essay electronic gadgets for online learning

Sample of a Short Essay on Electronic Gadgets

Using electronic devices in the classroom is often underestimated. They can bring a lot of benefits if students and professors use them only for studying purposes. Otherwise, if students use their smartphones and laptops only for entertainment, this misuse significantly distracts them from the learning process and makes their devices uselessm, unless they look for “ write my essay for me cheap ” help.

WritingCheap cheap essay writing service proposes students to read our sample short essay on electronic gadgets. After getting acquainted with this subject, you can understand the methods of using electronic devices in the classroom and how to write a perfect essay by yourself. Impress your teacher with your knowledge of the advantages and disadvantages of electronic gadgets in class.

Effects of Electronic Devices on Education Electronic devices include integrated circuits controlled by the electric current; they are mainly used for processing, transfer, and control systems. Education, on the other hand, involves the process of gaining knowledge through an interactive process. Electronic devices affect education positively and negatively; the positive influence concerns enhancing education, and the negative influences affect the entire learning process. Positive Effects Electronic devices enhance education by making the learning resources easily assessable. By using a computer, students can access education information through the Internet. Additionally, there are technology-related projects that help the student be creative, innovative, and inventive (Eggers, 16). It also improves the teacher-student communication; these devices make a classroom a network system where there is a transfer of information from teacher to student and among students. Moreover, they directly help teachers in educating by bringing out the real picture in the process of giving information. For example, documentaries show the practical experience of events in history. Negative Effects The negative effects include making students spend the most time on devices, time that could otherwise be used for studying. Additionally, the information given tends to diminish the necessity of education. Some devices, such as mobile phones, also affect the learning process through interruptions from calls and text messages. Moreover, there is too much information available on electronic devices, and some of it is wrong. Hence, they tend to misguide students (Chen & Yun 6). Finally, these devices also create an opportunity for cheating among students. Conclusion In conclusion, electronic devices positively affect the communication process by making it easier for both the student and the teacher. However, if they are not contained, they change the process negatively. Therefore, there is a need to establish the best approach to ensure that devices have a positive effect, for example, through creating rules about the use of these devices in a classroom. Works Cited Chen, Shengjian, and Yun Lu. “The Negative Effects and Control of Blended Learning in the University.” 2013 the International Conference on Education Technology and Information System (ICETIS 2013) . Atlantis Press, 2013. Eggers, William D. Government 2.0: Using Technology to Improve Education, Cut Red Tape, Reduce Gridlock, and Enhance Democracy. Rowman & Littlefield, 2017.

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

• Published: 11 November 2022
• Volume 28 , pages 6287–6320, ( 2023 )

Cite this article

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

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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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Acknowledgements

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

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ChatGPT for Teachers

Trauma-informed practices in schools, teacher well-being, cultivating diversity, equity, & inclusion, integrating technology in the classroom, social-emotional development, covid-19 resources, invest in resilience: summer toolkit, civics & resilience, all toolkits, degree programs, trauma-informed professional development, teacher licensure & certification, how to become - career information, classroom management, instructional design, lifestyle & self-care, online higher ed teaching, current events, debating the use of digital devices in the classroom.

While many parents allow children free reign of the internet at home, it’s a common debate in education circles on how —and if —digital devices should be allowed at school.

Supporters of technology in the classroom say that using laptops, tablets, and cellphones in the classroom can keep students engaged. Technology is what they know. Most students today don’t even remember a time without the internet.

But critics say it’s yet another distraction in the classroom. From social media to texting, allowing digital devices could hinder a student’s performance in the classroom.

Read on to discover the main arguments surrounding the global debate on digital devices and their place in our schools.

Pros of digital devices in the classroom

• Peace of mind:  Cellphones and smartphones can offer parents a little more peace of mind when their children are at school. Parents know that in an emergency the student can contact them, or vice versa. In addition, more and more cellphones and smartphones contain GPS devices that can be tracked if necessary.
• Instant answers:  Access to the internet provides instant answers for the curious. This is the search-and-learn environment kids are involved in today. Now, when they want to know “Why do leaves change color,” they are only a search away from an answer. This also gives students the ability to get an answer to a question they may feel uncomfortable asking in class. If a teacher uses a term they don’t understand, they can find the answer discretely, and without interrupting the class.
• Wider access to information:  With internet access, children can be exposed to a world of creative ideas outside of their bubble. They can learn other languages, teach themselves how to draw, knit, or play chess. They have access to an endless array of options available to help them learn, and gain skills they might not otherwise be exposed to. All of this can be accomplished through a  smartphone, which can be a valuable learning tool , if used correctly.
• Access to video:  Electronic devices in the classroom can enhance the learning experience by providing instant video access. Martin Luther King’s “I Have a Dream” speech is not just something to read about. Man’s first step on the moon, early flight, presidential speeches, bridges being built—they all are made more real and easier to digest in the form of instant video availability.
• Wide range of music available:  Sure, you might think of kids listening to their pop, hip-hop, and rap music on digital devices, but remember that all music is available. This gives students access to classical, jazz, big band, and early rock ‘n’ roll. Students could have the opportunity to compare and discuss the differences in these styles in a way that is familiar to them.
• Social learning: Social media can have a negative connotation when you link it to kids. However, there can be an educational aspect. Social learning is a great way for students to share information, thoughts, and ideas on a subject. Properly focused, quieter, and shyer students may blossom in a social learning situation made possible by digital devices.
• Teacher advancement:  Finding ways to effectively utilize digital devices in the classroom provides teachers with an opportunity to advance their skillset and grow with their students. Many teachers are taking their digital literacy to the next level by earning an  master’s degree in education technology .

