or against cell phones in classrooms [ 6 ], empirical evidence showed that students who used their phones in class took fewer notes [ 7 ] and had poorer overall academic performance, compared to those who did not [ 8 , 9 ]. Students often multitask in classrooms and even more so with smartphones in hand. One study showed no significant difference in in-class test scores, regardless of whether they were using instant messaging [ 10 ]. However, texters took a significantly longer time to complete the in-class test, suggesting that texters required more cognitive effort in memory recall [ 10 ]. Other researchers have posited that simply the presence of a cell phone may have detrimental effects on learning and memory as well. Research has shown that a mobile phone left next to the participant while completing a task, is a powerful distractor even when not in use [ 11 , 12 ]. Their findings showed that mobile phone participants could perform similarly to control groups on simple versions of specific tasks (e.g. visual spatial search, digit cancellation), but performed much poorer in the demanding versions. In another study, researchers controlled for the location of the smartphone by taking the smartphones away from participants (low salience, LS), left the smartphone next to them (high salience/HS), or kept the smartphones in bags or pockets (control) [ 13 ]. Results showed that participants in LS condition performed significantly better compared to HS, while no difference was established between control and HS conditions. Taken together, these findings confirmed that the smartphone is a distractor even when not in use. Further, smartphone presence also increases cognitive load, because greater cognitive effort is required to inhibit distractions.

Reliance on smartphones has been linked to a form of psychological dependency, and this reliance has detrimental effect on our affective ‘mood’ states. For example, feelings of anxiety when one is separated from their smartphones can interfere with the ability to attend to information. Cheever et al. observed that heavy and moderate mobile phone users reported increased anxiety when their mobile phone was taken away as early as 10 minutes into the experiment [ 14 ]. They noted that high mobile phone usage was associated with higher risk of experiencing ‘nomophobia’ (no mobile phone phobia), a form of anxiety characterized by constantly thinking about one’s own mobile phones and the desire to stay in contact with the device [ 15 ]. Other studies reported similar separation-anxiety and other unpleasant thoughts in participants when their smartphones were taken away [ 16 ] or the usage was prohibited [ 17 , 18 ]. Participants also reported having frequent thoughts about their smartphones, despite their device being out of sight briefly (kept in bags or pockets), to the point of disrupting their task performance [ 13 ]. Taken together, these findings suggest that strong attachment towards a smartphone has immediate and lasting negative effects on mood and appears to induce anxiety.

Further, we need to consider the relationship between cognition and emotion to understand how frequent mobile phone use affects memory e.g. memory consolidation. Some empirical findings have shown that anxious individuals have attentional biases toward threats and that these biases affect memory consolidation [ 19 , 20 ]. Further, emotion-cognition interaction affects efficiency of specific cognitive functions, and that one’s affective state may enhance or hinder these functions rapidly, flexibly, and reversibly [ 21 ]. Studies have shown that positive affect improves visuospatial attention [ 22 ], sustained attention [ 23 ], and working memory [ 24 ]. The researchers attributed positive affect in participants’ improved controlled cognitive processing and less inhibitory control. On the other hand, participants’ negative affect had fewer spatial working memory errors [ 23 ] and higher cognitive failures [ 25 ]. Yet, in all of these studies–the direction of modulation, intensity, valence of experiencing a specific affective state ranged widely and primarily driven by external stimuli (i.e. participants affective states were induced from watching videos), which may not have the same motivational effect generated internally.

Present study

Prior studies have demonstrated the detrimental effects of one’s smartphone on cognitive function (e.g. working memory [ 13 ], visual spatial search [ 12 ], attention [ 11 ]), and decreased cognitive ability with increasing attachment to one’s phone [ 14 , 16 , 26 ]. Further, past studies have demonstrated the effect of affective state on cognitive performance [ 19 , 20 , 22 – 25 , 27 ]. To our knowledge, no study has investigated the effect of positive or negative affective states resulting from smartphone separation on memory recall accuracy. One study showed that participants reporting an increased level of anxiety as early as 10 minutes [ 14 ]. We also do not know the extent of smartphone addiction and phone conscious thought effects on memory recall accuracy. One in every four young adults is reported to have problematic smartphone use and this is accompanied by poor mental health e.g. higher anxiety, stress, depression [ 28 ]. One report showed that young adults reached for their phones 86 times in a day on average compared to 47 times in other age groups [ 29 ]. Young adults also reported that they “definitely” or “probably” used their phone too much, suggesting that they recognised their problematic smartphone use.

We had two main aims in this study. First, we replicated [ 13 ] to determine whether ‘phone absent’ (LS) participants had higher memory accuracy compared to the ‘phone present’ (HS). Second, we predicted that participants with higher smartphone addiction scores (SAS) and higher phone conscious thought were more likely to have lower memory accuracy. With regards to separation from their smartphone, we hypothesised that LS participants will experience an increase of negative affect or a decrease in positive affect and that this will affect memory recall negatively. We will also examine whether these predictor variables–smartphone addiction, phone conscious thought and affect differences—predict memory accuracy.

Materials and methods

Participants.

A total of 119 undergraduate students (61 females, M age = 20.67 years, SD age = 2.44) were recruited from a private university in an Asian capital city. To qualify for this study, the participant must own a smartphone and does not have any visual or auditory deficiencies. Using G*Power v. 3.1.9.2 [ 30 ], we require at least 76 participants with an effect size of d = .65, α = .05 and power of (1-β) = .8 based on Thornton et al.’s [ 11 ] study, or 128 participants from Ward’s study [ 13 ].

Out of 119 participants, 43.7% reported using their smartphone mostly for social networking, followed by communication (31.1%) and entertainment (17.6%) (see Table 1 for full details on smartphone usage). Participants reported an average smartphone use of 8.16 hours in a day ( SD = 4.05). There was no significant difference between daily smartphone use for participants in the high salience (HS) and low salience groups (LS), t (117) = 1.42, p = .16, Cohen’s d = .26. Female participants spent more time using their smartphones over a 24-hour period ( M = 9.02, SD = 4.10) compared to males, ( M = 7.26, SD = 3.82), t (117) = 2.42, p = .02, Cohen’s d = .44.

Ethical approval and informed consent

The study was conducted in accordance with the protocol approved by the Department of Psychology Research Ethics Committee at Sunway University (approval code: 20171090). All participants provided written consent before commencing the study and were not compensated for their participation in the study.

Study design

Our experimental study was a mixed design, with smartphone presence (present vs absent) as a between-subjects factor, and memory task as a within-subjects factor. Participants who had their smartphone out of sight formed the ‘Absent’ or low-phone salience (LS) condition, and the other group had their smartphone placed next to them throughout the study, ‘Present’ or high-phone salience (HS) condition. The dependent variable was recall accuracy from the memory test.

Working memory span test

A computerized memory span task ‘Operation Span (OS)’ retrieved from software Wadsworth CogLab 2.0 was used to assess working memory [ 31 ]. A working memory span test was chosen as a measure to test participants’ memory ability for two reasons. First, participants were required to learn and memorize three types of stimuli thus making this task complex. Second, the duration of task completion took approximately 20 minutes. This was advantageous because we wanted to increase separation-anxiety [ 16 ] as well as having the most pronounced effect on learning and memory without the presence of their smartphone [ 9 ].

The test comprised of three stimulus types, namely words (long words such as computer, refrigerator and short words like pen, cup), letters (similar sound E, P, B, and non-similar sound D, H, L) and digits (1 to 9). The test began by showing a sequence of items on the left side of the screen, with each item presented for one second. After that, participants were required to recall the stimulus from a 9-button box located on the right side of the screen. In order to respond correctly, participants were required to click on the buttons for the items in the corresponding order they were presented. A correct response increases the length of stimulus presented by one item (for each stimulus category), while an incorrect response decreases the length of the stimulus by one item. Each trial began with five stimuli and increased or decreased depending on the participants’ performance. The minimum length possible was one while the maximum was ten. Each test comprised of 25 trials with no time limit and without breaks between trials. Working memory ability was measured through the number of correct responses over total trials: scores ranged from 0 to 25, with the highest score representing superior working memory.

Positive and Negative Affect Scale (PANAS)

We used PANAS to assess the current mood/affective state of the participants with state/feeling-descriptive statements [ 32 ]. PANAS has ten PA statements e.g. interested, enthusiastic, proud, and ten NA statements e.g. guilty, nervous, hostile. Each statement was measured using a five-point Likert scale ranging from very slightly or not at all to extremely, and then totalled to form overall PA or NA score with higher scores representing higher levels of PA or NA. In the current study, the internal reliability of PANAS was good with a Cronbach’s alpha coefficient of .819, and .874 for PA and NA respectively.

Smartphone Addiction Scale (SAS)

SAS is a 33-item self-report scale used to examine participants’ smartphone addiction [ 33 ]. SAS contained six sub-factors; daily-life disturbance that measures the extent to which mobile phone use impairs one’s activities during everyday tasks (5 statements), positive anticipation to describe the excitement of using phone and de-stressing with the use of mobile phone (8 statements), withdrawal refers to the feeling of anxiety when separated from one’s mobile phone (6 statements), cyberspace-oriented relationship refers to one’s opinion on online friendship (7 statements), overuse measures the excessive use of mobile phone to the extent that they have become inseparable from their device (4 statements), and tolerance points to the cognitive effort to control the usage of one’s smartphone (3 statements). Each statement was measured using a six-point Likert scale from strongly disagree to strongly agree, and total SAS was identified by totalling all 33 statements. Higher SAS scores represented higher degrees of compulsive smartphone use. In the present study, the internal reliability of SAS was identified with Cronbach's alpha correlation coefficient of .918.

Phone conscious thought and perceived effect on learning

We included a one-item question for phone conscious thought: “During the memory test how often do you think of your smartphone?”. The aim of this question was two-fold; first was to capture endogenous interruption experienced by the separation, and second to complement the smartphone addiction to reflect current immediate experience. Participants rated this item on a scale of one (none to hardly) to seven (all the time). We also included a one-item question on how much they perceived their smartphone use has affected their learning and attention: “In general, how much do you think your smartphone affects your learning performance and attention span?”. This item was similarly rated on a scale of one (not at all) to seven (very much).

We randomly assigned participants to one of two conditions: low-phone salience (LS) and high-phone salience (HS). Participants were tested in groups of three to six people in a university computer laboratory and seated two seats apart from each other to prevent communication. Each group was assigned to the same experimental condition to ensure similar environmental conditions. Participants in the HS condition were asked to place their smartphone on the left side of the table with the screen facing down. LS participants were asked to hand their smartphone to the researcher at the start of the study and the smartphones were kept on the researcher’s table throughout the task at a distance between 50cm to 300cm from the participants depending on their seat location, and located out of sight behind a small panel on the table.

At the start of the experiment, participants were briefed on the rules in the experimental lab, such as no talking and no smartphone use (for HS only). Participants were also instructed to silence their smartphones. They filled in the consent form and demographic form before completing the PANAS questionnaire. They were then directed to CogLab software and began the working memory test. Upon completion, participants were asked to complete the PANAS again followed by the SAS, phone conscious thought, and their perception of their phone use on their learning performance and attention span. The researcher thanked the participants and returned the smartphones (LS condition only) at the end of the task.

Statistical analysis

We examined for normality in our data using the Shapiro-Wilk results and visual inspection of the histogram. For the normally distributed data, we analysed our data using independent-sample t -test for comparison between groups (HS or LS), paired-sample t test for within groups (e.g. before and after phone separation), and Pearson r for correlation. Non-normally distributed or ranked data were analysed using Spearman rho for correlation.

Preliminary analyses

Our female participants reported using their smartphone significantly longer than males, and so we examined the effects of gender on memory recall accuracy. We found no significant difference between males and females on memory recall accuracy, t (117) = .18, p = .86, Cohen’s d = .03. Subsequently, data were collapsed, analysed and reported on in the aggregate.

Smartphone presence and memory recall accuracy

An independent-sample t- test was used to examine whether participants’ performance on a working memory task was influenced by the presence (HS) or absence (LS) of their smartphone. Results showed that participants in the LS condition had higher accuracy ( M = 14.21, SD = 2.61) compared to HS ( M = 13.08, SD = 2.53), t (117) = 2.38, p = .02, Cohen’s d = .44 (see Fig 1 ). The effect size ᶇ 2 = .44 indicates that smartphone presence/salience has a moderate effect on participant working memory ability and a sensitivity power of .66.

Relationship between Smartphone Addiction Score (SAS), higher phone conscious thought and memory recall accuracy

Sas and memory recal.

We first examined participants’ SAS scores between the two conditions. Results showed no significant difference between the LS (M = 104.64, SD = 24.86) and HS (M = 102.70, SD = 20.45) SAS scores, t (117) = .46, p = .64, Cohen’s d = .09. We predicted that those with higher SAS scores will have lower memory accuracy, and thus we examined the relationship between SAS and memory recall accuracy using Pearson correlation coefficient. Results showed that there was no significant relationship between SAS and memory recall accuracy, r = -.03, n = 119, p = .76. We also examined the SAS scores between the LS and HS groups on memory recall accuracy scores. In the LS group, no significant relationship was established between SAS score and memory accuracy, r = -.04, n = 58, p = .74. Similarly, there was no significant relationship between SAS score and memory accuracy in the HS group, r = .10, n = 61, p = .47. In the event that one SAS subscale may have a larger impact, we examined the relationship between each subscale and memory recall accuracy. Results showed no significant relationship between each sub-factor of SAS scores and memory accuracy, all p s > .12 (see Table 2 ).

