Effects of WhatsApp 's Use on Working Memory Performance Among Youth

Abstract

The current study seeks to shed light on the usage habits of the WhatsApp application among teenagers, exploring the effect of mobile instant messaging distractions on pupils' working memory performance. Research was conducted in Israel during 2016 school year. The study sample was divided into two groups randomly—a control group and an experimental group. Researchers used six questionnaires to gather personal details, execution assessment questionnaire, and Working Memory Index from the Wechsler Intelligence Scale for Children-IV. The main findings show that WhatsApp's distractions, transmitted via smartphones, decrease pupils' performance of working memory. In addition, students are aware of the difficulty WhatsApp causes while performing a learning task and of the decrease in learning effectiveness. The present study displays a unique experiment that explored the direct effect of the distractions stemming from a new technological platform—WhatsApp—on young pupils' working memory. Further, it suggests that instructors and teachers should be aware of the potential damage of multitasking caused by smartphone use during learning tasks.

Keywords

working memory cognitive load mobile technology exploratory study

Introduction

Interpersonal communication through cellular communications, and especially through smartphones, has become an integral part of our daily lives (Cellular News, 2013; Clabaugh, 2013). People use smartphones for voice calls, text messages, games, navigation, and social networking (Salehan & Negahban, 2013). One of the most common applications for text messaging is WhatsApp. WhatsApp contains several special features that add to its quick adaption. These include the ability to set up groups and communicate between members of the group, an indicator of the availability of members who have the application installed on their phone, and a notification of the status (i.e., sending and reading) of a single message (Church & de Oliviera, 2013).

The current reality of availability at any time and place requires coping with more than one task at the same time. Multitasking is defined as the ability to maneuver between tasks or as the ability to do more than one task sequentially (Ajao, 2012). Recent changes in our lives, caused by technology, have contributed to a way of life based on multitasking and constant limited attention (Friedman, 2006). Young people believe they have the ability to perform different tasks that require attention in parallel, such as text-messaging correspondence while studying or talking on the phone while driving (Rogers & Monsell, 1995). Users of instant messaging (IM) applications believe they are capable of managing such communication (Madell & Muncer, 2007). However, these users do not control the place or the time they receive the messages. Finley, Tullis, and Benjamin (2014) noted that people are not aware of the price they pay when their attention is divided.

Bowman, Levine, Waite, and Gendron (2010) proposed that the functioning of students in educational tasks may be impaired when they are simultaneously engaged in IM programs. One of the cognitive systems is the working memory system. This system has several components: a phonological loop, visual-spatial sketchpad, an episodic buffer, and a central processor (Baddeley, 2003). The functioning of the working memory system affects the overall cognitive ability of a person. Students use working memory in a wide variety of tasks, including remembering names, numbers, complex sentences, and work instructions. Damage to these functions may cause considerable difficulties in the areas of learning and work (Baddeley, 1986, 2003). To date, studies have not directly examined the effect of text messages using the WhatsApp platform during learning task fulfillment on the functioning of the working memory system. The present study will examine the direct effect of WhatsApp distractions on pupils' working memory.

Literature Review

Cognitive Load

The cognitive load theory portrays instructional implications of characteristics of human cognitive architecture. The main elements of this design are long-term memory (a collection of information patterns in the form of organized knowledge structures called schemas) and working memory (a conscious information processor and a mechanism for limiting the scope of random changes to the store; Kalyuga, 2011). The cognitive load theory focuses on the learning of complex cognitive tasks, where learners are overwhelmed by information and interactions that have to be processed before meaning learning can take place (Paas, Renkl, & Sweller, 2003; Sweller, 1988, 1999). The theory proposes that the best learning occurs under conditions that are adjusted to human cognitive architecture. Kalyuga (2011) suggests that a productive instructional design could be achieved by reducing learner cognitive activities that are not important for learning and create unnecessary load that is called extraneous cognitive load.