Cons of digital devices in the classroom

• Harmful effects of digital devices:  There are concerns from the EPA about long-term exposure to wireless devices and computer screens . While there is no direct evidence of harmful effects, the EPA discourages too much exposure for students who have video screens in front of their faces or computers in their laps. If students frequently use these devices at home, additional exposure at school could be viewed as harmful.
• Inappropriate materials:  While schools can limit the availability of websites that can be viewed on their network, students may find links that slipped through the system. There will also be times that students will not be accessing the internet through a monitored network.
• Distraction from schoolwork:  With the temptation of social media and texting in their hands, students may focus solely on their social life instead of the lesson plan.
• Child predators:  Child predators are a problem everywhere. Using digital devices at school creates just that much more exposure and potential danger for students.
• Cyberbulling : This is an increasing issue that’s grown exponentially in recent years. Permitting use of digital devices in the classroom could potentially lead to more of it.
• Provide a disconnect:  While some believe digital devices make for greater connections for students, there are also those who believe too much time with digital devices disconnects students from face-to-face social activities, family communications, and nature. Digital devices in the classroom could lead to an even greater disconnect.
• Could widen the gap : Technology spending varies greatly across the nation. Some schools have the means to address the digital divide so that all of their students have access to technology and can improve their technological skills. Meanwhile, other schools still struggle with their computer-to-student ratio and/or lack the means to provide economically disadvantaged students with loaner iPads and other devices so that they can have access to the same tools and resources that their classmates have at school and at home.

Should schools permit digital devices?

Some school districts have seen great improvements by allowing digital devices in the classroom. One thing is clear: if digital devices are permitted, there should be guidelines and rules in place .

Students need to be taught online safety, the use of judgment in determining good quality sources of information, and restraint from personal use in the classroom. In other words, they need to learn all about digital literacy and  digital citizenship .

There are many resources for teaching these concepts, and a great place to start is the International Society for Technology in Education  (ISTE). Their   comprehensive standards  focus on  the skills and qualities students should have in order to be successful in the digital world. ISTE also teamed up with Google and developed an online digital citizenship game called  Interland . It educates kids about digital citizenship in interactive ways. Students learn how to be good digital citizens as well as how to combat hackers, phishers, oversharers, and bullies.

If a school is going to allow and/or encourage the use of digital devices in the classroom, then teachers also need proper support in terms of training, professional development, and curriculum. They can start with curriculum and PD resources such as those provided by   Common Sense Media , but in order to fully utilize them, teachers need time to plan and collaborate. Digital devices should only be used when there are specific goals in mind, focusing on student safety, digital citizenship, critical thinking, collaboration, advancement, and equity.

You may also like to read

• How to Incorporate Digital Stories in the Classroom
• Teaching Via Tech: Digital Advancements in the Classroom
• Google Docs for Teachers: Classroom and Lesson Plan Management
• Five Skills Online Teachers Need for Classroom Instruction
• 3 Examples of Innovative Educational Technology
• How Assistive Learning Technology has Impacted the Disabled

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Home — Essay Samples — Information Science and Technology — Digital Era — A Digital Revolution in Education Using Tech Tools and Devices

A Digital Revolution in Education Using Tech Tools and Devices

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9 tech tools designed to make online learning better for students and teachers

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• Zoom for Home - DTEN ME: Amazon
• Best 2-in-1 laptop - Microsoft Surface Pro 7: Amazon
• Tablet - Apple iPad: Amazon
• Logitech C270 3MP Webcam: Amazon
• Noise-cancellation headphones COWIN E7 PRO: Cowin
• DOSS SoundBox Touch Portable Wireless Bluetooth Speaker: Amazon
• Shure MV5 external microphone: Amazon
• Atolla 7-Port USB Data Hub Splitter: Amazon
• WiFi extender - NETGEAR WiFi Range Extender EX2700: Netgear

Due to the coronavirus pandemic, a number of schools and universities will continue to operate remotely during this fall. Needless to say, the spring online education had rather mixed results, as teachers and students alike struggled to adapt to a digital curriculum on short notice. Without the face-to-face component of the traditional classroom, distance learning requires a host of technologies to enhance the educational experience. From versatile computing options to basic devices to boost home connectivity, there are a number of devices to help educators and students make online learning better this fall. We’ve curated this roundup to highlight some of the best online learning tools to enhance distance education this fall.