Phone conscious thought and memory accuracy

We found a significant negative relationship between phone conscious thought and memory recall accuracy, r S = -.25, n = 119, p = .01. We anticipated a higher phone conscious thought for the LS group since their phone was kept away from them during the task and examined the relationship for each condition. Results showed a significant negative relationship between phone conscious thought and memory accuracy in the HS condition, r S = -.49, n = 61, p = < .001, as well as the LS condition, r S = -.27, n = 58, p = .04.

Affect/mood changes after being separated from their phone

We anticipated that our participants may have experienced either an increase in negative affect (NA) or a decrease in positive affect (PA) after being separated from their phone (LS condition).

We first computed the mean difference (After minus Before) for both positive ‘PA difference’ and negative affect ‘NA difference’. A repeated-measures 2 (Mood change: PA difference, NA difference) x 2 (Conditions: LS, HS) ANOVA was conducted to determine whether there is an interaction between mood change and condition. There was no interaction effect of mood change and condition, F (1, 117) = .38, p = .54, n p 2 = .003. There was a significant effect of Mood change, F (1, 117) = 13.01, p < .001, n p 2 = .10 (see Fig 2 ).

Subsequent post-hoc analyses showed a significant decrease in participants’ positive affect before ( M = 31.12, SD = 5.79) and after ( M = 29.36, SD = 6.58) completing the memory task in the LS participants, t (57) = 2.48, p = .02, Cohen’s d = .28 but not for the negative affect, Cohen’s d = .07. A similar outcome was also shown in the HS condition, in which there was a significant decrease in positive affect only, t (60) = 3.45, p = .001, Cohen’s d = .37 (see Fig 2 ).

PA/NA difference on memory accuracy

We predicted that LS participants will experience either an increase in NA and/or a decrease in PA since their smartphones were taken away and that this will affect memory recall negatively. Results showed that LS participants who experienced a higher NA difference had poorer memory recall accuracy ( r s = -.394, p = .002). We found no significant relationship between NA difference and memory recall accuracy for HS participants ( r s = -.057, p = .663, n = 61) and no significant relationship for PA difference in both HS ( r s = .217, p = .093) and LS conditions ( r s = .063, p = .638).

Relationship between phone conscious thought, smartphone addiction scale and mood changes to memory recall accuracy

Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, multicollinearity and homoscedasticity. There was a significant positive relationship between SAS scores and phone conscious thought, r S = .25, n = 119, p = .007. Using the enter method, we found that phone conscious thought explained by the model as a whole was 19.9%, R 2 = .20, R 2 Adjusted = .17, F (4, 114) = 7.10, p < .001. Phone conscious thought significantly predicted memory recall accuracy, b = -.63, t (114) = 4.76, p < .001, but not for the SAS score, b = .02, t (114) = 1.72, p = .09, PA difference score, b = .05, t (114) = 1.29, p = .20, and NA difference score, b = .06, t (114) = 1.61, p = .11.

Perception between phone usage and learning

For the participants’ perception of their phone usage on their learning and attention span, we found no significant difference between LS ( M = 4.22, SD = 1.58) and HS participants ( M = 4.07, SD = 1.62), t (117) = .54, p = .59, Cohen’s d = .09. There was also no significant correlation between perceived cognitive interference and memory accuracy, r = .07, p = .47.

We aimed [ 1 ] to examine the effect of smartphone presence on memory recall accuracy and [ 2 ] to investigate the relationship between affective states, phone conscious thought, and smartphone addiction to memory recall accuracy. For the former, our results were consistent with prior studies [ 11 – 13 ] in that participants had lower accuracy when their smartphone was next to them (HS) and higher accuracy when separated from their smartphones (LS). For the latter, we predicted that the short-term separation from their smartphone would evoke some anxiety, identified by either lower PA or higher NA post-test. Our results showed that both groups had experienced a decrease in PA post-test, suggesting that the reduced PA is likely to have stemmed from the prohibited usage (HS) and/or separation from their phone (LS). Our results also showed lower memory recall in the LS group who experienced higher NA providing some evidence that separation from their smartphone does contribute to feelings of anxiety. This is consistent with past studies in which participants reported increased anxiety over time when separated from their phones [ 14 ], or when smartphone usage was prohibited [ 17 ].

We also examined another variable–phone conscious thought–described in past studies [ 11 , 13 ], as a measure of smartphone addiction. Our findings showed that phone conscious thought is negatively correlated to memory recall in both HS and LS groups, and uniquely contributed 19.9% in our regression model. We propose that phone conscious thought is more relevant and meaningful compared to SAS as a measure of smartphone addiction [ 15 ] because unlike the SAS, this question can capture endogenous interruptions from their smartphone behaviour and participants were to simply report their behaviour within the last hour. The SAS is better suited to describe problematic smartphone use as the statements described behaviours over a longer duration. Further, SAS statements included some judgmental terms such as fretful, irritated, and this might have influenced participants’ ability in recalling such behaviour. We did not find any support for high smartphone addiction to low memory recall accuracy. Our participants in both HS and LS groups had similar high SAS scores, and they were similar to Kwon et al. [ 33 ] study, providing further evidence that smartphone addiction is relatively high in the student population compared to other categories such as employees, professionals, unemployed. Our participants’ high SAS scores and primary use of the smartphone was for social media signals potential problematic users [ 34 ]. Students’ usage of social networking (SNS) is common and the fear of missing out (FOMO) may fuel the SNS addiction [ 35 ]. Frequent checks on social media is an indication of lower levels of self-control and may indicate a need for belonging.

Our results for the presence of a smartphone and frequent phone conscious thought on memory recall is likely due to participants’ cognitive load ‘bandwidth effect’ that contributed to poor memory recall rather than a failure in their memory processes. Past studies have shown that participants with smartphones could generally perform simple cognitive tasks as well as those without, suggesting that memory failure in participants themselves to be an unlikely reason [ 1 , 3 , 5 ]. Due to our study design, we are unable to tease apart whether the presence of the smartphone had interfered with encoding, consolidation, or recall stage in our participants. This is certainly something of consideration for future studies to determine which aspects of memory processes are more susceptible to smartphone presence.

There are several limitations in our study. First, we did not ask the phone conscious thought at specific time points during the study. Having done so might have determined whether such thoughts impaired encoding, consolidating, or retrieval. Second, we did not include the simple version of this task as a comparison to rule out possible confounds within the sample. We did maintain similar external stimuli in their environment during testing, e.g. all participants were in one specific condition, lab temperature, lab noise, and thereby ruling out possible external factors that may have interfered with their memory processes. Third, the OS task itself. This task is complex and unfamiliar, which may have caused some disadvantages to some participants. However, the advantage of an unfamiliar task requires more cognitive effort to learn and progress and therefore demonstrates the limited cognitive load capacity in our brain, and whether such limitation is easily affected by the presence of a smartphone. Future studies could consider allowing participants to use their smartphone in both conditions and including eye-tracking measures to determine their smartphone attachment behaviour.

Implications

Future studies should look into the online learning environment. Students are often users of multiple electronic devices and are expected to use their devices frequently to learn various learning materials. Because students frequently use their smartphones for social media and communication during lessons [ 34 , 36 ], the online learning environment becomes far more challenging compared to a face-to-face environment. It is highly unlikely that we can ban smartphones despite evidence showing that students performed poorer academically with their smartphones presented next to them. The challenge is then to engage students to remain focused on their lessons while minimising other content. Some online platforms (e.g. Kahoot and Mentimeter) create a fun interactive experience to which students complete tasks on their smartphones and allow the instructor to monitor their performance from a computer. Another example is to use Twitter as a classroom tool [ 37 ].

The ubiquitous nature of the smartphone in our lives also meant that our young graduates are constantly connected to their smartphones and very likely to be on SNS even at work. Our findings showed that the most frequently used feature was the SNS sites e.g. Instagram, Facebook, and Twitter. Being frequently on SNS sites may be a challenge in the workforce because these young adults need to maintain barriers between professional and social lives. Young adults claim that SNS can be productive at work [ 38 ], but many advise to avoid crossing boundaries between professional and social lives [ 39 , 40 ]. Perhaps a more useful approach is to recognise a good balance when using SNS to meet both social and professional demands for the young workforce.

In conclusion, the presence of the smartphone and frequent thoughts of their smartphone significantly affected memory recall accuracy, demonstrating that they contributed to an increase in cognitive load ‘bandwidth effect’ interrupting participants’ memory processes. Our initial hypothesis that experiencing higher NA or lower PA would have reduced their memory recall was not supported, suggesting that other factors not examined in this study may have influenced our participants’ affective states. With the rapid rise in the e-learning environment and increasing smartphone ownership, smartphones will continue to be present in the classroom and work environment. It is important that we manage or integrate the smartphones into the classroom but will remain a contentious issue between instructors and students.

Acknowledgments

We would like to thank our participants for volunteering to participate in this study, and comments on earlier drafts by Louisa Lawrie and Su Woan Wo. We would also like to thank one anonymous reviewer for commenting on the drafts.

Funding Statement

MHY received funding from Sunway University (GRTIN-RRO-104-2020 and INT-RRO-2018-49).

Data Availability

• PLoS One. 2020; 15(8): e0219233.

Decision Letter 0

27 Aug 2019

PONE-D-19-17118

Dear Dr. Yong, ,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Your study addresses an interesting question about the impact of mobile phones on memory.  One area that raised o concerns was your assessment of phone conscious thought.  First you need to provide a clear conceptual  definition of this construct and also your rationale for how to assess it.  In the discussion you seem to imply that phone conscious thought is measuring separation anxiety while there was no assessment of anxiety. Also what is the rationale for measuring affect before and after the memory assessment/?  This point needs to be clarified.    There are also concerns about the analysis of mood changes before and after the memory assessment.  These analyses need to be described more clearly.  Both reviewers raised concerns about your design in terms of your control group.  You need to acknowledge the limitations of your design in the discussion and discuss  how it limits your theoretical interpretation. Overall much more care must be given to the writing of the manuscript.  Reviewer 1 has pointed out numerous examples of how the writing could be improved or clarified.  You must address all points raised by both reviewers in your revised manuscript.  

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Partly

Reviewer #2: Partly

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: No

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The present study examined the mnemonic consequences associated with the presence of a smartphone. Overall, the authors found that participants without their cellphones had higher accuracy scores than those who had their cell phones present. They also found a negative correlation between accuracy and "phone conscious thought."

Overall, I think this is an interesting area of research. However, the following issues need to be addressed before I can recommend publication. I will start with the larger issues before moving to the smaller issues:

Larger issues

-Probably the biggest issue I found was the interpretation of their results. For example, on pg. 17, the authors state that "Although we did not find a significant relationship between SAS to memory accuracy, our measurements to 'phone conscious thought' is more relevant and meaningful because it measured participants separation anxiety..." This simply cannot be true: First, the question representing" phone conscious thought" asks "During the memory test how often do you think of your smartphone?" What does this even mean, exactly? How did participants interpret this question? Either way, I think it is quite a stretch to consider this anxiety. And, second, the SAS included a "'Withdrawal' sub-factor [that] describ[ed] the feeling of anxiety when separated from one's mobile phone." (pg. 9), but the authors found no significant correlations for any of the subfactors. Thus, not sure how a vague question about thoughts better represents anxiety than the specify subfactor of the SAS.

-Additionally, the authors suggestion that the decrease in positive affect is the result of "prohibited usage/or separation from their phone" (pg. 18). But the authors have no data to support this. For all they know, the participants had a decrease in positive affect simply because they were participating in a study since both groups exhibited this.

-In terms of the procedure, I'm a little concerned that only the "HS group" were told "no phone use." Obviously, I get the logic of this given that the phone was present for them but not for the "LS group." However, this could be a significant confound. Indeed, this could have drawn the participants attention to the fact that they couldn't use it and, in turn, could have distracted them, not simply because it was present but because of the fact that they were told they couldn't use their phone.

-Additionally, did the authors run any preliminary analysed based on how many participants were in each group when they participated? Given the importance just the mere presence of a cell is for the present study, the present of others could have influenced their results as well.

Smaller Issues:

-How is the reader supposed to know what "phone conscious though" means in the abstract?

-Pg. 2, Lines 13-14: A citation is needed to support this.

-Pg. 2 and throughout: "e.g." and "i.e." should only be used in parentheses. Otherwise, it should be "for example" and "that is" respectively and should always have commas around them.

-Pg. 2, Line 19: "Undoubtedly, the constant connectivity is applauded and desired..." This is way too editorial.

-Pg. 3, Line 38: Describe what the "digit cancelation task" is

-Pg. 3, Lines 41-42: "a mobile phone or a phone-sized notebook placed on participant's table before complete the tasks." Is not a complete sentence.

-Pg. 3, Line 42: "...showed no significant on..." Awkward. Reword

-Pg. 3, Line 43: Insert "the" between "during" and "simple"

-Pg. 3, Line 52: "in" should be "on" (there are a lot of typos throughout. I won't highlight them all, but a careful proofreading is necessary

-Pg. 3, Lines 54 & 57: Why do the authors provide the citation number to Ward et al., at the second instance and not the first?

-Pg. 4, Lines 73-78: I think all those sentences could be integrated and stated much more succinctly

-Pg. 5, Line 89 and throughout: The authors use the term "memory" throughout. However, there are many different types of memory. They should specify what they mean exactly by "memory" at each instance.