Working Memory

Atkinson and Shiffrin (1968) suggested that information is processed and stored in three stages: sensory memory, working memory, and long-term memory. The current study focuses on the second element of the information processing model: the working memory.

Working memory is the system that influences the capacity to store and manipulate information for short period of time (Alloway, Gathercole, Kirkwood, & Elliott, 2009). Various models of working memory distinguish between short-term memory and working memory (Baddeley, 2000; Baddeley & Hitch, 1974; Baddeley & Logie, 1999). Short-term memory stores information temporarily, while working memory is liable not only for storage but also for processing information.

Researchers (Baddeley & Hitch, 1974) have described their model using the mechanism of working memory as a mechanism that includes two systems. The first system consists of two components: the phonological loop and the visual-spatial sketchpad. The latter contains the central executive, which is the center of control and supervision of two storage elements of the first system (Baddeley, 1986, 1990).

The phonological loop is a storage component associated with memory portions of voices, sounds, and language for a few seconds, before they fade. It also contains an element of the return process. In addition, the phonological loop is involved in language acquisition and serves as a powerful predictor of language acquisition capabilities (Baddeley, 2003).

The visual-spatial sketchpad is a storage component that enables storing and manipulating information. It can convey verbal or visual information to a spatial channel, thus remembering it better (Baddeley, 2003). The central processor system monitors both of these components and performs essential control and supervision processes on daily behavior. It works mainly when we are in new and less familiar situations (Baddeley, 2003). Baddeley (2000) added another component to the model of working memory mechanism: a temporary storage space (episodic buffer). This component can be viewed as a separate subsystem controlled by the central processor system. It is a system with limited capacity but integrates several sources of information to create interactive scenes that assist retrieval processes (Baddeley, 2003).

Cowan and Alloway (1997) assert that there are individual differences in working memory capacity that significantly affect children's acquiring knowledge and new skills. Students often need to rely on working memory in class to perform a variety of tasks. Working memory problems may lead to a failure not only in fulfilling complex assignments that combine storage, information processing, and tracking task progress (Gathercole & Alloway, 2008; Gathercole, Lamont, & Alloway, 2006) but also in performing simple tasks such as work instructions in class (Engle, Carullo, & Collins, 1991).

Working memory is an essential factor for executive functions—a concept that addresses cognitive processes. It enables behavior targeted at achieving goals through strategic planning and cognitive flexibility. Executive functions refer to different processes such as initiative, planning, monitoring, switching between tasks, regulation, filtering information, and working memory. These allow the individual to keep information and use it in his or her work (Baddeley & Hitch, 1974; Johnston, Mash, Miller, & Ninowski, 2012; Jurado & Rosselli, 2007; Norman & Shallice, 1986). Baddeley (1986, 2003) indicated that injuring verbal working memory (remembering instructions, names, numbers, complex sentences, and relevant information for troubleshooting) can cause considerable difficulties in various areas such as functional learning, work, play, and social skills.

Smartphones

Mobile communication has become an integral part of life for many people. The most common and widespread cellular communications are done through smartphones (Cellular News, 2013; Clabaugh, 2013). People use smartphones for various forms of communication, such as verbal, visual, and textual communication. These forms of communication play a central role in fulfilling their daily social needs. Ling (2008) claimed that cellular communication enables direct communication and experience more than any other media channel. A market survey conducted in 2016 found that the penetration rate of smartphones in the United States has reached 79.1% (comScore Reports, 2016). It seems that smartphones are no longer considered a new high technology but have become a common device for many people. Researchers found that many smartphone users check the phone immediately in the morning and also a moment before they go to bed (Oulasvirta, Rattenbury, Ma, & Raita, 2012). It was also found that respondents checked their smartphones 34 times a day for no real reason. A recent study found that smartphone users checked their phones 46 times a day, and young people (aged 18–24), checked their smartphones 76 times a day (Eadicicco, 2015).