Zoom for Home - DTEN ME

Zoom recently announced a new product category, Zoom for Home, designed with remote work and distance learning in mind. The first Zoom for Home product, DTEN ME , is an exceptional all-in-one workstation for distance learning. The 27-inch 1080p LED touchscreen provides students plenty of digital workspace for interactive lessons. Many laptops come standard with rather lackluster microphones and webcams. The DTEN ME features three wide-angle cameras and an eight-microphone array built into the device that enhances audio and video during lessons.

Best 2-in-1 laptop - Microsoft Surface Pro 7

The ever-popular 2-in-1 laptop design is a great option for distance learning. These models allow students to leverage a traditional screen-and-keyboard style device while offering the versatility and functionality of a standalone tablet. With this in mind, the Microsoft Surface Pro 7 is a solid 2-in-1 model for those so inclined.

The Surface Pro features a 10th Gen Intel Core i7 processor and up to 1TB of storage for optimal performance during the digital school day. Front- and rear-facing cameras (1080p) enable visual collaboration for interactive lessons. The manufacturer estimates that the Surface Pro 7 can last more than 10 hours on a single charge.

Tablet - Apple iPad

Not all students will prefer the look and feel of a 2-in-1 device and some may appreciate a dedicated tablet. With up to 128GB of storage, the classic iPad is a great option and now supports the Apple Smart Keyboard for added versatility. The 10.2-inch Retina display offers plenty of digital workspace in a compact, lightweight handheld device. During more interactive assignments, these handy tools can help with digital collaboration and some students may choose to take notes on the tablet.

Logitech C270 3MP Webcam

Not all computers come with quality webcams and some more affordable devices lack this hardware entirely. A basic webcam is also a great option for those looking to enhance their e-learning experience. This Logitech model offers widescreen HD video and even automatically corrects lighting for crisper video. The C270 3MP comes with a universal clip and easily attached to a wide spectrum of monitors and laptop screens.

Noise-cancellation headphones COWIN E7 PRO

Learning at home can be distracting for students and teachers alike. Headphones are a great option for individuals who want to block outside distractions and tune in for the lesson at hand. This model from COWIN features noise-canceling technology to further eliminate background noise during lessons. The manufacturer estimates that this model is capable of performing for 30 hours on a single charge, which is more than enough for multiple school days.

DOSS SoundBox Touch Portable Wireless Bluetooth Speaker

Not all computers are designed with top-notch speakers onboard. At higher volumes, these factory components can pop and crackle, greatly diminishing the sound quality. In an online lesson, clean crisp audio is imperative. A Bluetooth speaker is a great option for those looking to turn up the volume and dial in for a lesson. During independent learning, students can also use the speaker to play their favorite study playlists. This DOSS model is one of the more popular Bluetooth speakers on Amazon and the battery is more than capable of powering students through a full day of lessons and beyond.

Shure MV5 external microphone

A dedicated external microphone is another easy way to give your virtual collaboration a boost. The Shure MV5 is an exceptional compact model featuring various preset modes (instrumental, flat, vocals) to provide superior audio quality in a host of acoustic settings. As an added benefit, the microphone also detaches from the small mount for a more low-profile fit on a workstation.

Atolla 7-Port USB Data Hub Splitter

For an optimal online learning experience, many students need to leverage numerous devices, often at the same time. Many newer laptops have few (if any) USB ports to help with external recharges. Moreover, running all of these devices off of the laptop can quickly drain its battery midway through the school day. For this reasoning and others, a dedicated external USB charging hub is a great way to keep all of your devices fully charged without burying your laptop under a mountain of cables and dongles.

WiFi extender - NETGEAR WiFi Range Extender EX2700

A child’s remote education experience is wholly hinged to a household’s Wi-Fi connection. Perpetually being booted from a lesson can be a drag on engagement and information retention. For some households, upgrading their home network may be the necessary first step to making online learning better this fall.

Purchasing an entirely new router isn’t necessary in all situations. In fact, simply adding a Wi-Fi extender to an existing network could be an adequate fix for many. These devices work by boosting your existing router signal around the house. This can help shore up parts of the home with low or poor quality connections.