-Pg. 5, Prior to "present study": I think the authors could do a better job of more explicitly stating what gap in the literature the their study will fill.

-Results: Generally speaking, all t-tests should include cohen's d

-Pg. 7, Line 138: "begun" should be "began."

-Pg. 8, Line 153 & 161: Technically, the 5 should be spelled out. However, at the very least, keep it consistent. That is, the authors us 5 and spell out six.

-Smartphone addiction Scale: Many of the sentences in this section have errors and need to be fixed. Additionally, the authors use "secondly" on line 167, but there's no "first" and there's no "third," etc... Also, examples of each of the sub-factors should be included.

-Pg. 10: Some of this should be in the materials, not the procedure.

-Pg. 11, Gender: Why not include this analysis as a preliminary analysis. If gender, alternatively, is an important issue, then is should be set up as such in the lit review and the authors should examine the interaction with an F-test.

-Pg. 12, Lines 215-220: This should be a preliminary analysis. There's no reason to expect a difference between the groups assuming they were assigned randomly

-Pg. 13 (and elsewhere): The authors sometimes repeat the question in the results. This isn't needed. It's redundant.

-Pg. 14: Why didn't the authors run an ANOVA to examine for an interaction between mood change and condition?

-Pg. 15: More information is needed in terms of the variables included in the model.

-Pg. 18: There are no studies suggested under "Further Studies." The closest is a meaningless sentence: "Future studies should look into the online learning environment."

-Pgs. 18-19: "These behaviors are likely to remain the same when students graduate and move into the workforce." Can the authors provide a citation to back this up or what are the authors basing this on?

-Pg. 19, Lines 327-330: I don't understand this sentence or example...

-Pg. 19, Lines 342-343: "...the extent of the device purpose..." is awkward sounding.

Overall, many typos and awkward phrases. A careful proofreading is necessary.

Reviewer #2: 1. Is the manuscript technically sound, and do the data support the conclusions?

• How was sample size determined? Seems arbitrary, with no power analysis.

• The addition of “phone-conscious thought” is a construct that does not seem to be validated in the peer reviewed literature. It’s ok to include this, but the methods behind the development of these questions should be clearly stated, and the authors must define this construct. There are some problems with how it is defined, because the question used relies specifically on phone-related thoughts during the task, while the phone is either in their presence (HS) or absent (LS). So, this question appears to serve as more of a manipulation check rather than a true measurement of phone-conscious thought. There are many issues with the construct of “phone-conscious thought” in the current manuscript.

• Why is affect measured both before and after the memory test? Explain the rationale. Is the memory test expected to influence mood in any way?

• The inclusion of the phone-conscious thought question in the beginning of the study may have primed participants to think about their phones more overall, and this may have inflated the differences between the LS and HS groups.

• There should have been a 3rd control group where participants were given no instruction about what to do with their phone. This would help assess whether the LS group experienced lower recall or if the HS group experienced higher recall, relative to baseline.

*2. Has the statistical analysis been performed appropriately and rigorously?

• Results for the affect/mood changes are very unclear and should be edited to be more precise. Needs to be much more descriptive.

*3. Have the authors made all data underlying the findings in their manuscript fully available?

*4. Is the manuscript presented in an intelligible fashion and written in standard English?

The writing is unclear at times with strange vocabulary choices (e.g. “Undoubtedly, the constant connectivity is applauded and desired but this has also spiralled into an obsession with the device for many individuals” lines 19-20). What do the authors mean by “applauded and desired”? Further, writing around the explanations of the relevant literature is imprecise and should be cleaned up so that no previous findings can be mischaracterized. Requires rigorous editing to be publishable, in my opinion.

Lines 57-58: In which direction? And in which tasks? All of them? Needs much greater precision.

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Author response to Decision Letter 0

18 Oct 2019

18 October 2019

Dear Academic Editor,

We would like to express our thanks and gratitude for the helpful comments raised in our paper. Below is a point-by-point response to each comment/question. Please note that the line numbers and pages is taken from the clean version of the revised manuscript. The citations and references are also taken from the clean manuscript, and as such the numbering of the references will be off in this letter.

Best regards,

C Tanil & MH Yong

** we thank you for your insightful comments. We have addressed each point in subsequent pages.

*** We thank the reviewer for this comment.

We should explain our reasoning for asking ‘phone conscious thought’ question. In Ward et al.’s study, they included three questions post-task, and we used two out of three questions. The two questions were (1) phone conscious thought “how often were you thinking about your cellphone” and (2) “…to what extent they believed their phones affected their performance and attention spans” (p. 145). The third question was about phone location, and we did not ask this question because we only had two locations and is a pointless question in our study. Ward et al. found that as smartphone salience increases (measured by the 3 questions), available cognitive capacity decreases – which is an indication that this particular question is meaningful to tap endogenous interruptions due to smartphone-related usage throughout the task. Even though the participants in both LS and HS conditions were not allowed to use their phone, their high phone use (average use per day in our sample was 8.16 hours, and 47% participants or 56 out of 119, are considered as addicted when compared to Kwon’s sample) might have evoked such thoughts, as suggested by Wilmer and Chien (2017) in their review. Some participants consider their smartphone as a ‘limb’ and losing this ‘limb’ is more common and has powerful effects than previously thought.

In Kwon et al.’s paper, the authors described the withdrawal sub-factor as “…involves being impatient, fretful, and intolerable without a smartphone, constantly having one’s smartphone in one’s mind even while not using it, never giving up using one’s smartphone, and becoming irritated when bothered while using one’s smartphone…” (p. 7). The 6 specific questions are as follows:

1. Won’t be able to stand not having a smartphone

2. Feeling impatient and fretful when I am not holding my smartphone

3. Having my smartphone in my mind even when I’m not using it

4. I will never give up using my smartphone even when my daily life is already greatly affected by it.

5. Getting irritated when bothered while using my smartphone

6. Bringing my smartphone to the toilet even when I am in a hurry to get there

One of the bigger challenges in using self-reported survey such as SAS is that these questions brings further attention to their behaviour which may then indirectly affects their response behaviour “social desirability” and/or inability to recall the frequency of such behaviour. Having the phone conscious thought is more spot-on and without the risk of both social desirability (negative terms such as impatient, fretful, irritation) and asking individuals to reflect on their past behaviour.

As to what our participants thought of seeing this question, we think that this is a simple straightforward question. We have since added more information about phone conscious thought in Abstract (page 2, line 6), Introduction (page 6, line 91-97), and Discussion (page 19, line 347-353).

*** We thank the reviewer for this comment. Indeed, both LS and HS groups experienced a decrease in PA. We realised that the sentences were misleading, and we apologise for the confusion. We have since reworded the sentences, see below and also on page 20, Line 364-369.

“While both groups showed a decrease in PA after completing the tasks, it is possible that the reduced PA is likely to have stemmed from the prohibited usage “HS” and/or separation from their phone “LS”. This is consistent with Cheever et al. (15), whose participants reported increased anxiety over time when separated from their phones and with Clayton, Leshner and Almond (18) findings, where participants were unable to use their phone.”

*** Thank you for this comment. The participants were informed to put their phones on silent, and either leave them at their side (HS) or hand them over to the researcher (LS). No phone use instruction was provided to both groups to prevent one group from accessing their phone over another. We have included this confound in the Discussion and suggested improvements. Please see revised section on page 20, line 369-373.

“Future studies could consider allowing participants to use their phone in both conditions and including eye tracking measures to determine their phone attachment behaviour.”

*** We thank the reviewer for this comment. We did not analyse for the presence of others as each session was mainly comprised of 3 participants only. We only had 2 sessions of 6 pax per session throughout.

*** Thank you for highlighting this. Please see revised section on page 2, line 6.

*** Thank you. We have added a new citation “GeekWire”. Please see the addition on page 3, line 14.

*** Thank you. We have made the changes throughout.

*** Thank you. We have revised the sentence (see below) and on page 3, line 18-19.

“Smartphones today have many functions that allows one to be constantly connected to others but this …”

*** Thank you for highlighting this omission. We have added the following sentence on page 4, line 40-44.

“The digit cancellation task involves crossing out one digit from a series of numbers with reference to a target number. Performance is measured by referring to the number of lines completed and a cancellation score based on the total number of targets possible for the lines completed minus the number of errors made (failure to cancel a target or mistakenly cancelled an inappropriate number).”

*** Thank you. We have revised the sentence to “…two groups; a mobile phone or a phone-sized notebook, which were placed on participant’s table before...” Please see page 4, line 46.

*** Thank you. We have revised the sentence to “…significance difference on performance between the phone and notebook condition for the simple digit ….” Please see page 4, line 47.

*** Thank you. Please refer to the above comment as the sentence has been revised.

*** Thank you. We have engaged a native English speaker to proof read our revised manuscript.

*** Thank you for highlighting this. We have since revised this.

*** Thank you. We have made the changes. Please see page 5, line 77-83.

*** Thank you. We have since included specific types of memory when describing past studies in earlier and subsequent pages.

*** Thank you. We have included a research gap in our aim under Present Study. Please see section below, and also found on page 7, line 112-121.

“Prior studies have demonstrated the detrimental effects of one’s smartphone on cognitive function (e.g. working memory (13), visual spatial search (14), attention (12)), and decreased cognitive ability with increasing attachment to one’s phone (15,17,20). In addition to the presence of a mobile phone , it is also possible that one’s current affective state influences cognitive performance (21–23). But we are uncertain whether one’s current positive or negative affective / mood states plays a bigger role on cognitive function such as memory recall accuracy, suggesting a more complex relationship between current mood states and memory recall accuracy. To our knowledge, no study has examined the relationship between mood states and memory recall accuracy, with smartphone addiction and phone conscious thought as potential mediators. We hypothesised … “

*** Thank you. We have added Cohen’s d for all t-tests.

*** Thank you. We have made the change.

*** Thank you. We have made the changes to include sample questions for each sub-factor. Please see page 10-11, line 190-216.

*** We have since relooked at our procedure and move out some items (e.g. phone conscious thought, and perception on learning) into Materials.

*** Thank you for this comment. Gender is not of interest in this study. However, we found that in our sample, females spent more time on their phone compared to males and wanted to determine if there is a gender effect on memory accuracy. We have included a preliminary analysis to include gender analysis under Results section. Please see page 14, line 255-258.

*** Indeed, we agree with this comment that there should not be any difference. However, this analysis is more of a precautionary measure. Please see page 14, line 258-261.

*** We have now changed our sentences to better reflect our findings.

*** Thank you for this suggestion. We have not only added this, but also explained what is PA and NA difference. Please see page 17, line 298-303.

“We first computed the mean difference (After minus Before) for both positive ‘PA difference’ and negative affect ‘NA difference’. A repeated-measures 2 (Mood change: PA difference, NA difference) x 2 (Conditions: LS, HS) ANOVA was conducted to determine whether there is an interaction between mood change and condition. There was no interaction effect of mood change and condition, F (1, 117) = .38, p = .54, np2 = .003. There was a significant effect of Mood change, F (1, 117) = 13.01, p < .001, np2 = .10 (see Fig 2).”

*** Thank you. We have added more information about PA and NA difference score in the earlier results. Please see the above explanation.

*** Thank you. What we meant is actually Implications, rather than Future Studies. We have now reworded the sub-heading.

*** We have revised this section. Please refer to page 21, line 387-395.

“The ubiquitous nature of the smartphone in our lives also meant that our young graduates are constantly connected to their phones and very likely to be on SNS even at work. Our findings showed that the most frequently used feature was the SNS sites e.g. Instagram, Facebook, and Twitter. Being frequently on SNS sites may be a challenge in the workforce because these young adults need to maintain barriers between professional and social lives. Young adults claim that SNS can be productive at work (33), but many advise to avoid crossing boundaries between professional and social lives (34,35). Perhaps a more useful approach is to recognise a good balance when using SNS to meet both social and professional demands for the young workforce.”

*** We have reworded this section. Please see above.

*** We have revised it to “… integrate the phones into the classroom but will remain as a contentious issue between… “ See page 21, line 405.

*** Thank you for this comment. We reported observed power of .66 in our findings, and effect size of ᶇ2 = .44. Please see the added content below and on page 8, line 134-136.

“Using G*Power v. 3.1.9.2 , we need 76 participants with an effect size of d = .65, α = .05 and power (1-β) = .8 based on Thornton et al.’s study, and 128 participants based on numbers from Ward’s study. “

*** we thank the reviewer for this insightful comment. We certainly did not intend this question to be a manipulation check about their phones. We acknowledged that we omitted a fair amount of phone conscious thought in our earlier submission. We have since added more information about phone conscious thought in Abstract (page 2, line 6), Introduction (page 6, line 91-97), and Discussion (page 19, line 347-353).

*** we thank the reviewer for this comment. We realised that this is a major oversight on our part. The main reason for including affect measurement before and after was derived on the possibility that one’s mood may affect your cognitive function, and not simply due to phone presence. We have since made this clearer under Present Study (refer to page 7, line115-121 and in Results (refer to page 16-17, line 295-309).

*** we thank the reviewer for this comment. The phone conscious thought was asked at the end of the memory task. This was included in the Procedure section, page 12, line 243.