Text Messages

The invention of SMS technology more than 20 years ago changed the way people communicate with each other. This technology has significantly modified interpersonal communication by implementing text-based communication over face-to-face or voice communications (Harrison & Gilmore, 2012).

Text message delivery is simple and engages typing and sending a short electronic message between two or more users via mobile phones or using other mobile devices. Short-term messaging is a very popular channel, and the amount of text messages sent worldwide has increased by 7,700% over the past decade (Burke, 2016).

According to a survey of Americans by the Pew Research Center (2016), 72% of U.S. adults own a smartphone, compared with 64% in 2014. Sixty-seven percent of telephone owners check whether a message was received even without a ring or vibration. Forty-four percent of phone owners sleep when the phone is near their bed because they want to make sure they did not miss a call or text message or other update during the night. Sending and receiving messages continues to be one of the most common mobile phone activities.

A number of studies have suggested that students use text messages to update their daily schedules in real time and conduct discussions that are not always suitable for voice conversation (Grinter, Palen, & Eldridge, 2006). In addition, students noted that they sent text messages during work, during showering, and even during religious activity (Harrison & Gilmore, 2012). Wei and Wang (2010) proposed that students who are used to sending text messages often are more likely to send them even during class time.

Various studies suggested that the use of nonlearning technology inhibits learning processes. Researchers reported a decrease in homework completion rates among text message senders (Junco & Cotten, 2011). Wood et al. (2012) found that the use of social technology, such as instant text messages, leads to a decrease in memory. Windham (2007) presented a negative correlation between average scores and instant text messages.

Lately, a new wave of mobile instant messaging applications has gained popularity. These applications include Viber, WeChat, and WhatsApp. They enable smartphone users to send real-time, cost-free text messages.

WhatsApp is a main mobile instant messaging application that enables users to send and receive real-time, diverse information to individuals and groups at no charge. As of February 2016, the number of users in the application has reached one billion, meaning that one in seven people worldwide actively uses this application (WhatsApp blog, 2016).

Church and de Oliveira (2013) suggested that most users of WhatsApp were exposed to the application from their friends. They noted that social impact and pressure played a main role in the motivation to purchase and adopt WhatsApp.

One of the special characteristics of WhatsApp is the ability to create and interact with only groups of friends. In these groups, the person who opened the group is also its manager and can add and remove participants without the need for approval by the rest of the group. In addition, all participants in the group enjoy equal rights.

Using WhatsApp creates an anticipation for attention and listening. The recipient of the message is expected to read it within minutes. WhatsApp suggests indicators for user availability. However, this creates a problematic expectation for both sender and receiver. On one hand, the sender's message will not always be processed in the expected time. On the other, the person receiving the message feels pressure from having to cope with a large number of messages per day (Shirazi et al., 2014). WhatsApp has an indicator showing the sender when the message was read on the receiving end. This information creates a strong expectation in the sender, and he or she may feel that if the recipient of the message were available, it means that he or she saw the message, but chose to ignore it (Church & de Oliveira, 2013). In view of the situations described earlier, we have chosen to investigate the effect of using WhatsApp on working memory function.

Multitasking

Multitasking can be defined as the ability to maneuver between two or more tasks simultaneously. It can also be defined as the ability to do more than one task in a sequence (Ajao, 2012). Multitasking can be done consciously or unconsciously.

The constant changes in technology contribute to a way of life based on the multitasking and continuous partial attention. Today, many young people believe that they are able to perform different tasks that require attention at the same time. For example, text message while doing homework or talking on the phone while driving. Various studies have shown the difficulty in performing several tasks in parallel (Rogers & Monsell, 1995). Further, the impact of such activity on the length of time to complete a single task grew as a result of switching from one task to another (Rubinstein, Meyer, & Evans, 2001).

