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Media Multitasking, Online Learning, and Social Media Activities among Adolescents
Suriati Gani
International Leadership University
ARTICLE INFO ABSTRACT
Article History:
Accepted: 28-08-2021 Approved: 19-10-2022
Abstract: The increasing prevalence of the overuse of social media among adolescents has raised concerns. Educators worry that it may decrease the cognitive functioning of these adolescents. This study of 245 secondary-school students in Palu, Indonesia, reveals that there is a correlation between these students’ diminished perceived attention and the intensity of their internet and social media use. Due to their eagerness to multitask, students who check their social media notifications while doing their online learning experience perceived attention discontinuity. This study suggests the significance of prevention and intervention for adolescents’ media-related problems as they multitask in their online learning environments.
Keywords:
adolescents’ social media use;
cognitive functioning;
online learning media multitasking;
perceived attention problems;
personal learning environment Corresponde Address:
Suriati Gani
International Leadership University Mtito Andei Rd., North, Nairobi, Kenya E-mail: [email protected]
Technology use is an inevitable part of life in the 21st century. The arrival of Web 2.0, 3.0, and 4.0 not only changed already available web technologies, but also changed the ways we communicate with, relate to, and learn from one another (Kimbrell, 2013; Darweesh, 2018). Conjointly, the amount of information that is available online through the World Wide Web has increased exponentially, and the accessibility of this information has likewise increased with the introduction of various modern multimedia devices (Wiradhany & Nieuwenstein, 2017). Barr et al. (2020) add on that digital media availability has surged over the past decade. Ubiquitous Web access and the variety of devices that enable students to interact with it have given students the opportunity to pick and choose tools and services that are better adapted to their needs, as well as providing them with means for personalizing their learning experience (Torres-Kompen et al., 2019). For example, as Internet use becomes more popular, its influence and effects become more widespread in various societal circles, especially in student circles. As Cataldo et al.
(2021) agree, children and teenagers grew up together with Internet-based services, which have become an integral part of their personal and social lives. The Internet has become the main forum for learning in higher and lower education. Apart from being a rich source of information, however, the Internet also offers many distractions that challenge student attention (Wu, 2015).
Simultaneous with Internet use, digital technology is a significant factor that makes our contemporary lives very different than even twenty years ago. It makes sense for us to anticipate that digital technology will continue to be an important part of how our lives will be shaped in the future, given that the nature of global economics, politics, culture, and society continues to shift (often in unexpected ways) (Selwyn et al., 2020). Furthermore, digital technology has had and will continue to have a sizeable impact on the economy and society as it changes the way we work, communicate, engage in social activities, and enjoy our time alone. It does not come as a surprise, then, if there is a strong correlation between education and the absorption and use of digital technology in various areas of life (Organization for Economic Co-operation and Development [OECD], 2016). For example, information and communication technologies, specifically the Internet, have widened the boundaries of learning in student circles and have made education much cheaper and easier to access (Wu, 2017; Gebremeskel et al., 2016; Perna et al., 2015; Talebian et al., 2014; United Nations Educational, Scientific and Cultural Organization [UNESCO], 2014).
Likewise, Serdyukov (2017) noted that applied technologies (such as computer-based learning, networked learning, and e-learning) are part of educational innovation. These innovations have had a drastic impact on the educational system as a whole. Moreover, there is now compelling evidence about the impact of technological transformation on human activities, especially in facilitating teaching and learning (Brown, 2015). For example, Massive Open Online Courses (MOOC) platforms are transforming the way professional information, including teaching and learning, is disseminated (García-Peñalvo et al., 2016; Park, 2018). Within the world of higher education, a hybrid approach is often employed wherein some learning activities proceed face-to-face in the classroom while other activities take place online (OECD, 2015; Zhu et al., 2016). This is especially so since the World Health Organization (World Health Organization, 2020) announced COVID-19 a pandemic on March 11,
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2020. It has become increasingly clear that learning facilitated through the Web, the Internet, and other digital technologies has become a relied-upon alternative.
Internet and Social Media Use in a Personal Learning Environment
Digital technology has been at the center of the COVID-19 pandemic globally. Madianou (2020) states that the public health response to the COVID-19 pandemic has led to the unprecedented situation of millions of people relying almost exclusively on communication technology to work, study, and socialize. Likewise, scientific circles have also seen big potential in integrating social media technology into secondary and tertiary education (OECD, 2015; Matzat & Vrieling, 2016). As one example, the Personal Learning Environment (PLE) is a potentially promising pedagogical approach for both integrating formal and informal learning using social media and supporting student self-regulated learning in higher education contexts. PLEs created by social media (such as Facebook, WhatsApp, and Twitter) enable students to share, communicate, and collaborate with one another in formal and informal learning (Dabbagh & Kitsantas, 2012; Lee & Wu, 2012; Wu, 2017). In fact, social media platforms, such as Facebook, Twitter, and Instagram, are now part of almost everyone’s social life, especially for the younger generations (Cataldo et al., 2021). Moreover, Lau (2017, p. 286) emphasizes, "In today’s society, social media have become an almost indispensable part of daily life, particularly among university students, who are generally heavy social media users. Social media multitasking has also been increasingly prevalent."
Furthermore, advancements in technology in the domain of social media have contributed to numerous changes in daily functioning (Cudo et al., 2019). Foehr (2006) acknowledged that the way young people use media is changing dramatically. "Teens and tweens in particular have turned into real experts at multitasking.... For them, multitasking has simply become a way of life. Much of the multitasking they do revolves around media use" (p.1). Media multitasking is a common occurrence. With regard to education, Seemiller and Grace (2016), in their book Generation Z Goes to College, highlight that a learning environment that utilizes the interests and strengths of Generation Z (those born between 1995 and 2010), has to look very different from the environments of previous generations. Education is no longer the transfer of knowledge between teachers and students; rather, teaching takes the form of one’s guiding students towards an understanding of the abundance of information that is available to them. Therefore, sources of learning that already exist and are being continuously employed by this generation of students are electronic media sources such as the computer and the Internet. Cataldo et al. (2021) add that social media usage is often included in the broader category of Internet usage, despite the social connotation that primarily describes and defines these kinds of sites. With the prevalence of social media usage, students are likely already habituated to an abundance of information and face no issues in looking for information (Twenge, 2017).