*** We thank the reviewer for this comment. One of the objectives in this study to examine the effect of a phone presence when participants are completing a simple learning and memory task. For this objective, we had two conditions; phone present (HS) and phone absent (LS). By having a third control with no instructions on what to do with the phone is addressing a different objective and that’s not part of our study objectives. We acknowledged that instruction on phone use may possibly be a confound and as such, we have addressed this limitation in our Discussion (see page 20, line 371-373).

*** We noted this. We have since revised this section. Please see page 16-17, line 295-309

*** We thank the reviewer for this comment. We have engaged a native English speaker to proofread our manuscript in accordance with academic writing practices.

*** we thank the reviewer for this comment. We have revised this sentence to better inform the reader on Ward et al.’s findings.

Submitted filename: Response to Reviewers.docx

Decision Letter 1

PONE-D-19-17118R1

Dear DR. Yong,

I am very sorry for the delay in getting a decision for you.  It was difficult to get reviewers for your paper.  However I now have the response of two reviewers.  The first reviewer thought that your changes made the paper much better.  However there is still a fundamental question about what your paper is addressing.  The second reviewer raised this point as well and made an excellent suggestion that you need to discuss what mechanisms may explain your results and describe how they might be investigated.  You also need to work more on the overall writing style and make sure that the grammar is correct.  If you feel that you can address these issues please submit a revised version of the paper.Please note Reviewer One's points about the role of emotion and discuss how you might investigate its role in a future design as well as why that would be important.  Address all comments raised by the reviewers in your revision or justify why you are not addressing them.

We would appreciate receiving your revised manuscript by April 30 2020. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: (No Response)

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: Partly

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

4. Have the authors made all data underlying the findings in their manuscript fully available?

5. Is the manuscript presented in an intelligible fashion and written in standard English?

6. Review Comments to the Author

Reviewer #1: Overall, I congratulate the authors on the revisions they've made already. The paper is much better for it.

However, I still have some concerns.

Most notably, as far as I can tell, the contribution this manuscript makes to the literature is in the inclusion of emotion. Indeed, the authors make this point quite clear in their "Present Study" section. Indeed, they state "But it is uncertain whether one's current positive or negative affective/mood states plays a bigger role on cognitive function..." but this doesn't seem to cohere very well with the rest of the paper. Barely anything is mentioned about emotion in the intro/lit. review (till the very end). In the analyses, the stats including emotion seem almost like an afterthought. Additionally, emotion is barely mentioned in the discussion. I realize that the authors found no statistical difference across groups, and therefore don't focus on them, but that raises another possible issue: interpreting a null result. If the primary motivation for this study was emotion, it seems to me that one would devise a different design whereby you also manipulate emotion and then examine the different conditions in terms of mobile phone salience.

Thus, at the heart of it, the present paper replicates prior research and then finds a null effect for their primary research question, making interpretations difficult.

For these reasons, I, unfortunately, am recommending rejection.

For the authors reference moving forward, the paper was still a bit hard to parse in places due to language issues throughout. I know the authors state that a native English speaker proofread it, but more diligent proofreading is needed in the future.

Reviewer #3: The experiment presented in this paper is aimed at primarily investigating whether the salience of a phone (high vs. low) impacts memory accuracy. It is a fairly straightforward experimental design and set of results. My main concern is that the paper lacks a clear mechanism to explain the results. Is the main result (i.e., HS leads to lower memory accuracy than LS) due to the fact that high salience participants are distracted during encoding? Is it due to retrieval deficits? Do they not consolidate the information properly? Is it evidence of a bandwidth effect, by which phone-related thought intrusion interferes with memory processes?

My sense is that not only that the experimental design did not attempt to answer the question mechanistically, but there is no attempt to theoretically scaffold the results in a potential mechanism. I would advise the authors to at least speculate as to what could explains the set of results they obtained and to hint at possible investigation of the mechanism involved.

7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

Reviewer #3: No

Author response to Decision Letter 1

26 Apr 2020

*** We thank you for this. We do agree that we have omitted a fairly huge amount on the affective state and have since revised the Introduction to explain the interactions between emotion and cognition. Please see Page 4-5, Line 60-73 in Introduction. We have also reorganised the Aim/Hypotheses section – please see Page 5-6, Line 75-92.

*** We do apologise for this and have since secured a second proof reader. We hope that the manuscript is far more legible now. We have also reorganised parts of the manuscript to make it more coherent.

*** Thank you for this feedback. We will first address the question on whether the memory recall accuracy was due to encoding, consolidation or retrieval failure. From previous studies, simple versions of a cognitive task was not an issue between low salience (LS) and high salience (HS) participants (1–3) for they had similar performance levels. This suggests it is unlikely that participants had problems at either encoding, consolidation or retrieval for the simple tasks.

However in our study, we used OS Span task which is considered a complex task compared to simple memory span (4). Although we did not include simple memory span as a contrast to OS Span, previous studies suggest that this is not necessary because of similar performance levels across conditions. One of our aims was to replicate a previous study in investigating whether the presence of a smartphone was sufficient to affect memory recall accuracy (5). We found that our participants had significant difference in memory recall accuracy between HS and LS conditions, p = .02. While our results concurred with previous study findings, we are unable to tease apart whether the presence of the smartphone had interfered with encoding, consolidation, or recall phase in our participants. However there is a possibility that the separation from their smartphones may have caused feelings of anxiety, and anxiety may interrupt memory consolidation as suggested by some (6,7). This is certainly something of consideration for future studies.

Second, to the bandwidth effect interfering memory processes, we suspect that this might be the case, rather than an issue of failure in a specific memory process. This is because participants with smartphones or texters could generally perform simple cognitive tasks as well as those without, and the presence of the smartphone next to the participant is responsible for the increase in cognitive load (1,3,5).

Other than the smartphone presence to increase cognitive load, we intended to manipulate participants’ affective state by prohibiting smartphone usage (HS) or taking it away (LS). Previous research has shown that experiencing positive affect (PA) or negative affect (NA) would influence cognitive performance (6–8). We predicted that the short-term separation from smartphone would evoke some anxiety, measured either having lower positive affect (PA) or higher negative affect (NA) post-test. We also predicted that separation from the phone is directly correlated to lower memory recall (LS condition) (part of hypothesis 2). An increase in NA or decrease in PA (as an indicator of separation anxiety to their smartphones) often have a negative effect on cognition (6,7). Further, one study shown an increased level of anxiety even in 10 minutes (9) and OS Span generally takes 20 minutes. Our results supported this hypothesis for LS participants who experienced a stronger negative affect had poorer memory recall accuracy (rs = -.394, p = .002, n = 58). This suggests that phone separation anxiety does increases cognitive load. We did not find any significant relationship between NA and memory recall accuracy for the HS participants and also for the PA difference in both groups (see Results, page 14-15, line 259-265).

We also examined another variable – phone conscious thought – described in past studies (3,5). Here, we found that phone conscious thought is negatively correlated to memory recall in both HS and LS conditions (see Results page 15, line 273), and uniquely contributed 19.9% in our regression model.

Taken together, the results showed that phone conscious thought is a significant contributor to the bandwidth effect interrupting their memory processes, and not the change in affective states as we had originally predicted. We do not think that participants’ memory failed at critical points e.g. encoding, retrieval, consolidation. Our participants memory processes are not likely to be impaired as they are neurotypical young adults, unlike well-documented cases in ageing or traumatic brain injury populations. In conclusion, the presence of the smartphone and frequent thoughts of their smartphone were contributors that interrupted their memory processes.

We do acknowledge several limitations in our study. First, we did not ask the phone conscious thought at specific time points in this study. Having done so might determine whether such thoughts hindered encoding, consolidating, or retrieval. Second, we did not include the simple version of this task as a comparison to rule out possible confounds within the sample. We did maintain similar external stimuli in their environment during testing, e.g. all participants were in one specific condition, lab temperature, lab noise, and thereby ruling out possible external factors that may have interfered with their memory processes. Third, the OS task itself. This task is complex and unfamiliar, thus may have caused some disadvantages to some. However, the advantage of this task being likely to be more unfamiliar – requiring more cognitive effort to learn and progress – demonstrates the limited cognitive capacity in our brain, and whether such limitation is easily affected by a smartphone presence.

1. Ito M, Kawahara J-I. Effect of the presence of a mobile phone during a spatial visual search. Jpn Psychol Res. 2017 Apr 1;59(2):188–98.

2. Bowman LL, Levine LE, Waite BM, Gendron M. Can students really multitask? An experimental study of instant messaging while reading. Comput Educ. 2010 May 1;54(4):927–31.

3. Thornton B, Faires A, Robbins M, Rollins E. The mere presence of a cell phone may be distracting: Implications for attention and task performance. Soc Psychol. 2014;45(6):479–88.

4. Francis G, Neath I, VanHorn D. CogLab On A CD, Version 2.0. Belmont, CA: Wadsworth; 2008.

5. Ward AF, Duke K, Gneezy A, Bos MW. Brain drain: The mere presence of one’s own smartphone reduces available cognitive capacity. J Assoc Consum Res. 2017 Apr 1;2(2):140–54.

6. Levine LJ, Lench HC, Karnaze MM, Carlson SJ. Bias in predicted and remembered emotion. Curr Opin Behav Sci. 2018 Feb 1;19:73–7.

7. Okon-Singer H. The role of attention bias to threat in anxiety: mechanisms, modulators and open questions. Curr Opin Behav Sci. 2018 Feb 1;19:26–30.

8. Gray JR. Integration of Emotion and Cognitive Control. Curr Dir Psychol Sci. 2004 Apr 1;13(2):46–8.

9. Cheever NA, Rosen LD, Carrier LM, Chavez A. Out of sight is not out of mind: The impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users. Comput Hum Behav. 2014 Aug 1;37:290–7.

Submitted filename: Response to Reviewers PONE-D-19-17118R1 Yong.docx

Decision Letter 2

25 Jun 2020

PONE-D-19-17118R2

Dear Dr. Yong,

I and another reviewer have carefully read your paper and the revisions that you made.  You have addressed many of the points but a few more changes need to be implemented before it can be accepted.  The main issues concern the introduction.  One phrase in Line 62 has no verb and therefore can not be a sentence.  You need to correct the grammatical structure.  Secondly it would be good if the introductory sentence to the paragraph beginning on line 61 indicated the relationship between cognition and affect is important for understanding the impact of mobile phone use on memory.  As it is written the paragraph does link well to the previous paragraphs.  Further line 82 makes reference to smart phone addiction very briefly but the discussion focuses a great deal on smart phone addiction.  You need to define smart phone addiction  and indicate why it is important to examine this construct in your study. Further you need to define the subscales in of the SAS in the methods and also justify under phone consious thought why you are including this question.

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Reviewer #3: All comments have been addressed

Reviewer #3: The authors addressed my comments satisfactorily, so I recommend acceptance for the manuscript. I doubt, though, that anxiety plays a huge role in these dynamics, given that it is hard to imagine that one would be able to create the levels of anxiety necessary for the disruption of cognitive function by simply temporarily removing their phones.

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Author response to Decision Letter 2

14 Jul 2020

1. The main issues concern the introduction. One phrase in Line 62 has no verb and therefore can not be a sentence. You need to correct the grammatical structure.

*** We thank the editor for pointing this out. We have since removed the sentence.

2. Secondly it would be good if the introductory sentence to the paragraph beginning on line 61 indicated the relationship between cognition and affect is important for understanding the impact of mobile phone use on memory. As it is written the paragraph does link well to the previous paragraphs.

*** We thank the editor for this comment. We have revised the paragraph, please see Line 60-61, page 4.

“Further, we need to consider the relationship between cognition and emotion to understand how frequent mobile phone use affects memory e.g. memory consolidation. Some empirical findings … “

3. Further line 82 makes reference to smart phone addiction very briefly but the discussion focuses a great deal on smart phone addiction. You need to define smart phone addiction and indicate why it is important to examine this construct in your study.

*** We thank the reviewer for omission on our part. Please find the newly added sentences below on Line 83-88, page 5-6.

“One in every four young adults is reported to have problematic smartphone use and this is accompanied by poor mental health e.g. higher anxiety, stress, depression (Sohn et al., 2019). One report showed that young adults reached for their phones 86 times in a day on average compared to 47 times in other age groups (Deloitte Development LLC, 2017). Young adults also reported that they “definitely” or “probably” used their phone too much, suggesting that they recognised their problematic smartphone use. “

4. Further you need to define the subscales in of the SAS in the methods and also justify under phone consious thought why you are including this question.

*** We thank the reviewer for this comment. Please see the inclusion for SAS subscales on Line 159-166, page 9.

“SAS contained six sub-factors; daily-life disturbance that measures the extent to which mobile phone use impairs one’s activities during everyday tasks (5 statements), positive anticipation to describe the excitement of using phone and de-stressing with the use of mobile phone (8 statements), withdrawal refers to the feeling of anxiety when separated from one’s mobile phone (6 statements), cyberspace-oriented relationship refers to one’s opinion on online friendship (7 statements), overuse measures the excessive use of mobile phone to the extent that they have become inseparable from their device (4 statements), and tolerance points to the cognitive effort to control the usage of one’s smartphone (3 statements).”

We have also added the justification to the phone conscious thought. Please see this inclusion on Line 173-175, page 9.

“The aim of this question was two-fold; first was to capture endogenous interruption experienced by the separation, and second to complement the smartphone addiction to reflect current immediate experience.”