Other researchers who studied the effects of a phone call on those in a driving simulator equipped with hands-free technology found that the attention of the drivers declined when they were talking on the phone while driving (Strayer & Drews, 2007; Strayer, Drews, & Johnston, 2003; Strayer & Johnston, 2001). The ability to operate in a multitasking technology has caused IM to become popular in recent years among young people (Quan-Haase, 2007; Roberts, Foehr, & Riidaot, 2005).

The question that arises is the extent to which people are aware of the price they pay when they multitask. Ophir, Nass, and Wagner (2009) examined the use of various media sources in parallel (media multitasking). Results showed that subjects who reported they could perform several tasks in parallel had the lowest ability to filter information that was not relevant for the task. In addition, those people tended to overestimate their ability to multitask (Sanbonmatsu, Strayer, Medeiros-Ward, & Watson, 2013).

So far, research on WhatsApp has not yet investigated the harm caused by intensive use of WhatsApp applications on pupils' working memory, which is an important component of cognitive information processing and executive functions. The current study seeks to shed light on the usage habits of the WhatsApp application among teenagers. It explored the effect of WhatsApp distractions on pupils' working memory performance. The results of this study may shed light on its effect on teenagers' cognitive functioning and work habits. This information may assist educational managers and policy makers in planning and implementing novel learning environments.

Research Question and Hypotheses

RQ: What are the effects of WhatsApp distractions on pupils' working memory performance? Following are the research hypotheses:

H1: WhatsApp distractions will decrease the results of working memory tests and will present significant differences between the control group and study group.

H2: Participants who will experience WhatsApp distractions will report that the working memory tests are more difficult and less effective.

H3: A negative correlation will be found between the feeling of difficulty in subworking memory tests and participants' achievements. A positive correlation will be found between participants' feeling of efficacy at subworking memory tests and achievements.

Methodology

Data Collection

Researchers conducted an experimental study based on a convenience sample. The research was conducted during the 2016 school year and encompassed 64 pupils. Of the participants, 24 (37.5%) were male and 40 (62.5%) were female. Their ages were from 12 to 17. Participants were divided into an experimental group and a control group. There were 12 boys and 20 girls in each group. The following were the criteria for choosing participants:

Hebrew is their mother tongue.

They do not have cognitive or mental diagnoses, such as attention deficit hyperactivity disorders or mental disorders.

They do not have physical disabilities, such as hearing or vision impairments.

Measures

Research tools (see online Appendix 1).

The Personal Information Questionnaire (completed by the pupils' parents): This questionnaire included seven questions about the health and functioning of their child, as well as demographic details.

The Execution Assessment Questionnaire of working memory subtest comprises three statements rated on a 6-point Likert scale (1 = Not difficult at all, 6 = very difficult). In the first statement, respondents were asked to rank the difficulty level of working memory subtest. The second statement asked them to rank their efficiency in performing working memory subtest, and in the third, to assess the exact number of correct answers of this subtest.

Working Memory Index from the Wechsler Intelligence Scale for Children-IV (examples are given in online Appendix 1): The Wechsler test is given to children from age 6 until 17. The exam time varies from 65 to 80 minutes, depending on the child's level of intelligence and on several other secondary tests. The standard scores range from 40 to 160. The test was developed by David Wechsler and first published in 1949; since that time, several versions have been released. This study used the last version (IV) that was released in 2003 and includes 10 core tests and other tests that address verbal understanding, thinking and perception, working memory, and processing speed (Flanagan & Kaufman, 2009; Kaufman, Flanagan, Alfonso, & Mascolo, 2006). This version has a very high reliability in all subtests and in all age groups (α = .7 to .9). This study will use the subworking memory test that includes three parts:

Digit recall: Its Cronbach's alpha is .84 and has two parts (remembering forward and backward). Each part contains eight items, and each item has two sequences of digits of identical length. Respondents are asked to recall a string of two to nine digits (remembering forward and backward). Scoring: Each item will be given a score of 0 to 2, depending on its success. Each part calculated a raw score (maximum 16) and presented the maximum number of digits that the examinee could remember. The two subtest parts calculated a raw score (maximum 32). The test ended when the examinee received a grade of 0 in both attempts at the same item in each part separately.