Thus, a Personal Learning Environment (PLE) is no longer just a rich source of information for students; it also offers social opportunities and constitutes an independent environment (Kop & Fournier, 2011). In order to achieve a good study outcome in this kind of environment, students have to be able to arrange their focus and attention (Johnson & Sherlock, 2014;
Wu, 2017), especially when learning online. As Van der Schuur et al. (2015) reviewed, with the rise of mobile media technologies, the availability of media for students (especially youth) has increased dramatically. The emergence of multi- function devices has created a perceived need to always be connected to multiple media devices (Martín-Perpiñá et al., 2019), and the constant availability of media has led to an increase in media multitasking. Instead of being focused on a single task or stream of information, people try to monitor and interact with multiple streams of information simultaneously (Uncapher et al., 2016; Wiradhany & Nieuwenstein, 2017). Thus, young people tend to engage in more than one media activity at a time (Foehr, 2006; Brasel & Gips, 2011) or use two or more media simultaneously (May & Elder, 2018). Moreover, the rapid advancement of mobile computing devices and the ever-growing range of infotainment services they enable have cultivated high levels of media multitasking (i.e., engaging in more than one media or non-media activity simultaneously) (Parry & Le Roux, 2018; Le Roux & Parry, 2019).
Media Multitasking and Its Impact on Attention
Empirical research on media multitasking shows that it affects students’ attention and cognitive learning activities. As explained by Carrier et al. (2015), while multitasking through electronic device use is common practice across all age groups, it is most prevalent among younger generations—especially in multimedia form. This simultaneous use of multiple media streams is becoming the "new norm" across generations (Baumgartner et al., 2017; Uncapher et al., 2016). Not surprisingly, for students, multitasking at school or at home during private study is common. Cain et al. (2016), as well as Stavrinos et al. (2019), have also observed that, among adolescents, media use has been rising overall. In particular, simultaneous multimedia consumption (e.g., texting while watching TV, or switching between such activities as instant messaging (IM), email, ordering a book online, and catching a quick headline) has also notably been increasing (Foehr, 2006). If students are unable to exercise self-control and organization, it is not impossible that their attention will be directed towards things that are unrelated to the topic of study at hand.
Importantly, Stavrinos et al. (2019) remind us that adolescence is a critical period in brain development, particularly in regions related to attention and executive function. Related to this fact, Wu (2017, p. 57) emphasizes that "failure to regulate attention can lead to concurrent engagement in multiple media applications that are irrelevant to learning (e.g., texting, online chatting, non-homework-related Internet use)." Because the cognitive information process occurs after attention is established, the ability of learners to stay engaged and focused is critical. Attention sets the necessary conditions for "the onset of major cognitive learning activities" (Petersen & Posner, 2012; Uncapher et al., 2016). Essential for a learner to achieve focused attention is their ability to be aware of and exercise willful regulation over their attention (Reisberg & McLean, 1985; Wu, 2017). Attention may be broadly defined as the cognitive state that allows organisms to volitionally select one or more spatial locations for prolonged preferential sensory processing, normally in the presence of an attended visual stimulus (Libedinsky &
Fernandez, 2019, para. 3). As Cherry (2021) explains, attention is the ability to actively process specific information in the environment while tuning out other information, sensations, and perceptions that are not relevant at the moment and instead focusing your energy on the information that is important. In other words, "attention is a process carried out through cognitive abilities, to select something out of the various stimuli present in one environment and then to bring it to the center of one’s consciousness in order to perceive it clearly" (Mangal, 2012, p. 160). When students pay attention to things, they become mentally prepared for upcoming events and are automatically ready to respond to stimuli. Mangal describes some of the effects of attention as follows:
Attention provides strength and ability to continue the task of cognitive functioning despite the obstacles by the forces of distraction like noise and unfavourable weather conditions. Attention helps in providing proper deep concentration by focusing one’s consciousness upon one subject at one time rather than on any others. (p.160) Cherry also notes that since attention is limited in terms of both capacity and duration, it is important for students to be able to effectively manage the attentional resources they have. This is especially true in the midst of PLEs, as multitasking behavior has increasingly become a common phenomenon affecting student learning.
Moreover, Moisala et al. (2016) report that everyday media multitasking is associated with poorer control over attention. Multitasking behavior, in turn, has been associated with "various cognitive differences, such as difficulty filtering distracting information and increased trait impulsivity" (Uncapher et al., 2016). Recent research has also identified working memory as a critical component of multitasking ability (Redick, 2016). Due to the limited capacity of our cognitive resources, attempting to attend lectures and engage digital technologies for off-task activities can have a detrimental impact on learning (Wood et al., 2012; Wu & Peng, 2016). The "mere presence" of a cell phone may produce diminished attention and worsened task-performance, especially for tasks with high cognitive demands (Thornton et al., 2014); in addition, frequent media multitaskers exhibit poorer working memory performance and increased attentional impulsivity (Uncapher et al., 2016).
Media Multitasking and Its Impact on Academic Performance
May and Elder (2018) further described that academic performance is strongly influenced by mental habits—such as attention switching and division, as well as keeping track of multiple lines of thought—exercised and formed during media multitasking. Media multitasking has been found to be negatively correlated with learning outcomes or academic performance (Rosen et al., 2013; Sana et al., 2013; Chen & Yan, 2013; Van der Schuur et al., 2015; Patterson, 2017; Wammes, 2019). The research also indicates that media multitasking interferes with attention and working memory, negatively affecting GPA, test performance, recall, reading comprehension, note-taking, self-regulation, and efficiency (Cain et al., 2016; Kushniryk &
Levine, 2012; Lepp et al., 2015; May & Elder, 2018; Wu, 2017; Glass & Kang, 2019). Similarly, Lau (2017) confirms that using social media for non-academic purposes (video gaming in particular) and social media multitasking significantly negatively predicted academic performance.