5. How frequent do we reach for our phones?

*** Surprisingly, high, but unsurprisingly higher for young adults. Deloitte 2017 survey reported that the average American reaches for their phones 47 times while young adults (aged between 18 to 24) reach for 86 times. The same survey also reported that 89% looked at their phone within an hour of waking up and that 81% also looked at their phone within an hour before going to bed. Further, 90% of young adults reported using their phones in their daily activities ranging from shopping, leisure time, talking to friends, crossing the road, and this trend has been consistent for the past three years. The young adults also reported that they “definitely” or “probably” use their phone too much, suggesting some form of recognising their addiction (Deloitte Development LLC, 2017). Another poll reported that one in every 10 Americans check their phones every four minutes, and that most people struggle to go beyond 10 minutes without checking their phone (SWNS, 2017).

We have added couple of sentences to further highlight on mobile phone addiction under Research Aim (see Line 85-88, page 5-6).

Submitted filename: Response to Reviewers PONE-D-19-17118R2 Yong.docx

Decision Letter 3

PONE-D-19-17118R3

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The Harvard Gazette

Do phones belong in school, not-so-innocent bystanders, national & world affairs.

iStock by Getty Images

Bans may help protect classroom focus, but districts need to stay mindful of students’ sense of connection, experts say

By Anna Lamb Harvard Staff Writer

Date March 13, 2023 November 8, 2023

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Students around the world are being separated from their phones.

In 2020, the National Center for Education Statistics reported that 77 percent of U.S. schools had moved to prohibit cellphones for nonacademic purposes. In September 2018, French lawmakers outlawed cellphone use for schoolchildren under the age of 15. In China, phones were banned country-wide for schoolchildren last year.

Supporters of these initiatives have cited links between smartphone use and bullying and social isolation and the need to keep students focused on schoolwork.

But some Harvard experts say instructors and administrators should consider learning how to teach with tech instead of against it, in part because so many students are still coping with academic and social disruptions caused by the pandemic. At home, many young people were free to choose how and when to use their phones during learning hours. Now, they face a school environment seeking to take away their main source of connection.

“Returning back to in-person, I think it was hard to break the habit,” said Victor Pereira, a lecturer on education and co-chair of the Teaching and Teaching Leadership Program at the Graduate School of Education.

Through their students, he and others with experience both in the classroom and in clinical settings have seen interactions with technology blossom into important social connections that defy a one-size-fits-all mindset. “Schools have been coming back, trying to figure out, how do we readjust our expectations?” Pereira added.

It’s a hard question, especially in the face of research suggesting that the mere presence of a smartphone can undercut learning .

Michael Rich , an associate professor of pediatrics at Harvard Medical School and an associate professor of social and behavioral sciences at the Harvard T.H. Chan School of Public Health, says that phones and school don’t mix: Students can’t meaningfully absorb information while also texting, scrolling, or watching YouTube videos.

“The human brain is incapable of thinking more than one thing at a time,” he said. “And so what we think of as multitasking is actually rapid-switch-tasking. And the problem with that is that switch-tasking may cover a lot of ground in terms of different subjects, but it doesn’t go deeply into any of them.”

Pereira’s approach is to step back — and to ask whether a student who can’t resist the phone is a signal that the teacher needs to work harder on making a connection. “Two things I try to share with my new teachers are, one, why is that student on the phone? What’s triggering getting on your cell phone versus jumping into our class discussion, or whatever it may be? And then that leads to the second part, which is essentially classroom management.

“Design better learning activities, design learning activities where you consider how all of your students might want to engage and what their interests are,” he said. He added that allowing phones to be accessible can enrich lessons and provide opportunities to use technology for school-related purposes.

Mesfin Awoke Bekalu, a research scientist in the Lee Kum Sheung Center for Health and Happiness at the Chan School, argues that more flexible classroom policies can create opportunities for teaching tech-literacy and self-regulation.

“There is a huge, growing body of literature showing that social media platforms are particularly helpful for people who need resources or who need support of some kind, beyond their proximate environment,” he said. A study he co-authored by Rachel McCloud and Vish Viswanath for the Lee Kum Sheung Center for Health and Happiness shows that this is especially true for marginalized groups such as students of color and LGBTQ students. But the findings do not support a free-rein policy, Bekalu stressed.

In the end, Rich, who noted the particular challenges faced by his patients with attention-deficit disorders and other neurological conditions, favors a classroom-by-classroom strategy. “It can be managed in a very local way,” he said, adding: “It’s important for parents, teachers, and the kids to remember what they are doing at any point in time and focus on that. It’s really only in mono-tasking that we do very well at things.”

Géraldine Schwarz (center) spoke at Harvard Law School with Abadir Ibrahim (left), associate director of the Human Rights Program, and Cass Sunstein, Robert Walmsley University Professor.

Kris Snibbe/Harvard Staff Photographer

Weighing the Costs and Benefits of Cellphones in Schools

• Posted August 10, 2022
• By Emily Boudreau
• K-12 System Leadership
• Teachers and Teaching
• Technology and Media

Typically, the discussion around cellphones in school — whether they are learning tools or distractions — has revolved around their impact on measures of academic success like test scores or grades. But in his research, Ed School alum Dylan Lukes looks at other outcomes policymakers should be considering.

“I’m hoping to move beyond thinking about test scores and consider the potential importance of other outcomes like discipline and school culture which may factor into student wellbeing,” says Lukes, Ph.D.’22.

As schools are gearing up for the fall, with some considering new and amended policies on the use of cellphones in class, Luke gets into his findings — including how the New York City Department of Education’s (NYCDOE) recently reversed cellphone ban impacted student suspensions and school culture — and gives his thoughts on what schools and districts should be considering when creating policies around technology moving forward. 

Why are cellphones in schools such a contested topic among educators, parents, and students? The motivation for many of these policies comes from a desire to limit distractions. If you think about it, from a school’s perspective, if a cellphone ban can improve student learning, that’s a great low-cost intervention with a favorable benefit-cost ratio. However, from a parent’s perspective, the calculus is a bit different, and the cost of not being able to get a hold of their kid(s) may outweigh any potential benefit accrued from the ban. 

How have cellphone policies evolved over the years? Over the past several decades, many large urban school districts have intermittently experimented with cellphone bans. However, most cellphone bans have been repealed due to their unpopularity with parents and students and concerns over equity [ as low income students often have mobile-only access to the internet ]. In March 2015, the NYCDOE lifted their longstanding districtwide cellphone ban and provided schools with significant discretion in designing and implementing school-level policies governing student cellphone use — and that shift is what I explore in my research.

Most research around cellphone use in schools looks at the impact on test scores, reaction time, and the ability to focus. You look instead at two areas: discipline and a sense of safety.  The existing studies provide evidence that allowing phones in the classroom negatively impacts test scores and long-term learning retention. There are some correlational studies that suggest negative relationships between off-task device use and student achievement. Further, in psychology, research on multitasking generally finds negative effects on learning and task completion and, more generally, research has shown that cellphones distract and negatively impact reaction times, performance, enjoyment of focal tasks, and cognitive capacity.

In my research, my thinking was that as schools consider removal of bans or enforcement, they should also consider often overlooked dimensions of school culture that could play a role in educational productivity and student wellbeing. That is not to say academic achievement is not important — it is — but there are other potentially important inputs that contribute to educational productivity such as school discipline and culture.  

Why? From a disciplinary standpoint, if the school has a cellphone ban and there are students breaking that cellphone ban, it’s possible that over time — and I’ve seen this from survey responses from NYCDOE school principals and parent coordinators — at some point there can be some punitive measures if you’re caught breaking that ban. That’s one of the reasons I explore the impact on discipline and suspension — you could be using a cellphone which, yes, could be distracting, but even more negatively, have the student removed from school. That kind of impact on learning could be a net-negative, even when you consider that against the positive effects a cellphone ban may have on a student’s learning and their peers’ learning. 

I also think it’s important to look at other factors we don’t typically think about, like school culture, that might also have a big impact on learning. 

And what did you find? So just as a disclaimer, there might be policies I can’t control for that impact these outcomes. For example, in 2014, there was a new chancellor [in New York] who made changes to the discipline code. With that caveat, I do find that the ban removal positively impacted school discipline but had negative impacts on student perception of school culture across the dimensions of respect, student behavior, and school safety. It also had negative impacts on teacher perception of school safety. My findings suggest an improvement in educational productivity due to the NYCDOE’s ban removal. But there’s a tradeoff — a cost to school culture. 

What do you mean by safety? When it comes to emergencies, students likely feel safer having access to a phone. But the day-in and day-out component of school safety is how students use phones within school. This might include things like bullying, harassment, videotaping, and posting to social media. Those are reasons why having phones within schools could potentially be accelerators of negative student behavior. These safety measures which look at how safe students feel in classrooms, hallways, locker rooms, cafeterias, show a pretty negative jump after the ban has been lifted, which suggest to me that having a phone is at least interrupting a student’s ability to safely navigate those spaces.

So what should policymakers think about moving forward? This is just the tip of the iceberg. It would be interesting to look at how cellphones further contribute to school culture using more robust measures across time. And to be clear, I don’t think there’s anything inherently bad about cellphones but I do think it’s key to engage in a discussion around the tradeoffs of having them in schools and classrooms. There might be some interesting ways to balance the tradeoffs of their distractions and their benefits — something like having magnetized pouches and allowing students to take out cellphones under special circumstances (e.g., class activity, lunch). Some schools are already experimenting with these alternatives and there are some prime opportunities in this space to evaluate impacts of these polices on educational outcomes, including school discipline and school culture. 

The latest research, perspectives, and highlights from the Harvard Graduate School of Education

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Student Opinion

Should Schools Ban Cellphones?

Rules restricting when students can use phones are on the rise. Do they work? Are they fair?

By Jeremy Engle

Nearly one in four countries has laws or policies banning or restricting student cellphone use in schools .

Proponents say the smartphone crackdowns reduce classroom distractions by preventing students from scrolling through social media and sending bullying text messages.

Critics believe the bans could limit students’ opportunities to develop personal responsibility and warn that enforcing restrictions could increase harsh disciplinary measures like school suspensions.

What do you think?

How would you and your peers react to a cellphone ban in your school? Could a no-phone rule work? Would it be fair? Would you welcome or oppose it? Or, perhaps, does your school already have a ban?

In “ This Florida School District Banned Cellphones. Here’s What Happened ,” Natasha Singer writes that in the wake of Orlando’s new policy, which bars students from using cellphones during the entire school day, student engagement increased, but so did the hunt for contraband phones:

One afternoon last month, hundreds of students at Timber Creek High School in Orlando poured into the campus’s sprawling central courtyard to hang out and eat lunch. For members of an extremely online generation, their activities were decidedly analog. Dozens sat in small groups, animatedly talking with one another. Others played pickleball on makeshift lunchtime courts. There was not a cellphone in sight — and that was no accident. In May, Florida passed a law requiring public school districts to impose rules barring student cellphone use during class time. This fall, Orange County Public Schools — which includes Timber Creek High — went even further, barring students from using cellphones during the entire school day. In interviews, a dozen Orange County parents and students all said they supported the no-phone rules during class. But they objected to their district’s stricter, daylong ban. Parents said their children should be able to contact them directly during free periods, while students described the all-day ban as unfair and infantilizing. “They expect us to take responsibility for our own choices, ” said Sophia Ferrara, a 12th grader at Timber Creek who needs to use mobile devices during free periods to take online college classes. “But then they are taking away the ability for us to make a choice and to learn responsibility.” Like many exasperated parents, public schools across the United States are adopting increasingly drastic measures to try to pry young people away from their cellphones. Tougher constraints are needed, lawmakers and district leaders argue, because rampant social media use during school is threatening students’ education, well-being and physical safety.

Ms. Singer discusses some of the benefits of a phone-free environment:

In September, on the first day the ban took effect, Timber Creek administrators confiscated more than 100 phones from students, Mr. Wasko said. After that, the confiscations quickly dropped. Phone-related school incidents, like bullying, have also decreased, he said. The ban has made the atmosphere at Timber Creek both more pastoral and more carceral. Mr. Wasko said students now make eye contact and respond when he greets them. Teachers said students seemed more engaged in class. “Oh, I love it,” said Nikita McCaskill, a government teacher at Timber Creek. “Students are more talkative and more collaborative.” Some students said the ban had made interacting with their classmates more authentic. “Now people can’t really be like: ‘Oh, look at me on Instagram. This is who I am,’” said Peyton Stanley, a 12th grader at Timber Creek. “It has helped people be who they are — instead of who they are online — in school.”

The article also addresses some of the downsides of cellphone restrictions:

Other students said school seemed more prisonlike. To call their parents, they noted, students must now go to the front office and ask permission to use the phone. Surveillance has also intensified. To enforce the ban, Lyle Lake, a Timber Creek security officer, now patrols lunch period on a golf cart, nabbing students violating the ban and driving them to the front office, where they must place their phones in a locked cabinet for the rest of the school day. “I usually end up with a cart full of students,” Mr. Lake said as he sat behind the wheel of a black Yamaha golf cart during lunch period, “because I pick up more on the way to the office.” Mr. Lake said he also monitored school security camera feeds for students using cellphones in hallways and other spaces. Students who are caught may be taken out of class. Repeat violators can be suspended. Whether the potential benefits of banning cellphones outweigh the costs of curbing students’ limited freedom is not yet known. What is clear is that such bans are upending the academic and social norms of a generation reared on cellphones.

Students, read the entire article and then tell us:

Should schools ban cellphones? Would you welcome a ban in your school? Why or why not? Did anything in the article change your thoughts on the growing trend?

