The Effectiveness of Safe Surfing Intervention Program in Reducing WhatsApp Cyberbullying and Improving Classroom Climate and Student Sense of Class Belonging in Elementary School

Abstract

Schools have prioritized fighting cyberbullying by implementing intervention programs. Yet few interventions have been studied for their effectiveness in reducing cyberbullying and improving socio-emotional aspects in the classroom. This study reports the results of the Safe Surfing intervention program designed to reduce cyberbullying in WhatsApp classmate discourse. Data were collected in the 2017-2018 school year from 533 students in 25 fourth- to sixth-grade classes (50% females) in six elementary schools in Israel. Two of the schools served as the control group. Results indicated a significant decrease in WhatsApp cyberbullying victimization and a significant improvement of classroom climate and student sense of class belonging in the experiment group. This compared with a significant increase in cyberbullying and a significant decrease in classroom climate and student sense of belonging in the control group. Educational implications are discussed. The findings demonstrate the achievements of the anti-cyberbullying intervention program, alongside the threats of avoiding intervention.

Keywords

media and technology (music,TV,magazines,Internet,gaming,iphones and messaging bullying networks intervention/prevention classroom behavior/environment

Introduction

Social networking sites (SNSs) play a major role in youth socialization and socio-emotional development (Livingstone, Sefton-Green, 2016; O’Keeffe & Clarke-Pearson, 2011). As one such site (Cetinkaya, 2017), WhatsApp enjoys enormous popularity worldwide (Montag et al., 2015; Sánchez-Moya & Cruz-Moya, 2015), and especially in Israel (Bezeq, 2019; Israel National Council for the Child, 2019). However, along with the contribution of SNSs to pro-social activities, they have also raised concern as they have been abused as platforms for cyberbullying (Aizenkot, 2017b; Aizenkot & Kashy-Rosenbaum, 2018; Cohen-Almagor, 2018).

Cyberbullying has been negatively associated with classroom climate (Aizenkot & Kashy-Rosenbaum, 2018; Veiga Simão et al., 2017) and student sense of class belonging (Ferrer-Cascales et al., 2019). Moreover, scarce recent studies have shown that anti-cyberbullying intervention programs have improved classroom climate (Aizenkot & Kashy-Rosenbaum, 2018; Ferrer-Cascales et al., 2019) and student sense of belonging (Ferrer-Cascales et al., 2019) in addition to reducing cyberbullying.

Previous research has found that cyberbullying victimization is more likely to occur in primary school (specifically in fourth to sixth grade), compared with post-elementary school (middle and high school; Aizenkot & Kashy-Rosenbaum, 2019). It has therefore been suggested that anti-cyberbullying intervention efforts should focus on primary school, and specifically start in fourth grade, when most children receive their first smartphone and begin or increase their SNS activity (Aizenkot & Kashy-Rosenbaum, 2019; Israel National Council for the Child, 2019). Given the detrimental mental, social, and academic consequences of cyberbullying victimization (Aizenkot & Kashy-Rosenbaum, 2018; Extremera, Quintana-Orts, Mérida-López, & Rey, 2018; Palermiti, Servidio, Bartolo, & Costabile, 2017), and the increased prevalence of cyberbullying in elementary school (Aizenkot & Kashy-Rosenbaum, 2019), developing and empirically evaluating anti-cyberbullying intervention programs that focus on SNSs in late elementary school is of extreme importance (Gaffney, Farrington, Espelage, & Ttofi, 2019). Nonetheless, to date there is a dearth of research about the effectiveness of relevant intervention programs (Gaffney et al., 2019; Ortega-Barón, Buelga, Ayllón, Martínez-Ferrer, & Cava, 2019).

Against this backdrop, the current study evaluates the effectiveness of Safe Surfing anti-cyberbullying intervention program in reducing WhatsApp cyberbullying and improving classroom climate and student sense of class belonging. The study may have implications for the development of guidelines for preventive interventions in the school setting.