As noted briefly above, digital technologies are now an integral aspect of the university student experience (Henderson et al., 2017; Hazelrigg, 2019); high levels of digital media use are emerging and have become a feature of university lectures (Castañeda & Selwyn, 2018; Parry et al., 2020). The current generation of university and secondary school students display an increasing propensity for media multitasking behavior with digital devices such as laptops, tablets, and smartphones.
Consequently, the use of laptops or smartphones for purposes unrelated to the lecture is on the rise (Wammes, 2019). Le Roux and Parry (2009) have investigated and considered the effects of this form of behavior on cognitive control ability, with findings suggesting that chronic media multitasking is associated with reduced inhibitory control.
A growing body of empirical evidence has shown that this behavior is associated with reduced academic performance (Le Roux and Parry, 2017; Hazelrigg, 2019). Media multitasking is associated with changes in cognitive control and failures of everyday executive functioning (Parry & Le Roux, 2018). According to Mackie et al. (2013), attentional functions may be involved in the implementation of cognitive control as required to reduce uncertainty. Thus, cognitive control is essential to flexible, goal-directed behavior under uncertainty. Wilmer et al. (2017) noted that while smartphones and related mobile technologies are recognized as flexible and powerful tools that, when used prudently, can augment human cognition, there is also a growing perception that habitual involvement with these devices may have a negative and lasting impact on users'
abilities to think, remember, pay attention, and regulate emotion. Echoing this, Castañeda and Selwyn (2018) emphasize that, while certainly capable of supporting learning outcomes, studies indicate that, when media use is off-task, it presents as a disruption, distracting both users and those around them from academic tasks. For example, Rabl et al. (2019) reported that approximately a quarter of class time is lost to non-course-related activities and that students underestimate their own laptop use in comparison to tracking data. Even though they are aware of the distractive potential of the digital devices, this awareness has little effect on the students’ actual behavior, and they give in to the digital temptations despite knowing the potential for distraction. Similarly, a study in 2018 shows that splitting attention between instruction and cellphone or laptop use obstructs long-term retention (Whitford, 2018). Students were wrong to believe they could divide their attention between technology and class lectures, as technology reduces their ability to retain information from the class (Hazelrigg, 2019). Glass and Kang (2019) found that students who are more likely to divide attention between academic and non-academic tasks in class are also more likely to do so outside of class. The author of this article is confident that this occurs in student PLEs as well.
In light of this, the intention of this study is to examine adolescents, especially secondary school students, in their internet and social media usage that usually occurs together with media multitasking in PLEs and how these patterns of behavior affect their attention. In order to achieve the ultimate goal of education—good academic performance—especially with regard to high achievements in the cognitive domain (Purković & Kovačević, 2020), learners need a good understanding of their attention state as well as good strategies to regulate their attention, especially while learning online using media devices (Wu, 2017).
METHODS
The goal of this study was to investigate the frequency with which secondary school students use the internet and social media, as well as the factors associated with media multitasking and perceived attention problems. Thus, the correlational study, a research design used to describe the variables or assess the relationships between and among two or more variables that occur naturally between and among them (Stangor, 2011), was used in the research.
The research was carried out on a sample group. Participants were comprised of a total of 270 students (between the ages of 11 and 19) enrolled in the 7th, 8th, and 9th grades (middle school, 66%) and the 10th, 11th, and 12th grades (high school, 34%) of three private secondary schools in Palu, Central Sulawesi, Indonesia. A total of 268 questionnaires were returned, of which 265 were considered valid and eligible to be included in this study. Students who participated in the study were 52% girls, 47% boys, and 1% did not answer or declare their gender. Respondents participated in the study on a voluntary basis, and responses were anonymous. The confidentiality and anonymity of the students were maintained throughout the study.
The survey’s front page noted that participants should not give false answers. Students were told that their decision to participate or not participate would not affect their rights or academic grades. As a surprise token of appreciation, after completing and returning the questionnaire, each student may choose and take one silicone wristband bracelet.
Self-administered questionnaires were used to collect data. The questionnaire used in this research consisted of two parts. The first part was focused on three aspects, including: 1) participants’ demographic elements (namely, age, gender, grade, and name of school), digital devices they have and use, and family financial situation; 2) parental role in facilitating students’
digital device needs and parental attitude towards the adolescents’ digital device use; and 3) pattern of digital device use by the adolescents, including average time spent on the Internet per day, average time spent on Facebook and other social media accounts per hour, average number of Facebook and other social media account visits per day, and average time spent on Facebook and other social media accounts per day; one question asked students about "how active they consider themselves compared to their peers in using the Internet and social media." Responses were measured on a Likert scale in order to avoid extreme values.
The second part of the questionnaire required students to report their perceived attention state and regulatory strategies in online learning using the revised Online Learning-Motivated Attention and Regulation Strategies (OL-MARS v.2) scale, developed by Wu (2017). It comprised 24 items in two constructs of engagement in multitasking, namely, perceived attention problems (PAP) and self-regulatory strategies (SRS). PAP includes three subscales: lingering thoughts (4 items), social media notifications (3 items), and perceived attention discontinuity (8 items), while SRS includes two subscales: outcome appraisal (3 items) and behavioral strategies (6 items). The responses were graded on a 5-point scale, with 1 representing extreme disagreement and 5 representing extreme agreement.
Having obtained the original English translation from the author of the revised OL-MARS v.2 scale, the author adapted the scale for an Indonesian context. After obtaining permission from the author, OL-MARS v.2 was translated into Indonesian.
The goal of cross-cultural translation is to achieve equivalence between two different languages. This study uses the Brislin model for instrument translation, which is a well-known method for cross-cultural research (Brislin, 1970; Jones et al., 2001).