How widespread is cellphone use at your school? How much do you use your phone during the school day? Do you think that phones interfere with your, or your peers’, academic learning, quality of social interactions and overall engagement in school?

Nikita McCaskill, a government teacher at Timber Creek High School who loves the new policy, stated, “Students are more talkative and more collaborative.” Which of the benefits of a cellphone ban discussed in the article do you find most appealing?

Many students, however, said the new rules made school more prisonlike. Others argued that the ban was infantilizing. Sophia Ferrara, a 12th grader at Timber Creek, noted: “They expect us to take responsibility for our own choices. But then they are taking away the ability for us to make a choice and to learn responsibility.” Which downsides described in the article concern you most?

What rules, if any, does your school have about cellphone use? How are they enforced? Do you think they are effective? What changes would you recommend to the current policy?

What, if anything, do you think is missing from this conversation? What do you think teachers, educators and parents may not understand about cellphones, especially how young people use them?

Students 13 and older in the United States and Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public and may appear in print.

Find more Student Opinion questions here. Teachers, check out this guide to learn how you can incorporate these prompts into your classroom.

Jeremy Engle joined The Learning Network as a staff editor in 2018 after spending more than 20 years as a classroom humanities and documentary-making teacher, professional developer and curriculum designer working with students and teachers across the country. More about Jeremy Engle

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Open Access

Peer-reviewed

Research Article

Mobile phones: The effect of its presence on learning and memory

Roles Conceptualization, Data curation, Investigation, Writing – original draft

Affiliation Department of Psychology, Sunway University, Selangor, Malaysia

Roles Formal analysis, Investigation, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Clarissa Theodora Tanil, 
• Min Hooi Yong

• Published: August 13, 2020
• https://doi.org/10.1371/journal.pone.0219233
• See the preprint
• Peer Review
• Reader Comments

Our aim was to examine the effect of a smartphone’s presence on learning and memory among undergraduates. A total of 119 undergraduates completed a memory task and the Smartphone Addiction Scale (SAS). As predicted, those without smartphones had higher recall accuracy compared to those with smartphones. Results showed a significant negative relationship between phone conscious thought, “how often did you think about your phone”, and memory recall but not for SAS and memory recall. Phone conscious thought significantly predicted memory accuracy. We found that the presence of a smartphone and high phone conscious thought affects one’s memory learning and recall, indicating the negative effect of a smartphone proximity to our learning and memory.

Citation: Tanil CT, Yong MH (2020) Mobile phones: The effect of its presence on learning and memory. PLoS ONE 15(8): e0219233. https://doi.org/10.1371/journal.pone.0219233

Editor: Barbara Dritschel, University of St Andrews, UNITED KINGDOM

Received: June 17, 2019; Accepted: July 30, 2020; Published: August 13, 2020

Copyright: © 2020 Tanil, Yong. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: MHY received funding from Sunway University (GRTIN-RRO-104-2020 and INT-RRO-2018-49).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Smartphones are a popular communication form worldwide in this century and likely to remain as such, especially among adolescents [ 1 ]. The phone has evolved from basic communicative functions–calls only–to being a computer-replacement device, used for web browsing, games, instant communication on social media platforms, and work-related productivity tools, e.g. word processing. Smartphones undoubtedly keep us connected; however, many individuals are now obsessed with them [ 2 , 3 ]. This obsession can lead to detrimental cognitive functions and mood/affective states, but these effects are still highly debated among researchers.

Altmann, Trafton, and Hambrick suggested that as little as a 3-second distraction (e.g. reaching for a cell phone) is adequate to disrupt attention while performing a cognitive task [ 4 ]. This distraction is disadvantageous to subsequent cognitive tasks, creating more errors as the distraction period increases, and this is particularly evident in classroom settings. While teachers and parents are for [ 5 ] or against cell phones in classrooms [ 6 ], empirical evidence showed that students who used their phones in class took fewer notes [ 7 ] and had poorer overall academic performance, compared to those who did not [ 8 , 9 ]. Students often multitask in classrooms and even more so with smartphones in hand. One study showed no significant difference in in-class test scores, regardless of whether they were using instant messaging [ 10 ]. However, texters took a significantly longer time to complete the in-class test, suggesting that texters required more cognitive effort in memory recall [ 10 ]. Other researchers have posited that simply the presence of a cell phone may have detrimental effects on learning and memory as well. Research has shown that a mobile phone left next to the participant while completing a task, is a powerful distractor even when not in use [ 11 , 12 ]. Their findings showed that mobile phone participants could perform similarly to control groups on simple versions of specific tasks (e.g. visual spatial search, digit cancellation), but performed much poorer in the demanding versions. In another study, researchers controlled for the location of the smartphone by taking the smartphones away from participants (low salience, LS), left the smartphone next to them (high salience/HS), or kept the smartphones in bags or pockets (control) [ 13 ]. Results showed that participants in LS condition performed significantly better compared to HS, while no difference was established between control and HS conditions. Taken together, these findings confirmed that the smartphone is a distractor even when not in use. Further, smartphone presence also increases cognitive load, because greater cognitive effort is required to inhibit distractions.

Reliance on smartphones has been linked to a form of psychological dependency, and this reliance has detrimental effect on our affective ‘mood’ states. For example, feelings of anxiety when one is separated from their smartphones can interfere with the ability to attend to information. Cheever et al. observed that heavy and moderate mobile phone users reported increased anxiety when their mobile phone was taken away as early as 10 minutes into the experiment [ 14 ]. They noted that high mobile phone usage was associated with higher risk of experiencing ‘nomophobia’ (no mobile phone phobia), a form of anxiety characterized by constantly thinking about one’s own mobile phones and the desire to stay in contact with the device [ 15 ]. Other studies reported similar separation-anxiety and other unpleasant thoughts in participants when their smartphones were taken away [ 16 ] or the usage was prohibited [ 17 , 18 ]. Participants also reported having frequent thoughts about their smartphones, despite their device being out of sight briefly (kept in bags or pockets), to the point of disrupting their task performance [ 13 ]. Taken together, these findings suggest that strong attachment towards a smartphone has immediate and lasting negative effects on mood and appears to induce anxiety.

Further, we need to consider the relationship between cognition and emotion to understand how frequent mobile phone use affects memory e.g. memory consolidation. Some empirical findings have shown that anxious individuals have attentional biases toward threats and that these biases affect memory consolidation [ 19 , 20 ]. Further, emotion-cognition interaction affects efficiency of specific cognitive functions, and that one’s affective state may enhance or hinder these functions rapidly, flexibly, and reversibly [ 21 ]. Studies have shown that positive affect improves visuospatial attention [ 22 ], sustained attention [ 23 ], and working memory [ 24 ]. The researchers attributed positive affect in participants’ improved controlled cognitive processing and less inhibitory control. On the other hand, participants’ negative affect had fewer spatial working memory errors [ 23 ] and higher cognitive failures [ 25 ]. Yet, in all of these studies–the direction of modulation, intensity, valence of experiencing a specific affective state ranged widely and primarily driven by external stimuli (i.e. participants affective states were induced from watching videos), which may not have the same motivational effect generated internally.

Present study

Prior studies have demonstrated the detrimental effects of one’s smartphone on cognitive function (e.g. working memory [ 13 ], visual spatial search [ 12 ], attention [ 11 ]), and decreased cognitive ability with increasing attachment to one’s phone [ 14 , 16 , 26 ]. Further, past studies have demonstrated the effect of affective state on cognitive performance [ 19 , 20 , 22 – 25 , 27 ]. To our knowledge, no study has investigated the effect of positive or negative affective states resulting from smartphone separation on memory recall accuracy. One study showed that participants reporting an increased level of anxiety as early as 10 minutes [ 14 ]. We also do not know the extent of smartphone addiction and phone conscious thought effects on memory recall accuracy. One in every four young adults is reported to have problematic smartphone use and this is accompanied by poor mental health e.g. higher anxiety, stress, depression [ 28 ]. One report showed that young adults reached for their phones 86 times in a day on average compared to 47 times in other age groups [ 29 ]. Young adults also reported that they “definitely” or “probably” used their phone too much, suggesting that they recognised their problematic smartphone use.

We had two main aims in this study. First, we replicated [ 13 ] to determine whether ‘phone absent’ (LS) participants had higher memory accuracy compared to the ‘phone present’ (HS). Second, we predicted that participants with higher smartphone addiction scores (SAS) and higher phone conscious thought were more likely to have lower memory accuracy. With regards to separation from their smartphone, we hypothesised that LS participants will experience an increase of negative affect or a decrease in positive affect and that this will affect memory recall negatively. We will also examine whether these predictor variables–smartphone addiction, phone conscious thought and affect differences—predict memory accuracy.

Materials and methods

Participants.

A total of 119 undergraduate students (61 females, M age = 20.67 years, SD age = 2.44) were recruited from a private university in an Asian capital city. To qualify for this study, the participant must own a smartphone and does not have any visual or auditory deficiencies. Using G*Power v. 3.1.9.2 [ 30 ], we require at least 76 participants with an effect size of d = .65, α = .05 and power of (1-β) = .8 based on Thornton et al.’s [ 11 ] study, or 128 participants from Ward’s study [ 13 ].

Out of 119 participants, 43.7% reported using their smartphone mostly for social networking, followed by communication (31.1%) and entertainment (17.6%) (see Table 1 for full details on smartphone usage). Participants reported an average smartphone use of 8.16 hours in a day ( SD = 4.05). There was no significant difference between daily smartphone use for participants in the high salience (HS) and low salience groups (LS), t (117) = 1.42, p = .16, Cohen’s d = .26. Female participants spent more time using their smartphones over a 24-hour period ( M = 9.02, SD = 4.10) compared to males, ( M = 7.26, SD = 3.82), t (117) = 2.42, p = .02, Cohen’s d = .44.

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https://doi.org/10.1371/journal.pone.0219233.t001

Ethical approval and informed consent

The study was conducted in accordance with the protocol approved by the Department of Psychology Research Ethics Committee at Sunway University (approval code: 20171090). All participants provided written consent before commencing the study and were not compensated for their participation in the study.

Study design

Our experimental study was a mixed design, with smartphone presence (present vs absent) as a between-subjects factor, and memory task as a within-subjects factor. Participants who had their smartphone out of sight formed the ‘Absent’ or low-phone salience (LS) condition, and the other group had their smartphone placed next to them throughout the study, ‘Present’ or high-phone salience (HS) condition. The dependent variable was recall accuracy from the memory test.

Working memory span test.

A computerized memory span task ‘Operation Span (OS)’ retrieved from software Wadsworth CogLab 2.0 was used to assess working memory [ 31 ]. A working memory span test was chosen as a measure to test participants’ memory ability for two reasons. First, participants were required to learn and memorize three types of stimuli thus making this task complex. Second, the duration of task completion took approximately 20 minutes. This was advantageous because we wanted to increase separation-anxiety [ 16 ] as well as having the most pronounced effect on learning and memory without the presence of their smartphone [ 9 ].

The test comprised of three stimulus types, namely words (long words such as computer, refrigerator and short words like pen, cup), letters (similar sound E, P, B, and non-similar sound D, H, L) and digits (1 to 9). The test began by showing a sequence of items on the left side of the screen, with each item presented for one second. After that, participants were required to recall the stimulus from a 9-button box located on the right side of the screen. In order to respond correctly, participants were required to click on the buttons for the items in the corresponding order they were presented. A correct response increases the length of stimulus presented by one item (for each stimulus category), while an incorrect response decreases the length of the stimulus by one item. Each trial began with five stimuli and increased or decreased depending on the participants’ performance. The minimum length possible was one while the maximum was ten. Each test comprised of 25 trials with no time limit and without breaks between trials. Working memory ability was measured through the number of correct responses over total trials: scores ranged from 0 to 25, with the highest score representing superior working memory.

Positive and Negative Affect Scale (PANAS).

We used PANAS to assess the current mood/affective state of the participants with state/feeling-descriptive statements [ 32 ]. PANAS has ten PA statements e.g. interested, enthusiastic, proud, and ten NA statements e.g. guilty, nervous, hostile. Each statement was measured using a five-point Likert scale ranging from very slightly or not at all to extremely, and then totalled to form overall PA or NA score with higher scores representing higher levels of PA or NA. In the current study, the internal reliability of PANAS was good with a Cronbach’s alpha coefficient of .819, and .874 for PA and NA respectively.

Smartphone Addiction Scale (SAS)

SAS is a 33-item self-report scale used to examine participants’ smartphone addiction [ 33 ]. SAS contained six sub-factors; daily-life disturbance that measures the extent to which mobile phone use impairs one’s activities during everyday tasks (5 statements), positive anticipation to describe the excitement of using phone and de-stressing with the use of mobile phone (8 statements), withdrawal refers to the feeling of anxiety when separated from one’s mobile phone (6 statements), cyberspace-oriented relationship refers to one’s opinion on online friendship (7 statements), overuse measures the excessive use of mobile phone to the extent that they have become inseparable from their device (4 statements), and tolerance points to the cognitive effort to control the usage of one’s smartphone (3 statements). Each statement was measured using a six-point Likert scale from strongly disagree to strongly agree, and total SAS was identified by totalling all 33 statements. Higher SAS scores represented higher degrees of compulsive smartphone use. In the present study, the internal reliability of SAS was identified with Cronbach's alpha correlation coefficient of .918.