Series and letters: Its Cronbach's alpha is .76 and contained two items set as an example, followed by 10 items, containing three sections each. In each section, the tester read a series of letters and numbers (from two characters in the series up to eight characters in the series). The examinee had to repeat the numbers in an ascending order and then to repeat the letters in alphabetically order. Examinees' answers were recorded in an appropriate column. Each item was given a score of 0 to 3 according to the number of correct answers. A raw score (maximum 30) was calculated. The test was terminated when the examinee received a grade 0 in all three items.

Math Cronbach's alpha is .92. The questionnaire contains 34 items of math word problems. Item 12 is the first item in this subtest (total 23 items) for ages 10 to 17. The tester reads a verbal question, and the examinee has to give a numerical answer within 30 seconds. Examinees' answers were written in the appropriate column. Each item was given a score of 0 to 1. A raw score (maximum 23) was calculated. The test was terminated when the examinee received a grade of 0 in four consecutive questions. Raw scores were converted to standardized scores according to age charts.

Procedure

After receiving approval from the Ethics Committee, researchers identified youth who wanted to participate in the study by posting a post in Facebook and WhatsApp. Teenagers who were interested contacted the researchers via e-mail, telephone, or in person. Researchers presented the study to the teenagers and their parents, and they signed consent forms. In addition, parents completed a personal information questionnaire, and the questionnaire was returned to the researcher via email or manually. The personal information questionnaire was used for choosing the appropriate candidates. The study sample was divided into two groups randomly—a control group and an experimental group. Researchers distributed participants according to age and gender to balance the two groups. Each group contained 32 pupils. Each participant met with the researcher in a quiet room for 45 minutes. The study included a neuropsychological, rehabilitative psychologist from a well-known university in Israel.

Experimental Group

Participants were asked to perform three subsets of working memory index in Wechsler-IV and, at the same time, distractions caused by WhatsApp were sent to them. Participants received scheduled messages after the tester read each question or section. Messages were collected from a list of messages used by teenagers in WhatsApp (see online Appendix 1). The maximum number of WhatsApp messages that a participant could receive in the test group was 80.

Each subject was given the following instruction:

In front of you, there are three subtests, which test memory while working. You are requested to concentrate in order to achieve the best possible result. During the test, you will accept personal WhatsApp messages, Your phone must be in front of you during the test, You are asked to read the incoming message. Remember, the goal is to succeed in the test!

During the test, participants were distracted by WhatsApp messages that came from a smartphone placed next to the participant. The distractions appeared between reading the sequence of numbers/letters or while reading the math question. The order of the tests was similar for both groups (experimental and control): digit recall test, series letters test, and a math test.

Control Group

Each subject was given the instruction: “In front of you, there are three subtests, which test memory while working. You are requested to concentrate in order to achieve the best possible result. Remember, the goal is to succeed in the test!” Three subtests were transferred in the same way that was carried out in experimental group (i.e., without distractions). At the end of each subtest, participants in both groups completed the performance assessment questionnaire.

Results

Descriptive Statistics

Table 1 presents participants' mobile use at school and at home.

Table 1.

Participants' Mobile Use at School and at Home.

Statements	Frequency (as percentage)
Study with a tablet in school
Study	16 (25%)
Not study	63 (73%)
A tendency to play or mess with smartphones during class
Tend to	28 (44%)
Not tend to	35 (54%)
Mobile mode while doing homework
Regular	24 (37%)
Vibration	33 (51%)
Turned off	4 (6%)
Reading messages while doing homework
Read	54 (84%)
Don't read	6 (10%)
Time of reading the message
Immediately upon receipt	38 (59%)
After completing currently task	22 (35%)

Table 1 shows that the majority (73%) of students did not learn with a tablet at school and did not tend to use a mobile during class (54%). However, at home, about half (51%) of the pupils put the mobile on vibrate, and the rest put it in normal mode (37%) or turn it off (6%). Most pupils read the messages they received while doing homework (84%), or on the spot (59%).