The forward and backward translations of the questionnaires were carried out by two bilingual individuals. The first individual, who had experience in English-to-Indonesian translation, translated the English version into the target language, Bahasa Indonesia. Then a second translator, another bilingual person, translated the revised OL-MARS v.2 independently back from the
target language to its original language. To ensure the equivalency of the translated documents, the back translation is done by blinding the second translator to the original scale. In other words, the Indonesian version was back-translated into English by another independent translator with no prior knowledge of the original version of the questionnaire. Both versions (the original and the back-translated questionnaire) were then compared for accuracy. Finally, the Indonesian versions were sent to the original author (Professor Wu-Jiun Yu) to obtain his authorization and comments. Then, an exploratory relational study was carried out in mid-October 2019 at the three private secondary schools.
After collecting the data, Statistical Package for Social Sciences (SPSS) 18 software was used for statistical analysis of the research data. Cronbach’s alpha coefficient was used to calculate the internal consistency and reliability of the revised OL- MARS v.2 scale. Pearson’s correlation coefficient, the test statistic that measures the statistical relationship, or association, between two continuous variables, was also used. Then, a factor analysis was also carried out.
RESULTS
The revised OL-MARS scales’ internal consistency and reliability were assessed by measuring Cronbach’s alpha value for each sub-scales; values ≥ 0.70 were considered satisfactory (Bland & Altman, 1997; Taber, 2018). Studies for the revised OL-MARS items scale among the students reported adequate and good internal consistency or reliability (Cortina, 1993;
Taber, 2018), obtained Cronbach’s alpha of 0.807.
A Pearson correlation coefficient was computed to assess the relationship between the 24 statements of the revised OL- MARS and the student’s activeness in Internet and social media usage, which was measured with the following question:
"According to your own estimation, compared to your peers, are you an active internet and social media user?" (See Table 1).
The correlation analysis can be found in Table 2. Statements that have a significant correlation with the activeness of the Internet and social media usage are numbers 1, 3, 10, 11, 12, 13, and 16. From these results, we see that student activeness in internet and social media usage proves to be tightly associated with perceived attention problems while multitasking with media. For example, when working on learning tasks, students often engage in other tasks and tend to check their social media notifications immediately.
Table 1. The Correlation of Online Learning Motivated Attention and Regulation Strategies (OL-MARS) and Activeness in Internet and Social Media Usage
Statement AISMU
r n p
1. Perceived Attention Discontinuity 7 2. Perceived Attention Discontinuity 3 3. Perceived Attention Discontinuity 4 4. Outcome Appraisal 2
5. Perceived Attention Discontinuity 6 6. Perceived Attention Discontinuity 2 7. Behavioural Strategies 4
8. Behavioural Strategies 3 9. Outcome Appraisal 3 10. Social Media Notifications 1 11. Perceived Attention Discontinuity 1 12. Perceived Attention Discontinuity 5 13. Social Media Notifications 2 14. Outcome Appraisal 1 15. Behavioural Strategies 1 16. Social Media Notifications 3 17. Perceived Attention Discontinuity 8 18. Lingering Thoughts 1
19. Lingering Thoughts 2 20. Lingering Thoughts 4 21. Lingering Thoughts 3 22. Lingering Thoughts 2 23. Behavioural Strategies 6 24. Behavioural Strategies 5
.227 .115 .134 .002 .073 .098 .036 .008 .007 .278 .221 .227 .209 .040 .004 .199 .061 .090 .056 .041 .011 .030 .042 .013
263 263 263 263 263 263 263 263 263 262 261 262 263 262 260 260 263 262 260 262 259 261 262 262
.000 .062 .030 .974 .240 .114 .557 .896 .905 .000 .000 .000 .001 .520 .947 .001 .326 .145 .373 .511 .856 .634 .503 .830
Internet and Social Media Activeness 1 265
Note. AISMU = Activeness in Internet and Social Media Usage. r = Pearson correlation coefficient. n = Sample. p
= p value.
Of those 7 statements, which correlated significantly with the activeness of students in using the Internet and social media, a factor analysis was carried out, and the results obtained 2 factors, namely, "checking notifications immediately" or
"social media notifications" (SMN) and multitasking, doing other things while completing tasks, or "perceived attention discontinuity" (PAD), as Wu (2017) calls it. Social media notifications consist of variables 10, 13, and 16; and perceived attention discontinuity consists of variables 1, 3, 11, and 12. As expected, the perceived attention discontinuity and social media notifications as part of perceived attention problems are significantly correlated with students’ activeness on the Internet and social media usage. This result agrees with Wu (2017, p. 57), who shows that "failure to regulate attention can lead to concurrent engagement in multiple media applications that are irrelevant to learning (e.g., texting, online chatting, non- homework-related Internet use)." This phenomenon has been referred to as "media multitasking."
Table 2. Results from a Factor Analysis of the Correlation between the Revised OL-MARS scale and Activeness in Internet and Social Media Usage
OL-MARS Item 1 2
Factor 1: Perceived Attention Discontinuity
1. I often visit Facebook or other social network sites unconsciously when using computers to do my homework or to learn online.
144 .771 3. I often turn on the computer for work (e.g., writing paper, learning, or searching for information),
but you’ll always find me always doing something else (e.g., watching YouTube, checking Facebook, reading online news or playing games).
11. I turn on the computer to do my homework, but I still visit Facebook first.
12. I turn on computer to do my work, but I still visit other websites or use another computer programs (e.g., games, YouTube, news) instead.
Factor 2: Social Media Notifications
.047
.487 .435
.764
.628 .590
10. When I see or hear notifications from social media (e.g., Twitter, Instagram, Facebook), I can’t wait to check them.
.737 .194 13. When studying, I immediately notice the alerts from instant messaging software (such as
LINE, texting, or What’sApp).
16. When I hear notifications from cell phones or tablets, I check them immediately.
.823 .771
.115 .195 Note. Extraction Method: Principal Component Analysis.