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10 best gadgets for online learning

People could debate all day long about whether online learning is better than actually attending a class, and in truth, there may never be a winner to that argument.

But there’s no doubt that online sessions have their perks. You can do them from the comfort of your bedroom or study, there’s no travel time required, and hey, you don’t even have to get properly dressed (though we wouldn’t recommend this last perk!). All you need is some best gadgets like WiFi, a laptop or a tablet and you’re good to go.

But to make the whole experience a lot smoother and more efficient, the team at Edvoy has put its heads together and come up with some really useful accessories and gadgets for online learning. Some are pretty hi-tech and flashy, while some are absurdly cheap and simple. Either way, each will make your online learning a good deal more comfortable, efficient and enjoyable.

10 Best gadgets for online classes for students

• Headphones w/ microphone
• Ergonomic chair
• Webcam upgrade
• Orthopedic backrest
• Laptop stand
• Standing desk
• Wireless keyboard/mouse
• WiFi Booster
• Desk writing pad
• Smart reusable notebook

1. Headphones with a microphone

There are few things more annoying in the “working from home” world than being disturbed by hearing your housemates' conversations!

With that in mind, get yourself a decent pair of headphones that can keep your sessions (and theirs) private. Though make sure that the headphones you buy also have a microphone, or else you’ll constantly be on mute during that Zoom class!

They don’t have to be big and bulky, nor do they have to be as slick as a set of AirPods, as long as you can hear and be heard clearly without having to shout.

2. Ergonomic chair

You’re going to be spending a lot of time sitting at a desk (or maybe standing… more on that below). So you may as well make sure that you can be comfortable, while keeping your posture right to avoid doing any long term damage!

Oh, and in case you’re wondering, ergonomic means that it’s designed for comfort and efficiency in a working environment. Bonus points for a chair with wheels — it’s fun to scoot around your room between classes. 

3. A webcam upgrade

If you’ve got a modern Macbook or other laptop, chances are you won’t need this. But webcams tend to be pretty poor on standard laptops, and perhaps non-existent if you're working on a desktop computer.

A good upgrade doesn't have to cost much and could make the difference between the digital you looking well… like you, or like a 1990s TV character.

4. Orthopedic backrest

A cheaper alternative to our ergonomic chair, or a pretty great supplement for it too!

The old folks might tell you that back pain is theirs and theirs alone, but as someone who’s been battling it since my high school days, one of the best investments I’ve ever made is a simple posture correcting backrest.

A decent one will allow you to sit comfortably while preventing you from slouching, too much of which leads to back and neck ache. Trust me on that one. 

5. Laptop stand

You can see we’re big on comfort and long term health here, right?

Ideally, the top of your computer screen should be level with somewhere around your nose or eyes. But laptops don’t work that way, and tablets certainly don’t. They like to suck you down into a bent-over position. A simple laptop stand will fix this.

Some are cheap as chips, while the more fancy ones adjust to a range of different heights and even keep your computer nice and cool. You don’t want it to overheat and go offline in the middle of a class, do you?

6. Standing desk

OK, one last comfort/health one and then we’re done, promise!

Standing desks are all the rage these days at techy startups and big corporations alike. Turns out sitting down too much isn’t that good for us (what a shame), so rather than reducing working (or studying) hours, some geniuses have created standing desks.

The thing is, they’re great! The best ones are collapsible, so you can set them up and stow them away easily, allowing you to switch between sitting and standing any time you choose.

7. Wireless keyboard/mouse

Now we’re getting into the gadgety side of these gadgets! Too many wires just makes your work area become cluttered, so getting yourself a bluetooth mouse and keyboard is a great call.

They pack away easily, and allow you some breathing space from your laptop, which will do your eyes (and your posture...again) no harm at all. They also just look pretty cool and futuristic. We like that.

8. Wi-Fi booster / extender

Two different names for essentially the same practical, effective gadget.

Even in the year 2021, houses seem to have WiFi weak spots. That’s a crying shame, and unfortunately, it’s one that could turn your online learning journey into a lagging, poorly connected, cutting-out-again-and-again mess.

One simple way to fix that is to plug directly into your WiFi router, but that probably isn’t in the most work-friendly place. So, a decent (usually quite cheap) WiFi booster should do the trick.

9. Desk writing pad

This isn’t quite a gadget in the modern sense of the word, but it’s going on the list. Why? It’s very simple, very useful, and the best in any stationery store.

Essentially it’s a big diary-style open notepad that (ideally) gently sticks to your desk. Perfect for jotting down reminders, making to-do lists, setting schedules and that kind of thing.