To examine whether there are differences between males and females, a series of t tests were conducted. Table 2 presents the comparison between girls and boys on the research variables.

Table 2.

A Comparison Between Girls and Boys on the Research Variables.

	Girls N = 40	Boys N = 24
Measures	M (SD)	M (SD)	t ₍₆₂₎	d
Digit recall test	12.21 (3.24)	12.03 (2.98)	0.23	0.06
Series letters test	9.88 (1.62)	9.90 (2.07)	0.05	0.04
Math test	11.17 (3.46)	9.68 (3.25)	1.73	0.44
Difficulty average	3.92 (0.85)	4.29 (0.78)	1.76	0.44
Efficiency average	3.36 (0.89)	3.61 (0.81)	1.14	0.29

*p < .05.

Table 2 shows that there were no significant differences between males and females. To examine the correlation between the dependent variables—digit recall, series letters, and math test—researchers performed Pearson correlations that are presented in Table 3.

Table 3.

Pearson Correlations Between the Dependent Variables (N = 64).

Measure	Digit recall	Series letters	Math test
Digit recall		0.413**	0.429**
Series letters	0.413**		0.522***
Math test	0.429**	0.522***

**p < .01. ***p < .001.

Hypotheses

To examine the first hypothesis, which proposed that distractions from WhatsApp will decrease working memory subtests performance, researchers performed a one-way multivariate analysis of variance (MANOVA). Significant differences were found concerning the three subtests (digit recall, series letters, and the math test) with F (3, 60) = 14.12, p < .001. The MANOVA revealed a significant difference, F (1, 62) = 8.09, p < .01, for the digit recall subtest. It seems that respondents who belonged to the experimental group had lower scores (M = 11.06, SD = 3.05) than those who belonged to the control group (M = 13.3, SD = 2.78). The MANOVA revealed another significant difference, F (1, 62) = 42.36, p < .001, concerning the series letters memory subtest. Respondents who belonged to the experimental group had lower scores (M = 8.69, SD = 1.63) than those who belonged to the control group (M = 11.09, SD = 1.3). A third difference was found regarding the math test, F (1,62) = 10.27, p < .01. Again, respondents who belonged to the experimental group had lower scores (M = 8.97, SD = 2.94) than those who belonged to the control group (M = 11.5, SD = 3.36). Table 4 presents the differences between the groups.

Table 4.

MANOVA Analysis Between the Groups.

	Test group (with distraction)	Control group (without distraction)
Variable	M (SD)	M (SD)	F (1, 62)	η ²
Digit recall test	11.06 (3.05)	13.3 (2.78)	8.09**	0.12
Series letters test	8.69 (1.63)	11.09 (1.3)	42.36***	0.41
Math test	8.97 (2.94)	11.5 (3.36)	10.27**	0.14

Note. MANOVA = multivariate analysis of variance.

**p < .01. ***p < .001.

To examine the second hypothesis that assumed that participants in the experimental group will report that the working memory tests are more difficult and their work is less effective, researchers performed a t test. Significant differences were revealed in both measures. Respondents in the experimental group felt that the working memory tests were more difficult (t₍₆₂₎ = 2.09, p < .05; M = 4.27, SD = 0.88) than respondents who belonged to the control group (M = 3.84, SD = 0.75). They also reported that the tests were less effective (t (62) = 1.98, p < .05; M = 3.36, SD = 0.83) than respondents who belonged to the control group (M = 3.76, SD = 0.88). Table 5 presents the differences between the groups.

Table 5.

Participants' Feelings of Difficulty and Efficiency.