The results therefore allow us to infer that "perceived attention discontinuity," which happens when students using media multitask by doing other things while completing tasks, and "social media notifications," in which students check notifications immediately from social media, proved to have significant correlations with the activeness of students’ Internet and social media use. It is important to note from this result that both "perceived attention discontinuity" and "checking notifications immediately" cause attention problems within students’ personal learning environments.
DISCUSSION
Among teenagers, multitasking behavior has become ubiquitous. This ubiquity of multitasking behavior cannot be divorced from their active usage of the internet and social media. Research about the rising prevalence of multitasking among students has generated worries about the negative effects of multitasking on student cognition, especially with regard to attention. Responding to these concerns, the author of this article hope to provide a real picture of adolescent online learning and media multitasking, internet and social media activeness, and the factors related to perceived attention problems in their personal learning environments by studying secondary-level students in Indonesia.
Current trends suggest media use among adolescents will continue to increase. It is expected that concurrent use of more than one type of media will grow rapidly as access to multiple media increases and begins at younger ages (Stavrinos et al., 2019). As Domoff et al. (2019) reported, "mobile media (e.g., smartphones and tablets) have proliferated in the digital age, providing youth and their parents with opportunities to use screen media at any time and any place.... Younger children are gaining access to mobile technology, with personal ownership of mobile media devices occurring earlier in childhood." (p. 2)
Barr et al. (2020) also concur that more and more young children are immersed in the digital world. Moreover, media multitasking is gradually becoming a significant problem among adolescents (Luo et al., 2018; Luo et al., 2019). As Purcell et al. (2012) reported, education providers are beginning to perceive detrimental effects of the Internet on children’s attention, with over 85% of teachers endorsing the statement that "today’s digital technologies are creating an easily distracted generation with a short attention span" (p. 2). Dealing with students’ use of digital devices and the resulting interruptions and distractions in educational classrooms has become part of the daily challenges for teaching faculty in higher education (Rabl et al., 2019) and also in secondary school classrooms (Kay et al., 2017).
As a result, digital devices and the internet appear to be an essential part of teenagers' daily lives. If not used with good and proper regulation, these tools may cause attention problems in the form of "perceived attention discontinuity" and social media notifications, which will ultimately impact learning outcomes. For example, as Glass and Kang (2019) show, long-term retention is impaired by divided attention (owing to laptop or cell phone use in class), as evidenced by poorer academic performance. Carter et al. (2016) also show an ultimate effect of divided attention in class on final exam performance.
Since attentional functions are known to allocate mental resources and prioritize the information to be processed by the brain, Mackie et al. (2013) suggest that "attention plays a critical role in cognitive control via the functions of alerting, orienting and executive control, and their interactions, to influence the priority of computations of cognitive processes for access to consciousness or response." Attempting to attend lectures in class or in a personal learning environment while engaging digital technologies for off-task activities can have a detrimental impact on learning (Wood et al., 2012). Attention allows students to focus on information in order to create memories and to avoid distractions so they can focus on and complete specific tasks (Cherry, 2021). It is important for students to be able to manage their own attention in learning since it is a cognitive aspect of their achievement (Purković & Kovačević, 2020). As a result, these findings lend support to previous research suggesting that attentional functions may be involved in the implementation of cognitive control required to reduce perceived attention problems.
CONCLUSION
This study is preliminary and should be interpreted in light of several limitations. First, the sample of this study only included adolescents from one city in Central Sulawesi and thus may not be representative of the whole population of adolescents in this province or in Indonesia. Therefore, caution is required when generalizing the findings. Future studies might improve the sampling method and recruit participants from different backgrounds and age groups (e.g., university students) because media multitasking and problems of attention are also prevalent among university students. Second, the instrument only assessed online learning motivation, attention experiences, and social media activeness through a self-reported questionnaire.
Future research should be conducted with data from objective measures such as observational studies or experimental designs to examine students' experiences using a variety of digital devices that may cause attention problems in different ways. Other questions, i.e., how many screens were opened and what kind of activities were engaged in, would be worth asking in future studies.
As a result of the research conducted, the author found that, with the prevalence of online learning, more and more middle and high school students are using the Internet for learning. Considering the perceived attention problems in the personal learning environment, it is concluded that adolescents who tend to check social media notifications immediately and at the same time are experiencing perceived attention discontinuity because of multitasking while learning online will tend to consider themselves active users of the internet and social media.
The significant correlation between perceived attention problems in online learning media multitasking (which is shown by perceived attention discontinuity and social media notifications) and self-reported activeness of the Internet and social media usage among students in this study supports the findings in the literature that media multitasking (or engaging digital technologies for off-task activities when attempting to attend to lectures) can have a detrimental impact on learning (Wood et al., 2012).
Nowadays, the boundary between learning and social entertainment in the online environment is blurred. The intrusion of internet-enabled electronic devices (laptop, tablet, and cell phone) allows students to divide their attention between the tasks of learning and unrelated activities, for example, texting, watching a movie, playing a game, and shopping. While studying, sharing information, or collaborating with classmates, students may engage in some or all of these activities (Wu 2017). This blurred reality has transformed the modern college lecture and secondary school class experiences into a divided attention task (Glass & Kang, 2019). Hence, the findings of this study may be useful for educators to assess and facilitate the prevention and intervention of students’ media-related attention problems and poor regulation strategy use within the personal learning environment, especially as related to the activeness of students’ internet and social media use.
Acknowledgements
The author gratefully acknowledge principals and teachers of three private schools in Palu, Central Sulawesi for their support to make this study happened. I would also like to express appreciation towards all the students who voluntary participated in the study.
Conflict of interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The study is supported by funding from LPPM, the Institute for Research and Community Services of STT SAAT, Malang, and the author gratefully acknowledges the support of the Research Fund. The study design, data collection, analysis, and interpretation of the data, the writing of the report, and the decision to submit the article for publication were all the sole responsibility of the author and were in no way influenced by the funding institution.