It’s better than an actual notebook because it’s just there… open and visible. A nice eco-friendlier alternative to this, by the way, would be a little whiteboard!

10. Smart reusable notebook

This is by far the coolest online learning gadget I’ve ever seen, and I wish I had one!

It looks like a notebook and pen, but it doesn’t use paper or ink! With a smart reusable notebook, you can listen to your lectures, make hand-written notes (which is faster than typing) on your computer, and everything you (or draw or sketch or doodle) gets uploaded to whatever cloud service you use (G-drive, Dropbox, iCloud etc).

It’s smooth, very cool, and you can feel good about saving trees too!

So now you know the best gadgets for online learning, maybe it’s time to get looking for the best course for you to study at university? Click here to get started with Edvoy, or follow the button below!

Guest Author

Sean is a freelance writer, copywriter & editor from Ireland.

Disclaimer: The views and opinions shared in this site solely belong to the individual authors and do not necessarily represent t ...Read More

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The Importance of Digital Gadgets for Students and Their Educational Achievements

Digital gadgets are essential for modern students today. They save you from carrying dozens of books in your backpack and make life more simple. On your tablet, you may bring thousands of useful literature and, at the same time, save your back from heavy loads.

However, the quantity of books you have doesn’t mean quality in your studying process. To increase your knowledge and improve your professional skills, you can use the nearest source: your gadget. Whether it is a smartphone or tablet, you can turn it into your everyday college helper. You may record your lectures, transcribe them to text documents, track your results with GPA calculator on  gpalabs.com , and share your home tasks in digital classrooms.

If it is all about you and you value your time, read this digitally inspired article created especially for students. Spend your time on the internet wisely and use gadgets for your incredible educational achievements. Thanks to gadgets, you can use various useful learning services like BestCustomWriting to better prepare for lessons and maintain high academic performance.

1. Versatility

Whether you are students of arts or students of engineering and technologies, digital gadgets play a significant role in educational achievements . Digital gadgets are universal tools for students of any grade and discipline. The versatility of digital devices helps every student to customize them under their personal needs.

2. Time-saving

Digital gadgets reduce time on studying and comprehension processes. Students listen to a lot of lectures, and they have a lot of tasks to do after. The combined cognitive activities take nearly 70 hours per week. Using gadgets can save time significantly.

When students use such applications as Speech To Text or OneNote, they save time basically on entering text manually. Transcribing voice to text is useful for long lectures when you are tired of writing down every teacher’s word. And such a program as OneNote will help you to take a photo of notebook pages and convert it to picture or text easily.

3. Multitasking

Most students using smartphones can solve several problems at once. For example, during the class, the student can record the lecture and listen to the teacher. Also, traveling in transport and communicating with friends and relatives, listening to music, or enjoying audiobooks contribute to the development of multitasking .

Using gadgets wisely during studies does not affect the academic performance of students.

4. Fast approach

With the help of gadgets, you can quickly contact your friends, colleagues, and relatives at any time. You may look for the necessary information or need to share it immediately: all you need is your smartphone or tablet and connection to the internet.

Life is full of extraordinary situations, and gadgets are helpful tools to get in touch with friends or services within a moment.

5. Developing language skills

Another advantage of gadgets is the ability to learn foreign languages and improve writing skills. Develop speech and eliminate gaps in systematic education using mobile applications.

Learning with electronic gadgets is a popular way of developing skills in writing as well. Mastering vocabulary, grammar makes it possible to understand the meaning of both native and foreign languages. Besides, gadgets promote installing grammatical corrections and proofreaders. Students can edit the text of their projects automatically.

6. Knowledge improvement

Once students own a gadget, they have a chance to make their learning more enjoyable. Digital devices provide access to various closed online training platforms. Students, regardless of distances, can efficiently study in universities, or on online courses on Udemy, EdX, Teachable, Coursera, or other online platforms. You can visit Ship 30 for 30 and explore other online platforms and get yourself familiar with their offerings.

The most significant advantage of education online is that students can attend classes at any convenient time. By the way, students find the keyboard more fun than standard writing using a traditional pen.

7. Space-saving

Initially, in the early years of tablet usage, wireless technologies were impressive. With the advent of technology, gadgets allow users to feel the freedom of connection when moving around.

A tablet saves a lot of space in a backpack and prevents students from carrying heavy books. Gadgets make things compact, especially when it is wireless earphones, tiny tablets, and thin smartphones.

8. Ecologically friendly

It costs a lot for students to print 400+ pages of paper every semester for course readings only to discard it after a single use to prepare for an assignment or a class discussion. Using electronic devices for learning is a new eco-friendly alternative. It does not just help to protect our trees but also saves costs and provides better education.