Measure	With distraction N = 32	Without distraction N = 32	t ₍₆₂₎	d
Measure	M (SD)	M (SD)	t ₍₆₂₎	d
The average level of difficulty	4.27 (0.88)	3.84 (0.75)	2.09*	0.52
The average level of efficiency	3.76 (0.88)	3.36 (0.83)	1.98*	0.47

*p < .05.

To explore if there were correlations between the feelings of difficulty and of efficacy and working memory subtest achievements, researchers performed Pearson correlations. These are given in Table 6.

Table 6.

Correlations Between Feelings of Difficulty and Efficiency and Working Memory Subtest Achievements (N = 64).

Measure	Digit recall	Series letters	Math test
Difficulty	−0.52***	−0.13	−0.37**
Efficiency	0.29*	−0.04	0.56***

*p < .05. **p < .01. ***p < .05.

Table 6 presents a significant negative correlation between the level of difficulty and respondents' digital recall working memory test, as well as a significant positive correlation between the level of efficiency and respondents' digital recall working memory test. Therefore, the higher the level of respondents' sense of difficulty, the lower their scores in the digital recall working memory test. Also, the higher respondents' sense of efficiency, the higher their scores in the digital recall working memory test. A similar pattern was found with the math working memory subtest. A significant negative correlation was seen between the level of difficulty and respondents' math working memory test. A significant positive correlation between the level of efficiency and respondents' math working memory test was also noted. Hence, the higher the level of respondents' sense of difficulty, the lower their scores in the math working memory test. Also, the higher respondents' sense of efficiency, the higher their scores in the math working memory test.

Discussion

The present study explored whether distractions such as WhatsApp text messages affect pupils' working memory. The rationale for the study stemmed from previous research that examined the effect of smartphones' on pupils and students' performance in the classroom and at home (Alzahabi & Becker, 2013; Minear, Brasher, McCurdy, Lewis, & Younggren, 2013; Ophir et al., 2009). Several studies have examined the impact of text messages on reading comprehension tests scores and final semester courses. McDonald (2013) noted that when smartphone use policy in the classroom was permissive and more text messages were sent, students' scores decreased. Lawson and Henderson (2015) found a 20% decrease in reading comprehension scores in students involved in sending and receiving simple text messages during class. In addition, Rosen, Lim, Carrier, and Cheever (2011) examined how students' reading comprehension is affected by text messages and reported that due to information overload, their working memory declined. We examined the direct effect of WhatsApp distractions on pupils' working memory, using the Working Memory Index from the Wechsler Intelligence Scale for Children-IV.

The following section focuses on the research hypothesis that suggested that WhatsApp distractions will decrease the results of the working memory tests and present significant differences between the control group and the experimental group. This hypothesis was accepted and participants who belonged to the experimental group had lower scores in the three subtests of working memory tests.

This finding echoes previous studies that explored the effects of using technology on pupils at home and in class. Windham (2007) found a negative correlation between the use of text messaging while studying and grade point average. Wood et al. (2012) proposed that the use of social media technology, such as instant text messages, leads to a decreased ability to retrieve from memory (Wood et al., 2012). Salvucci, Taatgen, and Borst (2009), who discussed multitasking theory, found that the more text messages students read during a lecture, the lower their memory scores in subsequent tests.

Other researchers (Oulasvirta & Saariluoma, 2004) showed that distractions from text messaging led to a decline in memory accuracy of 16%, especially when the content of the messages was related to the content of the study. Peverly et al. (2012) suggested that because working memory is considered a limited resource, the distraction from mobile phones makes it difficult to encode material properly and take notes accurately during classes. Junco and Cotten (2012) asserted that multitasking, social networking activity, and particularly receiving and sending text messages significantly impaired academic performance of students.