Cyberbullying in WhatsApp SNS

WhatsApp is one of the most preferred mobile-based messaging applications worldwide (Fiadino, Schiavone, & Casas, 2014), especially among Israeli school-age children and adolescents, of whom 95% use the WhatsApp application (Bezeq, 2019; Israel National Council for the Child, 2019). The outstanding popularity of WhatsApp however is not unique to Israel. Surveys comparing mobile phone data from 187 countries worldwide consistently found WhatsApp to be the world’s most popular messaging application in over 100 countries, about 55% of the countries in the world (Schwartz, 2016).

Israeli school-age students are members of about 27 WhatsApp groups on average (Israel National Council for the Child, 2019), some of them classmate groups. Classmate WhatsApp groups are opened independently by students and only include classmates, all or some, but do not include non-classmates (Aizenkot, 2017b; Aizenkot & Kashy-Rosenbaum, 2018, 2019). In a recent exploratory study conducted in Israel among 1,111 fourth- to 12th-grade students, Aizenkot (2017b) found that the vast majority who have access to smartphones are active in one (99%) or more (87%) classmate WhatsApp groups. As WhatsApp is used by 95% of Israeli students (Bezeq, 2019; Israel National Council for the Child, 2019), other SNSs were not measured in the current study.

SNS use offers many benefits (O’Keeffe & Clarke-Pearson, 2011). As such, group and private WhatsApp classmate discourse play an integral and central role in the online class social life (Rosenberg & Asterhan, 2018). However, SNSs can also be abused for cyberbullying, that is, behavior performed through electronic or digital media by individuals or groups that repeatedly communicate hostile or aggressive messages intended to inflict harm or discomfort on others (Hinduja & Patchin, 2008; Tokunaga, 2010). As a consensus has not yet been reached regarding the definition of cyberbullying, the current study uses two conceptual definitions to examine the phenomenon: cyberbullying—repeated aggressive online behavior, meaning that it occurred at least twice (Hinduja & Patchin, 2008; Tokunaga, 2010), and cyber-aggression—aggressive online behavior that occurred at least once (Dooley, Pyzalski, & Cross, 2009).

SNSs are the main platform through which school-age students perpetrate, victimize, and witness cyberbullying (Byrne, Vessey, & Pfeifer, 2018). WhatsApp cyberbullying can be reflected in verbal bullying (manifested in mockery, cursing, insults, derogatory names, and threat), group bullying (reflected in opening a group against someone and group shunning), visual bullying (using photos or videos offensively, for example, posting or sharing offensive photos and videos and tagging photos and videos offensively), and group selectivity, which refers to selectivity in choosing group members (opening a group without a person despite their desire to be included, preventing entry to a group, forced removal from a group, or opening a group comprised of only “selected members”; Aizenkot, 2017b). Recent findings indicate that about 57% of the school students report being victims of cyberbullying in their WhatsApp classmate groups at least once, and 30% report repeated cyberbullying victimization (Aizenkot, 2017b; Aizenkot & Kashy-Rosenbaum, 2018).

Cyberbullying has become a serious social problem throughout the world, especially among school-age children and adolescents (Byrne et al., 2018). This is due to the perpetrator’s ability to bully the victim in his or her own private space at any time (Van Ouystel, Walrave, & Vandebosch, 2014), the reduced likelihood of adult intervention (National Cyber Security Alliance and Microsoft, 2016), and the ability to post and spread aggressive content quickly to a large audience of peers (Byrne et al., 2018). Consequently, cyberbullying victimization has been linked to a range of negative symptoms, including social, physical, and psychological problems such as feelings of loneliness, depression, low self-esteem, social anxiety, and isolation, as well as illicit drug use, behavior problems, and suicide risk (Extremera et al., 2018; Palermiti et al., 2017). Given the gravity of cyberbullying and its proliferation among school-age students, there is an urgent need for research that will contribute to evidence-based intervention strategies. School-based intervention programs are critical in this respect (Aizenkot & Kashy-Rosenbaum, 2018; Cross et al., 2016; Scheithauer & Tsorbatzoudis, 2016).