9. Tracking a healthy lifestyle

One more gadget that helps to improve your healthy lifestyle is smartwatches. The popularity of smartwatches is enormous today. More and more young people connect them via Bluetooth to smartphones or tablets to keep an eye on their weight, several steps, sleeping hours, and daily sports activities.

10. Unleashing creativity

Gadgets are encouraged to cultivate innovation and creativity. Since technology is challenging, it sparks people to work and study to their full potential. It used to be complicated to start a business in the past. People had to invest lots of capital, but they had limited access to business information. Today, it is effortless to start a business wherever you are.

Companies like Etsy.com encourage creativity and can become an additional source of income. Platforms that enable creative people to sell their works online are excellent tools for students looking for extra money. Another good example is kickstarter.com, which helps people to get funds for their innovations through crowdfunding.

Final Point

Gadgets increase our efficiency. Before the invention of SMS texts or emails, sending traditional letters would take days to reach their destination. The software developers use for applications online is nothing but a technical jump in communication.

These tools have increased human productivity in terms of work and make the world a better place to live. Therefore, after a successful experience of using gadgets, we concluded that modern technology makes learning more enjoyable and efficient.

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

Jen chun wang.

1 Department of Industry Technology Education, National Kaohsiung Normal University, 62, Shenjhong Rd., Yanchao District, Kaohsiung, 82446 Taiwan

Chia-Yen Hsieh

2 Department of Early Childhood Education, National PingTung University, No.4-18, Minsheng Rd., Pingtung City, Pingtung County 900391 Taiwan

Shih-Hao Kung

Associated data.

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

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.

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.

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.

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.

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.

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.

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.

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.

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 ​ Tables10, 10 , ​ ,11, 11 , ​ ,12, 12 , ​ ,13 13 and ​ and14). 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.

KMO and Bartlett's Test

Total variance explained of smartphone behavior

Total variance explained of academic performance

Factor loading of smartphone behavior

Factor loading of academic performance

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 ​ Table2, 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”.

Cross-tabulation of self-control ability

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.

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.

Descriptive analysis

The descriptive analysis (Table ​ (Table1) 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.

Descriptive analysis results

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 ​ Table2 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 ​ Tables3 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 ​ Table3 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.

Significant independent variables (Parental control and Self-control) in the first dimension (interpersonal communication) of smartphone use

*** p  < .001

Independent variables (Parental control and Self-control) in the third dimension (information searches) of smartphone behavior

SD standard deviation

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 ​ Table4 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.

Significant independent variables (Parental control and Self-control) in the second dimension (leisure and entertainment) of smartphone behavior

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 ​ Table5 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.

Pearson’s correlation analysis of smartphone behavior and academic performance

Table ​ Table6 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.

Pearson’s correlation analysis of smartphone use and academic performance

** p  < .01

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 ​ (Table7), 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.

Cluster analysis results

BIC Bayesian information criterion

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 ​ (Table8). 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.

Multivariate analysis of variance results

Df degrees of freedom

Table ​ Table9 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.

Univariate analysis results

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

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 ).

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

• 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.

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.

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.

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.

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.

Acknowledgements

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

Appendix 1 Factor analysis results

Author contributions.

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

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.

Data availability

Declarations.

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

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Jen Chun Wang, Email: wt.ude.unkn@gnawcj .

Chia-Yen Hsieh, Email: wt.ude.utpn@anudnab .

Shih-Hao Kung, Email: wt.moc.oohay@1-hsg .

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Transforming Education with Electronic Gadgets: Improving Student Performance and Enhancing Teaching Methods

In today’s digital age, electronic gadgets such as tablets and smartphones have become ubiquitous in many aspects of our lives, including education. These devices have transformed the traditional classroom setup and created new opportunities for both teachers and students.

A study conducted by Behnke, Gilliland, Schneider and Singer in 2005 found that using gadgets such as tablets in class can contribute to improved student performance. The study showed that the use of electronic devices can help increase student engagement, promote active learning, and enhance the learning experience. This can lead to improved academic achievement and a decrease in the number of students who perform poorly in class.

Also see 10 Benefits and Uses of Electronic Gadgets in Learning

The benefits of using gadgets in the classroom extend beyond just improving student performance. These devices can also help improve the efficiency of teaching methods and learning capacities of students. By using tablets in class, teachers can create a more interactive and engaging learning environment. With the help of educational apps and software, teachers can deliver their lessons in a more dynamic and interactive way, allowing students to actively participate in the learning process.

These 7 tablets offer a range of features to enhance the learning experience, from interactive apps to powerful processing capabilities. Each has its strengths, so the choice may depend on specific classroom needs and preferences.