Phone conscious thought and perceived effect on learning

We included a one-item question for phone conscious thought: “During the memory test how often do you think of your smartphone?”. The aim of this question was two-fold; first was to capture endogenous interruption experienced by the separation, and second to complement the smartphone addiction to reflect current immediate experience. Participants rated this item on a scale of one (none to hardly) to seven (all the time). We also included a one-item question on how much they perceived their smartphone use has affected their learning and attention: “In general, how much do you think your smartphone affects your learning performance and attention span?”. This item was similarly rated on a scale of one (not at all) to seven (very much).

We randomly assigned participants to one of two conditions: low-phone salience (LS) and high-phone salience (HS). Participants were tested in groups of three to six people in a university computer laboratory and seated two seats apart from each other to prevent communication. Each group was assigned to the same experimental condition to ensure similar environmental conditions. Participants in the HS condition were asked to place their smartphone on the left side of the table with the screen facing down. LS participants were asked to hand their smartphone to the researcher at the start of the study and the smartphones were kept on the researcher’s table throughout the task at a distance between 50cm to 300cm from the participants depending on their seat location, and located out of sight behind a small panel on the table.

At the start of the experiment, participants were briefed on the rules in the experimental lab, such as no talking and no smartphone use (for HS only). Participants were also instructed to silence their smartphones. They filled in the consent form and demographic form before completing the PANAS questionnaire. They were then directed to CogLab software and began the working memory test. Upon completion, participants were asked to complete the PANAS again followed by the SAS, phone conscious thought, and their perception of their phone use on their learning performance and attention span. The researcher thanked the participants and returned the smartphones (LS condition only) at the end of the task.

Statistical analysis

We examined for normality in our data using the Shapiro-Wilk results and visual inspection of the histogram. For the normally distributed data, we analysed our data using independent-sample t -test for comparison between groups (HS or LS), paired-sample t test for within groups (e.g. before and after phone separation), and Pearson r for correlation. Non-normally distributed or ranked data were analysed using Spearman rho for correlation.

Preliminary analyses

Our female participants reported using their smartphone significantly longer than males, and so we examined the effects of gender on memory recall accuracy. We found no significant difference between males and females on memory recall accuracy, t (117) = .18, p = .86, Cohen’s d = .03. Subsequently, data were collapsed, analysed and reported on in the aggregate.

Smartphone presence and memory recall accuracy

An independent-sample t- test was used to examine whether participants’ performance on a working memory task was influenced by the presence (HS) or absence (LS) of their smartphone. Results showed that participants in the LS condition had higher accuracy ( M = 14.21, SD = 2.61) compared to HS ( M = 13.08, SD = 2.53), t (117) = 2.38, p = .02, Cohen’s d = .44 (see Fig 1 ). The effect size ᶇ 2 = .44 indicates that smartphone presence/salience has a moderate effect on participant working memory ability and a sensitivity power of .66.

https://doi.org/10.1371/journal.pone.0219233.g001

Relationship between Smartphone Addiction Score (SAS), higher phone conscious thought and memory recall accuracy

Sas and memory recal..

We first examined participants’ SAS scores between the two conditions. Results showed no significant difference between the LS (M = 104.64, SD = 24.86) and HS (M = 102.70, SD = 20.45) SAS scores, t (117) = .46, p = .64, Cohen’s d = .09. We predicted that those with higher SAS scores will have lower memory accuracy, and thus we examined the relationship between SAS and memory recall accuracy using Pearson correlation coefficient. Results showed that there was no significant relationship between SAS and memory recall accuracy, r = -.03, n = 119, p = .76. We also examined the SAS scores between the LS and HS groups on memory recall accuracy scores. In the LS group, no significant relationship was established between SAS score and memory accuracy, r = -.04, n = 58, p = .74. Similarly, there was no significant relationship between SAS score and memory accuracy in the HS group, r = .10, n = 61, p = .47. In the event that one SAS subscale may have a larger impact, we examined the relationship between each subscale and memory recall accuracy. Results showed no significant relationship between each sub-factor of SAS scores and memory accuracy, all p s > .12 (see Table 2 ).

https://doi.org/10.1371/journal.pone.0219233.t002

Phone conscious thought and memory accuracy.

We found a significant negative relationship between phone conscious thought and memory recall accuracy, r S = -.25, n = 119, p = .01. We anticipated a higher phone conscious thought for the LS group since their phone was kept away from them during the task and examined the relationship for each condition. Results showed a significant negative relationship between phone conscious thought and memory accuracy in the HS condition, r S = -.49, n = 61, p = < .001, as well as the LS condition, r S = -.27, n = 58, p = .04.

Affect/mood changes after being separated from their phone

We anticipated that our participants may have experienced either an increase in negative affect (NA) or a decrease in positive affect (PA) after being separated from their phone (LS condition).

We first computed the mean difference (After minus Before) for both positive ‘PA difference’ and negative affect ‘NA difference’. A repeated-measures 2 (Mood change: PA difference, NA difference) x 2 (Conditions: LS, HS) ANOVA was conducted to determine whether there is an interaction between mood change and condition. There was no interaction effect of mood change and condition, F (1, 117) = .38, p = .54, n p 2 = .003. There was a significant effect of Mood change, F (1, 117) = 13.01, p < .001, n p 2 = .10 (see Fig 2 ).

https://doi.org/10.1371/journal.pone.0219233.g002

Subsequent post-hoc analyses showed a significant decrease in participants’ positive affect before ( M = 31.12, SD = 5.79) and after ( M = 29.36, SD = 6.58) completing the memory task in the LS participants, t (57) = 2.48, p = .02, Cohen’s d = .28 but not for the negative affect, Cohen’s d = .07. A similar outcome was also shown in the HS condition, in which there was a significant decrease in positive affect only, t (60) = 3.45, p = .001, Cohen’s d = .37 (see Fig 2 ).

PA/NA difference on memory accuracy.

We predicted that LS participants will experience either an increase in NA and/or a decrease in PA since their smartphones were taken away and that this will affect memory recall negatively. Results showed that LS participants who experienced a higher NA difference had poorer memory recall accuracy ( r s = -.394, p = .002). We found no significant relationship between NA difference and memory recall accuracy for HS participants ( r s = -.057, p = .663, n = 61) and no significant relationship for PA difference in both HS ( r s = .217, p = .093) and LS conditions ( r s = .063, p = .638).

Relationship between phone conscious thought, smartphone addiction scale and mood changes to memory recall accuracy

Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, multicollinearity and homoscedasticity. There was a significant positive relationship between SAS scores and phone conscious thought, r S = .25, n = 119, p = .007. Using the enter method, we found that phone conscious thought explained by the model as a whole was 19.9%, R 2 = .20, R 2 Adjusted = .17, F (4, 114) = 7.10, p < .001. Phone conscious thought significantly predicted memory recall accuracy, b = -.63, t (114) = 4.76, p < .001, but not for the SAS score, b = .02, t (114) = 1.72, p = .09, PA difference score, b = .05, t (114) = 1.29, p = .20, and NA difference score, b = .06, t (114) = 1.61, p = .11.

Perception between phone usage and learning

For the participants’ perception of their phone usage on their learning and attention span, we found no significant difference between LS ( M = 4.22, SD = 1.58) and HS participants ( M = 4.07, SD = 1.62), t (117) = .54, p = .59, Cohen’s d = .09. There was also no significant correlation between perceived cognitive interference and memory accuracy, r = .07, p = .47.

We aimed [ 1 ] to examine the effect of smartphone presence on memory recall accuracy and [ 2 ] to investigate the relationship between affective states, phone conscious thought, and smartphone addiction to memory recall accuracy. For the former, our results were consistent with prior studies [ 11 – 13 ] in that participants had lower accuracy when their smartphone was next to them (HS) and higher accuracy when separated from their smartphones (LS). For the latter, we predicted that the short-term separation from their smartphone would evoke some anxiety, identified by either lower PA or higher NA post-test. Our results showed that both groups had experienced a decrease in PA post-test, suggesting that the reduced PA is likely to have stemmed from the prohibited usage (HS) and/or separation from their phone (LS). Our results also showed lower memory recall in the LS group who experienced higher NA providing some evidence that separation from their smartphone does contribute to feelings of anxiety. This is consistent with past studies in which participants reported increased anxiety over time when separated from their phones [ 14 ], or when smartphone usage was prohibited [ 17 ].

We also examined another variable–phone conscious thought–described in past studies [ 11 , 13 ], as a measure of smartphone addiction. Our findings showed that phone conscious thought is negatively correlated to memory recall in both HS and LS groups, and uniquely contributed 19.9% in our regression model. We propose that phone conscious thought is more relevant and meaningful compared to SAS as a measure of smartphone addiction [ 15 ] because unlike the SAS, this question can capture endogenous interruptions from their smartphone behaviour and participants were to simply report their behaviour within the last hour. The SAS is better suited to describe problematic smartphone use as the statements described behaviours over a longer duration. Further, SAS statements included some judgmental terms such as fretful, irritated, and this might have influenced participants’ ability in recalling such behaviour. We did not find any support for high smartphone addiction to low memory recall accuracy. Our participants in both HS and LS groups had similar high SAS scores, and they were similar to Kwon et al. [ 33 ] study, providing further evidence that smartphone addiction is relatively high in the student population compared to other categories such as employees, professionals, unemployed. Our participants’ high SAS scores and primary use of the smartphone was for social media signals potential problematic users [ 34 ]. Students’ usage of social networking (SNS) is common and the fear of missing out (FOMO) may fuel the SNS addiction [ 35 ]. Frequent checks on social media is an indication of lower levels of self-control and may indicate a need for belonging.

Our results for the presence of a smartphone and frequent phone conscious thought on memory recall is likely due to participants’ cognitive load ‘bandwidth effect’ that contributed to poor memory recall rather than a failure in their memory processes. Past studies have shown that participants with smartphones could generally perform simple cognitive tasks as well as those without, suggesting that memory failure in participants themselves to be an unlikely reason [ 1 , 3 , 5 ]. Due to our study design, we are unable to tease apart whether the presence of the smartphone had interfered with encoding, consolidation, or recall stage in our participants. This is certainly something of consideration for future studies to determine which aspects of memory processes are more susceptible to smartphone presence.

There are several limitations in our study. First, we did not ask the phone conscious thought at specific time points during the study. Having done so might have determined whether such thoughts impaired encoding, consolidating, or retrieval. Second, we did not include the simple version of this task as a comparison to rule out possible confounds within the sample. We did maintain similar external stimuli in their environment during testing, e.g. all participants were in one specific condition, lab temperature, lab noise, and thereby ruling out possible external factors that may have interfered with their memory processes. Third, the OS task itself. This task is complex and unfamiliar, which may have caused some disadvantages to some participants. However, the advantage of an unfamiliar task requires more cognitive effort to learn and progress and therefore demonstrates the limited cognitive load capacity in our brain, and whether such limitation is easily affected by the presence of a smartphone. Future studies could consider allowing participants to use their smartphone in both conditions and including eye-tracking measures to determine their smartphone attachment behaviour.

Implications

Future studies should look into the online learning environment. Students are often users of multiple electronic devices and are expected to use their devices frequently to learn various learning materials. Because students frequently use their smartphones for social media and communication during lessons [ 34 , 36 ], the online learning environment becomes far more challenging compared to a face-to-face environment. It is highly unlikely that we can ban smartphones despite evidence showing that students performed poorer academically with their smartphones presented next to them. The challenge is then to engage students to remain focused on their lessons while minimising other content. Some online platforms (e.g. Kahoot and Mentimeter) create a fun interactive experience to which students complete tasks on their smartphones and allow the instructor to monitor their performance from a computer. Another example is to use Twitter as a classroom tool [ 37 ].

The ubiquitous nature of the smartphone in our lives also meant that our young graduates are constantly connected to their smartphones and very likely to be on SNS even at work. Our findings showed that the most frequently used feature was the SNS sites e.g. Instagram, Facebook, and Twitter. Being frequently on SNS sites may be a challenge in the workforce because these young adults need to maintain barriers between professional and social lives. Young adults claim that SNS can be productive at work [ 38 ], but many advise to avoid crossing boundaries between professional and social lives [ 39 , 40 ]. Perhaps a more useful approach is to recognise a good balance when using SNS to meet both social and professional demands for the young workforce.

In conclusion, the presence of the smartphone and frequent thoughts of their smartphone significantly affected memory recall accuracy, demonstrating that they contributed to an increase in cognitive load ‘bandwidth effect’ interrupting participants’ memory processes. Our initial hypothesis that experiencing higher NA or lower PA would have reduced their memory recall was not supported, suggesting that other factors not examined in this study may have influenced our participants’ affective states. With the rapid rise in the e-learning environment and increasing smartphone ownership, smartphones will continue to be present in the classroom and work environment. It is important that we manage or integrate the smartphones into the classroom but will remain a contentious issue between instructors and students.

Acknowledgments

We would like to thank our participants for volunteering to participate in this study, and comments on earlier drafts by Louisa Lawrie and Su Woan Wo. We would also like to thank one anonymous reviewer for commenting on the drafts.

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Where Should Students Be Allowed to Use Cellphones? Here’s What Educators Say

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To ban or not to ban? This question has been front and center for many schools recently as they strategize how to address students’ ubiquitous use of cellphones.

With nearly 9 in 10 teens 13 and older possessing a smartphone , these devices have become a major source of distraction and disruption in schools, especially when students’ online arguments spill over into in-school arguments and physical fights.