REFERENCES
Barr, R., Kirkorian, H., Radesky, J., Coyne, S., Nichols, D., Blanchfield, O., ... & Bellagamba, F. (2020). Beyond screen time: a synergistic approach to a more comprehensive assessment of family media exposure during early childhood. Frontiers in Psychology, 11, 1283. https://doi.org/ 10.3389/fpsyg.2020.01283
van der Schuur, W. A., Baumgartner, S. E., Sumter, S. R., & Valkenburg, P. M. (2018). Media multitasking and sleep problems:
A longitudinal study among adolescents. Computers in Human Behaviour, 81, 316-324.
Brasel, S. A., & Gips, J. (2011). Media multitasking behavior: Concurrent television and computer usage. Cyberpsychology, Behaviour, and Social Networking, 14(9), 527-534. https://doi.org/10.1089/cyber.2010.0350
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of cross-cultural psychology, 1(3), 185-216. https:
//doi.org/10.1177/135910457000100301
Brown, J. P. (2015). Complexities of digital technology use and the teaching and learning of function. Computers &
Education, 87, 112-122. https://doi.org/10.1016/j.compedu.2015.03.022
Cain, M. S., Leonard, J. A., Gabrieli, J. D., & Finn, A. S. (2016). Media multitasking in adolescence. Psychonomic bulletin &
review, 23(6), 1932-1941. https://doi.org/10.3758/s13423-016-1036-3
Carrier, L. M., Rosen, L. D., Cheever, N. A., & Lim, A. F. (2015). Causes, effects, and practicalities of everyday multitasking. Developmental Review, 35, 64-78. https://doi.org/10.1016/j.dr.2014.12.005
Carter, S. P., Greenberg, K., & Walker, M. S. (2017). The impact of computer usage on academic performance: Evidence from a randomized trial at the United States Military Academy. Economics of Education Review, 56, 118-132.
https://doi.org/10.1016/j.econedurev.2016.12.005.
Castañeda, L., & Selwyn, N. (2018). More than tools? Making sense of the ongoing digitizations of higher
education. International Journal of Educational Technology in Higher Education, 15(1), 1-10. https://doi.org/- 10.1186/s41239-018-0109-y
Cataldo, I., Lepri, B., Neoh, M. J. Y., & Esposito, G. (2021). Social media usage and development of psychiatric disorders in childhood and adolescence: A review. Frontiers in Psychiatry, 11, 1332. https://doi.org/10.3389/fpsyt.2020.508595 Chen, Q., & Yan, Z. (2016). Does multitasking with mobile phones affect learning? A review. Computers in Human
Behavior, 54, 34-42.
Cherry, K. (2021). How psychologists define attention. Verywell Mind. https://www.verywellmind.com/what-is-attention- 2795009
Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78 (1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98
Cudo, A., Kopiś, N., Francuz, P., Błachnio, A., Przepiórka, A., & Torój, M. (2019). The impact of Facebook use and Facebook intrusion on cognitive control: Effect in proactive and reactive control. Advances in Cognitive Psychology, 15(1), 63-74 . https://doi.org/10.5709/acp-0257-6
Dabbagh, N., & Kitsantas, A. (2012). Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and higher education, 15(1), 3-8.
Darweesh, T. S. (2018). The interaction between cognitive motivation level and working group size in web 3.0 technologies and its impact on developing self-vocation among university students. https://doi.org/10.21608/jedu.2018.73092
Domoff, S. E., Radesky, J. S., Harrison, K., Riley, H., Lumeng, J. C., & Miller, A. L. (2019). A naturalistic study of child and family screen media and mobile device use. Journal of Child and Family Studies, 28(2), 401-410.
Foehr, U. G. (2006). Media multitasking among American youth: Prevalence, predictors and pairings. Henry J. Kaiser Family Foundation.
Fidalgo-Blanco, Á., Sein-Echaluce, M. L., & García-Peñalvo, F. J. (2016). From massive access to cooperation: lessons learned and proven results of a hybrid xMOOC/cMOOC pedagogical approach to MOOCs. International Journal of Educational Technology in Higher Education, 13(1), 1-13. https://doi.org/10.1016/j.tele.2017.09.012
Glass, A. L., & Kang, M. (2019). Dividing attention in the classroom reduces exam performance. Educational Psychology, 39(3), 395-408.
Gebremeskel, G. B., Kebede, A. A., & Chai, Y. (2016). The paradigm role of ICT for behavioral and educational psychology:
The case of developing countries. International Journal of Information and Education Technology, 6(4), 301-307.
https://doi.org/10.7763/IJIET.2016.v6.704
Hazelrigg, N. (2019). Survey: Nearly Half of Students Distracted by Technology. Inside Higher Ed. Retrieved: February 17, 2021.
Henderson, M., Selwyn, N., & Aston, R. (2017). What works and why? Student perceptions of ‘useful’digital technology in university teaching and learning. Studies in Higher Education, 42(8), 1567-1579. https://doi.org/10.1080/03075079.
2015.1007946
Johnson, M. W., & Sherlock, D. (2014). Beyond the Personal Learning Environment: attachment and control in the classroom of the future. Interactive Learning Environments, 22(2), 146-164. https://doi.org/10.1080/10494820.2012.745434 Jones, P. S., Lee, J. W., Phillips, L. R., Zhang, X. E., & Jaceldo, K. B. (2001). An adaptation of Brislin’s translation model for
cross-cultural research. Nursing research, 50(5), 300-304. https://doi.org/10.1097/00006199-200109000-00008
Kay, R., Benzimra, D., & Li, J. (2017). Exploring factors that influence technology-based distractions in bring your own device classrooms. Journal of Educational Computing Research, 55(7), 974-995.
Kimbrell, J. (2013). The impacts of web 2.0, web 3.0, and web 4.0 technologies used in distance education. East Carolina University.
Kop, R., & Fournier, H. (2011). New dimensions to self-directed learning in an open networked learning environment. International Journal of Self-Directed Learning, 7(2), 1-18.