• Apple iPad Pro (2023) : Known for its powerful performance and vibrant display, the iPad Pro is a favorite for its versatility, making it ideal for various educational applications and interactive lessons.
• Samsung Galaxy Tab S8 : Samsung’s flagship tablet offers a stunning Super AMOLED display and powerful hardware. It’s a great choice for educators looking for a high-performance Android tablet.
• Microsoft Surface Pro 8 : With its detachable keyboard and stylus support, the Surface Pro 8 is a versatile 2-in-1 device. It runs on Windows, providing compatibility with a wide range of educational software.
• Lenovo Tab P12 Pro : Lenovo’s premium tablet boasts a sleek design and a powerful processor. It’s suitable for both entertainment and productivity, making it a good fit for educational purposes.
• Google Pixel Slate : Running on Chrome OS, the Pixel Slate offers a desktop-like experience in a tablet form. It supports a variety of educational apps from the Google Play Store and benefits from seamless integration with Google Workspace for Education.
• Amazon Fire HD 10 Kids Pro : Specifically designed for younger students, the Fire HD 10 Kids Pro comes with robust parental controls, educational content, and a durable build. It’s an excellent choice for elementary and middle school classrooms.
• Huawei MatePad Pro 12.6 : Huawei’s flagship tablet combines a premium design with powerful hardware. It’s suitable for multitasking and content creation, making it a good option for teachers who require a versatile device.

Moreover, electronic gadgets have made activities conducted in classrooms more flexible. These devices offer various features and functions that can efficiently transform teaching and learning methods. For instance, teachers can use tablets to incorporate multimedia elements such as videos, images, and animations to make their lessons more engaging and dynamic. This can help students better understand complex concepts and retain information more effectively.

In addition, different senses of students are activated through the use of gadgets in the classroom. Students can learn through auditory, visual, and kinesthetic means by using electronic devices. This means that students who have different learning styles can benefit from using gadgets in the classroom. For instance, visual learners can benefit from watching educational videos on a tablet, while auditory learners can benefit from listening to audio recordings of their lessons.

Despite the many benefits of using gadgets in the classroom, some people are concerned about the potential drawbacks. One of the major concerns is that the use of electronic devices can be a distraction for students. With social media, messaging apps, and other digital distractions just a click away, students may find it difficult to stay focused during class. However, teachers can mitigate this risk by establishing clear guidelines on the use of gadgets in the classroom and setting up parental controls on devices to limit access to non-educational content.

Another concern is the potential negative impact of screen time on children’s health. While excessive screen time can have negative effects on children’s physical and mental health, the use of electronic devices in the classroom can be a healthy alternative to traditional teaching methods. With the right balance of screen time and physical activity, students can enjoy the benefits of using gadgets in the classroom without putting their health at risk.

All that being said, the use of electronic gadgets in the classroom can contribute to improved student performance, enhance the efficiency of teaching methods, and activate different senses of students. Despite some concerns, the benefits of using gadgets in the classroom far outweigh the potential drawbacks. With the right guidelines and balance, teachers can use these devices to create a more engaging, interactive, and dynamic learning environment for their students.

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I love how they go in detail in every episode of the show, didn't know I would enjoy home gadget info so much.

One of the best shows for the home pc enthusiast. I've been listening to it each week for over two years and I'd highly recommend it. Jim and team are very professional and the show is very informative.

This is one podcast you dont want to miss. They may talk alot of tech but they make it so anyone can understand it. Dont get lost in the tech jungle list to what they say and do your research. Subscribe and be treated to a ton of knowlage.

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so sorry, seems i left a review under my husband's name of pytheas2.0! LOL! well, now he is subscribed :) but thanks for hosting a great show with a variety of information! I'm a photographer and no where near knowing about techy stuff, so maybe i'll learn a thing or two and impress my husband ;)

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If you have a connected home, you owe it to yourself to listen to this podcast. The tips, tricks and news save me tons of time from browsing a ton of websites constantly.

Pretty Tech savvy. Thanks!

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Prediction on Impact of Electronic Gadgets in Students Life using Machine Learning

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Jessica Grose

Screens are everywhere in schools. do they actually help kids learn.

By Jessica Grose

Opinion Writer

A few weeks ago, a parent who lives in Texas asked me how much my kids were using screens to do schoolwork in their classrooms. She wasn’t talking about personal devices. (Smartwatches and smartphones are banned in my children’s schools during the school day, which I’m very happy about; I find any argument for allowing these devices in the classroom to be risible.) No, this parent was talking about screens that are school sanctioned, like iPads and Chromebooks issued to children individually for educational activities.