The next hypothesis focused on participants' reports about their feelings of difficulty and efficiency and suggested that respondents who experience distraction from WhatsApp will report that the working memory tests are more difficult and less effective. This hypothesis was confirmed, and findings were in accord with those of Buchweitz, Keller, Meyler, and Just (2011) who examined individuals' ability to listen to two people who talk simultaneously, and with those of Watson and Strayer (2010) who explored people in a driving simulator while they were given memory tasks and problems to solve. Both studies indicated that multitasking performance is less efficient in terms of time and accuracy.

The following hypothesis assumed that there would be a negative correlation between the feeling of difficulty in subworking memory tests and participants' achievements and a positive correlation between participants' feeling of efficacy at subworking memory tests and achievements. This hypothesis was partially confirmed, when considering math and digit recall working memory subtests. When participants felt that the test was difficult, it lowered their digit recall and math scores; when they felt that they were efficient, their scores in digit recall and math were higher.

A similar finding was found by Kirschner and Karpinski (2010), who reported that multitasking involving receiving and processing information decreased both the efficiency of study habits and scores. In a further study (Hembrooke & Gay, 2003), researchers found that using a laptop during class affected the process of learning. Students who used laptops had greater difficulty remembering the lesson content than those who did not use one. Other researchers (Rosen, Carrier, & Cheever, 2013; Rosen et al., 2011) noted that using social media activities during learning reduces the effectiveness of simultaneous learning tasks because both tasks consume the same cognitive resources (e.g., language and meaning analysis).

The main findings of the current study show that WhatsApp's distractions, transmitted via smartphones, decrease pupils' performance of working memory. In addition, students are aware of the difficulty WhatsApp causes while performing a learning task and of the decrease in learning effectiveness.

The current study has several limitations. First, the sample was a convenient one, and most of the pupils were from one school that has a high socioeconomic level, so it will be difficult to generalize the findings. Second, unlike other similar studies that allowed participants to receive messages during the study, in addition to the messages sent by the researchers (Rosen et al., 2011), the current study did not allow personal mobile device use, and the messages were sent by the researchers' device only. This may detract from the authenticity of the situation but enables researchers full control over the content of messages, intervals, and the number of messages; these were kept constant for all participants in the experimental group. Another limitation is the fact that the experiment did not examine working memory performance in a natural learning environment, such as a class lesson. A further limitation is the age range of participants. The Wechsler test that was used in this study is given to children from age 6 until 17; thus, it is possible that older participants will perform differently than younger participants.

We suggest that a follow-up study should take place in a more natural learning environment and include more participants. It will also be interesting to compare the results of the same participant and examine his or her working memory with and without distractions. It is recommended that further research would examine the impact of text message distractions on tasks that involve the use of a spatial-visual component as well.

The current study has theoretical as well as practical implications. On the theoretical level, it displays a unique experiment that explored the direct effect of the distractions stemming from a new technological platform—WhatsApp—on young pupils' working memory. The effect was examined using the Working Memory Index from the Wechsler Intelligence Scale for Children-IV. In addition, the study focused on young pupils, thus expanding the scope of research that considers young pupils' multitasking during the learning process.

On a practical level, the results of the current study suggest that instructors and teachers should be aware of the potential damage of multitasking caused by smartphone use during learning tasks. This can be done by providing students tools that will improve their learning strategies, based on developing executive functions such as flexible thinking, priorities, self-control, self-regulation, strategic planning and organizing, an initiative of operations, sustained attention, controlling distractions, and selecting information. In other words, instructors should create new learning programs that concentrate on content and integrate executive functions that will help students cope with distractions caused by technology. It is also recommended that smartphone use be limited during the school day, as a permissive policy that does not set clear boundaries negatively affects learning efficiency and quality.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Noa Aharony received her PhD in 2003 from the School of Education at Bar-Ilan University, Israel. Her research interests are the information literacy, Internet, technological innovations, and Web 2.0. Prof. Aharony is the head of the School of Information Science at Bar Ilan University. She has published in refereed LIS and education journals.

Avi Zion is a PhD student at the School of Information Science at Bar-Ilan University.

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