Kushniryk, A., & Levine, K. J. (2012). Impact of multitasking on listening effectiveness in the learning environment. The Canadian Journal for the Scholarship of Teaching and Learning, 3(2). https://dx.doi.org/10.5206/cjsotl-rcacea.2012.2.7 Lau, W. W. (2017). Effects of social media usage and social media multitasking on the academic performance of university
students. Computers in human behavior, 68, 286-291. https://doi.org/10.1016/j.chb.2016.11.043
Lee, Y. H., & Wu, J. Y. (2012). The effect of individual differences in the inner and outer states of ICT on engagement in online reading activities and PISA 2009 reading literacy: Exploring the relationship between the old and new reading literacy. Learning and Individual Differences, 22(3), 336-342. https://doi.org/10.1016/j.lindif.2012.01.007
Lepp, A., Barkley, J. E., & Karpinski, A. C. (2015). The relationship between cell phone use and academic performance in a sample of US college students. Sage Open, 5(1), 2158244015573169.
le Roux, D. B., & Parry, D. A. (2017). In-lecture media use and academic performance: Does subject area matter?. Computers in Human Behavior, 77, 86-94. https://doi.org/10.1016/j.chb.2017.08.030
le Roux, D. B., & Parry, D. A. (2019). Investigating differences in the attention distribution strategies of high and low media multitaskers through a two-dimensional game. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 13(3).
Libedinsky, C. D., & Fernandez, P. F. (2019). Focus: Attention Science: Graded Memory: A Cognitive Category to Replace Spatial Sustained Attention and Working Memory. The Yale journal of biology and medicine, 92(1), 121.
Schneider, W., Waldfogel, J., & Brooks-Gunn, J. (2017). The Great Recession and risk for child abuse and neglect. Children and youth services review, 72, 71-81. https://doi.10.1016/j.childyouth. 2018.10.044
Luo, J., Yeung, P. S., & Li, H. (2020). Relationship between media multitasking and self-esteem among Chinese adolescents:
Mediating roles of peer influence and family functioning. Journal of Child and Family Studies, 29(5), 1391-1401. https://
doi. https://doi.org/10.1007/s10826-019-01613-3
Mackie, M. A., Van Dam, N. T., & Fan, J. (2013). Cognitive control and attentional functions. Brain and cognition, 82(3), 301- 312. https://doi.org/10.1016/j.bandc.2013.05.004
Madianou, M. (2020). A Second-Order Disaster? Digital Technologies During the COVID-19 Pandemic. Social media+
society, 6(3), 2056305120948168. https://doi.org/10.1177/2056305120948168 Mangal, S. K. (2002). Advanced educational psychology. PHI Learning Pvt. Ltd.
Martín-Perpiñá, M. D. L. M., Viñas Poch, F., & Malo Cerrato, S. (2019). Media multitasking impact in homework, executive function and academic performance in Spanish adolescents. Psicothema (31), 81-87.
https://doi.org/10.7334/psicothema2018.178
Matzat, U., & Vrieling, E. M. (2016). Self-regulated learning and social media–a ‘natural alliance’? Evidence on students’ self- regulation of learning, social media use, and student–teacher relationship. Learning, Media and Technology, 41(1), 73-99.
https://doi.org/10.1080/17439884.2015.1064953.
May, K. E., & Elder, A. D. (2018). Efficient, helpful, or distracting? A literature review of media multitasking in relation to academic performance. International Journal of Educational Technology in Higher Education, 15(1), 1-17.https://doi.
org/10. 1186/s41239-018-0096-z
Moisala, M., Salmela, V., Hietajärvi, L., Salo, E., Carlson, S., Salonen, O., ... & Alho, K. (2016). Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults. NeuroImage, 134, 113- 121. https://doi.org/10.1016/j.neuroimage.2016.04.011
OECD (2015). Students, Computers and Learning: Making the Connection. PISA, OECD Publishing, Paris. https://doi.org/10.- 1787/9789264239555-en.
OECD. (2016). Innovating Education and Educating for Innovation: The Power of Digital Technologies and Skills, Educational Research and Innovation, OECD Publishing, Paris. http://dx.doi.org/10.1787/9789264265097-en
Park, O. N. (2018). A Study on the Changes of Libraries and Directions of Librarian Education in the era of the Fourth Industrial Revolution. Journal of the Korean Society for Library and Information Science, 52(1), 285-311.
Parry, D. A., & le Roux, D. B. (2019). Media multitasking and cognitive control: A systematic review of interventions. Computers in Human Behavior, 92, 316-327. https://doi.org/10.1016/j.chb.2018.11.031
Parry, D. A., le Roux, D. B., & Cornelissen, L. A. (2020). Managing in-lecture media use: the feasibility and value of a split- class policy. Journal of Computing in Higher Education, 32(2), 261-281. https://doi.org/10.1007/s12528-019-09232-z Patterson, M. C. (2017). A naturalistic investigation of media multitasking while studying and the effects on exam
performance. Teaching of Psychology, 44(1), 51-57. https://doi.org/10.1177/0098628316677913
Perna, L. W., Ruby, A., Boruch, R. F., Wang, N., Scull, J., Ahmad, S., & Evans, C. (2014). Moving through MOOCs:
Understanding the progression of users in massive open online courses. Educational Researcher, 43(9), 421-432.
https://doi.org/10.3102/0013189X14562423
Petersen, S. E., & Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annual review of neuroscience, 35, 73-89. https://doi.org/10.1146/annurev-neuro-062111-150525
Purcell, K., Rainie, L., Heaps, A., Buchanan, J., Friedrich, L., Jacklin, A., ... & Zickuhr, K. (2012). How teens do research in the digital world. Pew Internet & American Life Project.
Damir, P., & Stjepan, K. (2020). Teachers’perception of the influence of the teaching context on cognitive achievements in general technology education. International Journal of Cognitive Research in Science, Engineering and Education, 8(S).