I’m embarrassed to say that I couldn’t answer her question because I had never asked or even thought about asking. Partly because the Covid-19 era made screens imperative in an instant — as one ed-tech executive told my colleague Natasha Singer in 2021, the pandemic “sped the adoption of technology in education by easily five to 10 years.” In the early Covid years, when my older daughter started using a Chromebook to do assignments for second and third grade, I was mostly just relieved that she had great teachers and seemed to be learning what she needed to know. By the time she was in fifth grade and the world was mostly back to normal, I knew she took her laptop to school for in-class assignments, but I never asked for specifics about how devices were being used. I trusted her teachers and her school implicitly.

In New York State, ed tech is often discussed as an equity problem — with good reason: At home, less privileged children might not have access to personal devices and high-speed internet that would allow them to complete digital assignments. But in our learn-to-code society, in which computer skills are seen as a meal ticket and the humanities as a ticket to the unemployment line, there seems to be less chatter about whether there are too many screens in our kids’ day-to-day educational environment beyond the classes that are specifically tech focused. I rarely heard details about what these screens are adding to our children’s literacy, math, science or history skills.

And screens truly are everywhere. For example, according to 2022 data from the National Assessment of Educational Progress, only about 8 percent of eighth graders in public schools said their math teachers “never or hardly ever” used computers or digital devices to teach math, 37 percent said their math teachers used this technology half or more than half the time, and 44 percent said their math teachers used this technology all or most of the time.

As is often the case with rapid change, “the speed at which new technologies and intervention models are reaching the market has far outpaced the ability of policy researchers to keep up with evaluating them,” according to a dazzlingly thorough review of the research on education technology by Maya Escueta, Andre Joshua Nickow, Philip Oreopoulos and Vincent Quan published in The Journal of Economic Literature in 2020.

Despite the relative paucity of research, particularly on in-class use of tech, Escueta and her co-authors put together “a comprehensive list of all publicly available studies on technology-based education interventions that report findings from studies following either of two research designs, randomized controlled trials or regression discontinuity designs.”

They found that increasing access to devices didn’t always lead to positive academic outcomes. In a couple of cases, it just increased the amount of time kids were spending on devices playing games. They wrote, “We found that simply providing students with access to technology yields largely mixed results. At the K-12 level, much of the experimental evidence suggests that giving a child a computer may have limited impacts on learning outcomes but generally improves computer proficiency and other cognitive outcomes.”

Some of the most promising research is around computer-assisted learning, which the researchers defined as “computer programs and other software applications designed to improve academic skills.” They cited a 2016 randomized study of 2,850 seventh-grade math students in Maine who used an online homework tool. The authors of that study “found that the program improved math scores for treatment students by 0.18 standard deviations. This impact is particularly noteworthy, given that treatment students used the program, on average, for less than 10 minutes per night, three to four nights per week,” according to Escueta and her co-authors.

They also explained that in the classroom, computer programs may help teachers meet the needs of students who are at different levels, since “when confronted with a wide range of student ability, teachers often end up teaching the core curriculum and tailoring instruction to the middle of the class.” A good program, they found, could help provide individual attention and skill building for kids at the bottom and the top, as well. There are computer programs for reading comprehension that have shown similar positive results in the research. Anecdotally: My older daughter practices her Spanish language skills using an app, and she hand-writes Spanish vocabulary words on index cards. The combination seems to be working well for her.

Though their review was published in 2020, before the data was out on our grand remote-learning experiment, Escueta and her co-authors found that fully online remote learning did not work as well as hybrid or in-person school. I called Thomas Dee, a professor at Stanford’s Graduate School of Education, who said that in light of earlier studies “and what we’re coming to understand about the long-lived effects of the pandemic on learning, it underscores for me that there’s a social dimension to learning that we ignore at our peril. And I think technology can often strip that away.”

Still, Dee summarized the entire topic of ed tech to me this way: “I don’t want to be black and white about this. I think there are really positive things coming from technology.” But he said that they are “meaningful supports on the margins, not fundamental changes in the modality of how people learn.”

I’d add that the implementation of any technology also matters a great deal; any educational tool can be great or awful, depending on how it’s used.

I’m neither a tech evangelist nor a Luddite. (Though I haven’t even touched on the potential implications of classroom teaching with artificial intelligence, a technology that, in other contexts, has so much destructive potential .) What I do want is the most effective educational experience for all kids.

Because there’s such a lag in the data and a lack of granularity to the information we do have, I want to hear from my readers: If you’re a teacher or a parent of a current K-12 student, I want to know how you and they are using technology — the good and the bad. Please complete the questionnaire below and let me know. I may reach out to you for further conversation.

Do your children or your students use technology in the classroom?

If you’re a parent, an educator or both, I want to hear from you.

Jessica Grose is an Opinion writer for The Times, covering family, religion, education, culture and the way we live now.