And many educators and school support staff feel that students’ constant access to social media on their smartphones is harming their mental wellbeing and hurting their ability to learn. Some educators go so far to say that students are addicted to their devices.

Nearly a quarter of teachers, principals, and district leaders think that cellphones should be banned from school grounds, according to a recent nationwide survey conducted in September and October by the EdWeek Research Center.

But, overall, educators are divided on the issue.

“We should be learning to manage cellphones in the classroom. They are here to stay,” one educator said in the survey. “BUT they are the biggest distraction.”

Said another survey respondent: “We recently banned cellphones. Previously, they were allowed during passing time and at lunch. However, they had taken over instructional time. Students would get out their phones without thinking and teachers would have to spend as much time redirecting as they were teaching. That, or have a power struggle over confiscation.”

But schools face headwinds from students and parents—many of whom want to be able to reach their children throughout the day—when they try to restrict students’ access to cellphones during the school day.

And as the charts below show, in many cases there’s a yawning gap between what students are allowed to do and what educators think would be best for schools.

For example, nearly three-quarters of teachers, principals, and district leaders say that high school students in their schools and districts are allowed to use their phones during lunch, but only half believe that should be permitted.

The survey also found that a significantly larger share of teachers are in favor of banning cellphones on campus than district leaders. Principals were more in line with teachers than district leaders on that decision.

The following charts show where students are allowed to use cellphones on campus, where educators think phones should be permitted, and how teachers, principals, and district leaders differ on the issue of an all-out cellphone ban.

Data analysis for this article was provided by the EdWeek Research Center. Learn more about the center’s work.

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Example Of Research Paper On Should Cell Phone Be Banned At School?

Type of paper: Research Paper

Topic: Students , Education , Cell Phones , School , Phone , Telephone , Mobile Phones , Classroom

Words: 2250

Published: 01/26/2021

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The use of cell phone among students at school has generated intense debate over the years. This paper is developed amid this controversy. By relying on varied literature, the paper attempts to answer the question, “Should cell phone be banned at school?” Annotated Bibliography..II This section is split into five parts. Each part explores the perspectives of scholars on the use of cell phone at school. The following articles are explored.

"National School Debate: Banning Cell Phones in Public Schools: Analyzing a National School and Community Relations Problem."

"Cell Phones in American High Schools: A National Survey." "Cell phones for education." "Students and cell phones: Controversy in the classroom." "The use and abuse of cell phones and text messaging in the classroom: A survey of college students." ConclusionIII This part answers the fundamental question regarding cell phone use. The conclusion relies on the author’s submissions on the subject of debate.

Introduction

Opposition towards cell phone use hinges on the claim that use of cell phones at school cause unnecessary distraction to students. This is because students are supposed to learn and concentrate in their studies rather than make or receive phone calls. However, the contrary argument holds that cell phone use is the life line of kids. Kids use cell phones to call their parents. Proponents of cell phone use argue that kids should not be stereotyped on the basis of the negative outcomes that arise due to the use of cell phones. Instead, positive outcomes should be stressed. Studies reveal that students are going to take their phones to school irrespective of the school policies and regulations guiding the use of cell phones. In this regard, cell phone should be allowed at school but after a well thought out plan that would ensure that students do not use cell phone for purposes such as accessing materials for use in an examination situation. In light of these controversies, this paper sought to find an answer to the fundamental question regarding cell phone use. The following section sections give side views of various authors on the subject under study. Their views provide illumination into issues that exists on this subject and a premise upon which a conclusion is made.

Annotated bibliography

Johnson, Clarence, and William Allan Kritsonis. "National School Debate: Banning Cell Phones in Public Schools: Analyzing a National School and Community Relations Problem." Online Submission 25.4 (2007). This article focuses on community relations problems that pitied parents against school authorities following the ban on the use of cell phone among students. New York, Chicago and Miami cities undertook drastic measures to ban the use of cell phones among students. However, this move was frustrated by the futility of keeping phones away from reach of students. The situation was compounded by an external force exerted by parents that want constant access with their children. The authors argue that banning cell phones is counterproductive (Johnson & William 3). To address the community relations, schools are forced to try out friendly policies that allow students to use phones during breaks and lunch. Other policies include asking students to conduct web searches or view educational videos. This is because education stakeholders have realized that they cannot stop students from having cell phones in the classroom settings. The article reveals that parents overbearing desire to maintain constant communication with their children is the main reason why students sneak cell phone into school. This dual collaboration between the parent and the student, defeats an otherwise establishment that discourages the ban. In this regard, a strategy that promotes understanding between the community and the school helps to foster a warm relationship between the stakeholders of education. The article expresses the worry that teachers have about the use of cell phones. While it is important to establish relations between the school and the community, the long term benefit of the educational process to the student should not be overlooked (Johnson & William 4). The article reveals that schools should not condemn the use of cell phones but rather foster self control among students so that they become responsible people that can have control of their processes and activities. Enforcing a ban on the use of phones causes conflict between the teacher and the student (Johnson & William 5). This in turn hurts the relationship between the two important stakeholders in the educational process. Thus, the ban on the use of cell phones negatives the essence of the educational process. This article concludes that banning cell phone use does not solve the problem. Instead, banning cell phone use exacerbates a problem that has already been compounded by inevitable advancement in technology. Obringer, S. John, and Kent Coffey. "Cell Phones in American High Schools: A National Survey." Journal of Technology Studies 33.1 (2007): 41-47. This research carried a survey in American high schools to find out the use of cell phones among students. It developed a survey instrument to achieve this purpose. The findings of the study revealed that many high schools have policies in place reading the use of cell phones. Most parents supported the use of cell phones among students. The use of cell phones has been an increasing phenomenon among students. In 2004, 58% of students between grade 6 and 12 had cell phones. The United States registered an increase in cell phone use by 143.8 million between 1987 and 2002 (Obringer & Kent 41). The authors point out to the worry and concerns among administrators regarding the use of cell phones. These issues include distractions to students, cheating during examinations, bomb threats, text messaging during lessons and cyber bullying of students. In addition to this, students have also been reported to be using the functions on the calculator to cheat during math tests. Despite these worries, many parents see the positive aspects of cell phones as accessibility of students in cases of emergency. The study found out that most schools have disciplinary actions for abuse of cell phones. Students can either be reprimanded or have their phones confiscated. However, most schools are yet to put mechanisms that can address misuse of camera phones. The authors conclude that the use of phones in schools is part of American culture (Obringer & Kent 42). Most schools are embracing this technology. Due to this, schools should review their policies regularly to ensure that they stay ahead of emerging technologies. Roberson, James H., and Rita A. Hagelik. "Cell phones for education." Meridian 11.2 (2012). This article addresses the use of cell phones for educational purposes. It was developed against the backdrop of an earlier ban that the United States Department of Education had placed regarding the use of cell phones in classrooms. The ban was influenced by concerns that students used cell phones to cheat during examinations. It also caused various incidences between students. The authors argue that students should be allowed to use their phones in school. However, the article adds that the use of phone should be monitored to prevent cases of cheating among students. This involves having students placing their phones between the desk and a sandwich bad then sealing them with tapes (Roberson & Rita 3). Through this, students cannot have the temptation to look for answers to examination question through their phones since the teacher would be able to notice unzipping sound as the students try to get their phones. Shaw, Katherine. "Students and cell phones: Controversy in the classroom." Associated Content (2011). Shaw analyzed the controversy that exist in the classroom environment and found out that students use cell phones for various reasons. Shaw posited that phones should not be used in classroom because they don’t belong in the classroom. She added that students who have phones in school are seriously distracted because their attention shifts between classroom activities and socializing. Such students are said to have a short span of attention which limits their involvement in classroom activities. In her work, she reveals the story of a New York City teacher in a public school that recounted a worrying ordeal. While the teacher wrote on the blackboard, students concentrated on their phones to text messages or play games (Shaw 1). The above situation is worrying because not only do the students fail to understand what is taught in the classroom, the essence of the teacher’s time and effort in the classroom is thrown into jeopardy. It thus begs the questions, “When does a student get to listen to the teacher?” “When do students write the notes and participate in the classroom learning activities?” Shaw further observed that students that use phones in school fail in the final exams. This is because they are unable to relate what they are being tested for during the examination period with what they ought to learn, had they not been distracted by phones. Some students have deliberately opted to drop out of school. This is because they are unable to cope with the pace of learning. The onus of failure primarily hinges on the level of distraction that goes on the classroom environment (Shaw 3). Shaw observed that parents feel connected to their children using cell phones. This occurs during events of emergencies. However, emergencies cannot be used as excuses to justify the use of cell phones among students because they hardly occur. In the events that they occur, they do not occur all at once in all schools. Shaw observed that students play video games while the teacher is in class. While they play video games, they become relaxed an inattentive to the activities that take place inside the classroom. Students write messages to one another which make them derive fun than concentrate in the classroom activities. Shaw concludes that it may be impossible to prevent students from carrying phones to school (Shaw 7). Also, if students have cell phones at school, they will take into the classroom. Thus, to reduce their attention, there should be deliberate efforts to acknowledge that cell phones exist in school then ask students to put them on top of their lockers and hold them with stickers to ensure that they do not use them before the teacher. Tindell, Deborah R., and Robert W. Bohlander. "The use and abuse of cell phones and text messaging in the classroom: A survey of college students." College Teaching 60.1 (2012): 1-9. The work of Tindell and Robert reinforces that of Shaw. The authors argue that schools make a set of rules and regulations that govern the conduct and behavior of students while they are at school. These rules are meant to ensure enhanced quality of education. The basic purpose of the school is education and students should work hard towards meeting the goals and vision of their respective school by enhancing quality. But how is quality education enhanced? The authors answer this question by attributing the success stories of most school to a peaceful learning environment. Such a learning environment, they observed, is least distracted (2). Thus, using cell phones in the school environment is tantamount to entrenchment of distraction that has a direct correlation with student underperformance. The authors argued that cell phones cause a painful distraction of the student’s mind whose consequences live long after the student has finished studies at the institution. They observed that cell phones should be banned entirely. Banning should encompass limiting access of cell phones for students while they are in the school compound or while travelling in and from educational institutions.

This paper sought to answer the question, “Should cell phone be banned at school?” By relying on the works of researchers, the study revealed the controversies that have characterized the use of cell phones among students. Most parents support cell phone use among students. This argument is supported by the notion that cell phones help to connect students with their parents during emergencies. However, the contrary argument holds that cell phone use encourages laziness among students because students are distracted during classroom activities. The study found that the argument against use of cell phone is supported by many factors including students’ temptation to cheat using phones, cyber-bullying, bomb threats and writing text messages during class activities. The study recognizes that the use of cell phones among students is a rising phenomenon in the United States. That, regardless of various policies by schools, students would sneak phones into schools. Thus, imposing a ban on the cell phone use might not necessarily address the problems highlighted. In this regard, the cell phone use among students should not be banned. To solve the existing problems regarding phone use, there is need for administrators and policy makers to rethink their policies on phone use. These policies should be addressed in light of the inevitable realities that accompany new technologies.

Works Cited

Johnson, Clarence, and William Allan Kritsonis. "National School Debate: Banning Cell Phones in Public Schools: Analyzing a National School and Community Relations Problem." Online Submission 25.4 (2007). Obringer, S. John, and Kent Coffey. "Cell Phones in American High Schools: A National Survey." Journal of Technology Studies 33.1 (2007): 41-47. Roberson, James H., and Rita A. Hagelik. "Cell phones for education." Meridian 11.2 (2012). Shaw, Katherine. "Students and cell phones: Controversy in the classroom." Associated Content (2011). Tindell, Deborah R., and Robert W. Bohlander. "The use and abuse of cell phones and text messaging in the classroom: A survey of college students." College Teaching 60.1 (2012): 1-9.

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Home — Essay Samples — Information Science and Technology — Cell Phones — Cell Phones in the schools

Cell Phones in The Schools

• Categories: Cell Phones

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Words: 989 |

Published: Dec 5, 2018

Words: 989 | Pages: 2 | 5 min read

Works Cited

• Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2010). The 2010 Horizon Report: K-12 Edition. The New Media Consortium.
• Penuel, W. R., Briggs, D. C., Davidson, K. L., Herlihy, C., Hill, H. C., Farrell, C., ... & Gallagher, D. J. (2017). Findings from a study of research-practice partnerships in education and implications for the future. William T. Grant Foundation.
• Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67, 135-142.
• Boyd, D. (2014). It's complicated: The social lives of networked teens. Yale University Press.
• Heitner, D. (2017). Screenwise: Helping kids thrive (and survive) in their digital world. Routledge.
• Baron, N. S. (2015). Words Onscreen: The Fate of Reading in a Digital World. Oxford University Press.
• Ito, M., Horst, H. A., Bittanti, M., Boyd, D., Herr-Stephenson, R., Lange, P. G., ... & Tripp, L. (2008). Living and learning with new media: Summary of findings from the digital youth project. The John D. and Catherine T. MacArthur Foundation Reports on Digital Media and Learning.
• Christensen, C. M., Horn, M. B., & Staker, H. (2013). Is K-12 blended learning disruptive? An introduction to the theory of hybrids. Clayton Christensen Institute for Disruptive Innovation.
• Warschauer, M. (2018). Learning in the cloud: How (and why) to transform schools with digital media. Teachers College Press.
• UNESCO. (2013). Mobile learning for teachers in UNESCO member states. United Nations Educational, Scientific and Cultural Organization.

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