Rabl, M., Pattermann, J., Schlögl, S., & Canham, N. (2019, April). The use of digital devices in the university classroom:
exploring and comparing students’ perceptions and practices. In International Workshop on Learning Technology for Education in Cloud (pp. 103-113). Springer, Cham. https://doi.org/10.1007/978-3-030-20798-4_10
Redick, T. S. (2016). On the relation of working memory and multitasking: Memory span and synthetic work
performance. Journal of Applied Research in Memory and Cognition, 5(4), 401-409. https://doi.org/10.1016/j.jarmac.- 2016.05.003
Reisberg, D., & McLean, J. (1985). Meta-attention: Do we know when we are being distracted?. The Journal of general psychology, 112(3), 291-306. https://doi.org/10.1080/00221309.1985.9711015
Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948-958.
Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Computers & Education, 62, 24-31. https://doi.org/10.1016/j.compedu.2012.10.003
Seemiller, C., & Grace, M. (2016). Generation Z Goes to College, San Francisco: Jossey-Bass.
Selwyn, N., Hillman, T., Eynon, R., Ferreira, G., Knox, J., Macgilchrist, F., & Sancho-Gil, J. M. (2020). What’s next for Ed- Tech? Critical hopes and concerns for the 2020s. Learning, Media and Technology, 45(1), 1-6. https://doi.org/10.1080 /17439884.2020.1694945
Serdyukov, P. (2017). Innovation in education: what works, what doesn’t, and what to do about it?. Journal of Research in Innovative Teaching & Learning. 10 (1), 4-33. https://doi.org/10.1108/JRIT-10-2016-0007
Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.
Stavrinos, D., McManus, B., Underhill, A. T., & Lechtreck, M. T. (2019). Impact of adolescent media multitasking on cognition and driving safety. Human behavior and emerging technologies, 1(2), 161-168. https://doi.org/10.1002/hbe2.143
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in science education, 48(6), 1273-1296. https://doi.org/10.1007/s11165-016-9602-2
Talebian, S., Mohammadi, H. M., & Rezvanfar, A. (2014). Information and communication technology (ICT) in higher education: advantages, disadvantages, conveniences and limitations of applying e-learning to agricultural students in Iran. Procedia-Social and Behavioral Sciences, 152, 300-305. https://doi.org/10.1016/j.sbspro.2014.09.199
Thornton, B., Faires, A., Robbins, M., & Rollins, E. (2014). The mere presence of a cell phone may be distracting. Social Psychology. https://doi.org/10.1027/1864-9335/a000216
Kompen, R. T., Edirisingha, P., Canaleta, X., Alsina, M., & Monguet, J. M. (2019). Personal learning Environments based on Web 2.0 services in higher education. Telematics and informatics, 38, 194-206.
Twenge, J. M., Krizan, Z., & Hisler, G. (2017). Decreases in self-reported sleep duration among US adolescents 2009–2015 and association with new media screen time. Sleep medicine, 39, 47-53.
Uncapher, M. R., Thieu, M. K., & Wagner, A. D. (2016). Media multitasking and memory: Differences in working memory and long-term memory. Psychonomic bulletin & review, 23(2), 483-490. https://doi.org/10.3758/s13423-015-0907-3
UNESCO Institute for Statistics. (2014). Information and Communication Technology (ICT) in Education in Asia: A comparative analysis of ICT integration and e-readiness in schools across Asia. Information paper No.22. 2014, http://dx.doi.org/10.15220/978-92-9189-148-1-en
Van Der Schuur, W. A., Baumgartner, S. E., Sumter, S. R., & Valkenburg, P. M. (2015). The consequences of media
multitasking for youth: A review. Computers in Human Behavior, 53, 204-215. https://doi.org/10.1016/j.chb.201506.035 Wammes, J. D., Ralph, B. C., Mills, C., Bosch, N., Duncan, T. L., & Smilek, D. (2019). Disengagement during lectures: Media
multitasking and mind wandering in university classrooms. Computers & Education, 132, 76-89.
Whitford, E. (2018). The Myth of Multitasking. Inside Higher Ed.
Wilmer, H. H., Sherman, L. E., & Chein, J. M. (2017). Smartphones and cognition: A review of research exploring the links between mobile technology habits and cognitive functioning. Frontiers in psychology, 8, 605. https://doi.org/10.3389/f psyg.2017.00605
Wiradhany, W., & Nieuwenstein, M. R. (2017). Cognitive control in media multitaskers: Two replication studies and a meta- analysis. Attention, Perception, & Psychophysics, 79(8), 2620-2641. http://doi.org/10.3758/s13414-017-1408-4
Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi- tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. https://doi.org/10.
1016/j.compedu.2011.08.029
World Health Organization. (2020). Coronavirus disease (COVID-19) pandemic. WHO Health Topic, Europe, 2020. Available at https://www.euro.who.int/en/health-topics/health-emergencies/ coronavirus-covid-19/novel-coronavirus-2019-ncov Wu, J. Y. (2015). University students' Motivated Attention and use of regulation strategies on social media. Computers &
Education, 89, 75-90. https://doi.org:10.1016/j.compedu.2015.08.016
Wu, J. Y. (2017). The indirect relationship of media multitasking self-efficacy on learning performance within the personal learning environment: Implications from the mechanism of perceived attention problems and self-regulation
strategies. Computers & Education, 106, 56-72. https://doi.org/10.1016/j.compedu.2016.10.010
Wu, J. Y., & Peng, Y. C. (2017). The modality effect on reading literacy: Perspectives from students’ online reading habits, cognitive and metacognitive strategies, and web navigation skills across regions. Interactive Learning
Environments, 25(7), 859-876. https://doi.org/10.1080/10494820.2016.1224251
Zhu, Y., Au, W., & Yates, G. (2016). University students' self-control and self-regulated learning in a blended course. The Internet and higher education, 30, 54-62. https://doi.org/10.1016/j.iheduc.2016.04.001