School Intervention Programs Against Cyberbullying

Numerous studies have been published about cyberbullying in recent years (e.g., Cohen-Almagor, 2018; Kowalski, Giumetti, Schroeder, & Lattanner, 2014; Scheithauer & Tsorbatzoudis, 2016), but only a few have investigated the effectiveness and impact of intervention programs against cyberbullying (Cross et al., 2016; Scheithauer & Tsorbatzoudis, 2016). The studies that evaluated intervention programs have shown that cyberbullying significantly decreased post-intervention (Aizenkot & Kashy-Rosenbaum, 2018; Chaux, Velásquez, Schultze-Krumbholz, & Scheithauer, 2016; Cross et al., 2016; Ferrer-Cascales et al., 2019; Garaigordobil & Martınez-Valderrey, 2015; Ortega-Barón et al., 2019; Palladino, Nocentini, & Menesini, 2016; Wölfer et al., 2014). For example, Ferrer-Cascales et al. (2019) assessed the effectiveness of the Tutoría Entre Iguales (TEI) anti-cyberbullying intervention program among high school students. Results indicated a significant decrease in cyberbullying and a significant improvement of school climate and student sense of belonging post-intervention in the experimental group. Aizenkot and Kashy-Rosenbaum (2018) assessed the effectiveness of an intervention program designed to reduce cyberbullying in WhatsApp classmate groups and found similar findings. A significant decrease in WhatsApp cyberbullying and a significant improvement of classroom climate was achieved. However, the study lacked a control group. Other intervention programs that focused on online and off-line bullying and victimization reduction proved effective as well. For example, Garaigordobil and Martínez-Valderrey (2015) found that Cyberprogram 2.0, a program designed to prevent cyberbullying, was able to significantly reduce cyberbullying and traditional bullying perpetration and victimization. Similarly, Chaux et al. (2016) reported that Media Heroes, a program developed to prevent cyberbullying, led to a reduction in cyberbullying perpetration and victimization and traditional bullying perpetration. Ortega-Barón et al. (2019) found similar findings evaluating the effectiveness of the Prev@cib anti- bullying and cyberbullying intervention program. Results showed a significant decrease in bullying and victimization and cyberbullying and cyberbullying victimization in the experimental group, compared with the control group.

The theory of planned behavior (Ajzen, 1991) posits that people’s intention to perform a certain behavior is the best predictor of their actual behavior. Heirman and Walrave (2012) demonstrated that the theory of planned behavior provides a useful model for studying cyberbullying. In their study, the three main factors (attitude, subjective norm, and perceived behavioral control) explained 44.8% of the total variance of adolescents’ intention to cyberbully. Aizenkot and Kashy-Rosenbaum (2018) followed this model in a wide-scale, school-based intervention program designed to reduce cyberbullying in WhatsApp classmate groups. The results indicated a significantly decreased WhatsApp cyberbullying and a significantly improved classroom climate; yet, the research lacked a control group.

Emotional and Social Classroom Aspects and Cyberbullying

The classroom is perceived as a key point of interaction for young people (Livingstone, Sefton-Green, 2016). Student perceptions of the social atmosphere in their class have a vast impact on their well-being (Kashy-Rosenbaum, Kaplan, & Israel-Cohen, 2018) and on their sense of class belonging (Israel-Cohen, Kaplan, & Kashy-Rosenbaum, 2015). A sense of belonging denotes a feeling of relatedness or connection to others (Booker, 2016; Goodenow, 1993). Class belonging is described as a sense of being accepted, valued, and included by classmates (Goodenow, 1993). Feelings of relatedness and belonging, as well as inclusion and acceptance, have all been linked to emotional engagement (Rubie-Davies, Asil, & Teo, 2016) and emotional and academic outcomes (e.g., Israel-Cohen et al., 2015; Rubie-Davies et al., 2016).

Ample evidence points to the relationship between cyberbullying and a negative classroom climate (Aizenkot & Kashy-Rosenbaum, 2018; Ferrer-Cascales et al., 2019; Veiga Simão et al., 2017). The perception of a poor classroom climate together with cyberbullying may have negative implications, among them social withdrawal and school dropout (Nickerson, Singleton, Schnurr, & Collen, 2014). Accordingly, the literature highlights the need for research focusing on the psychological factors involved in interactions between students as the basis for creating a positive classroom climate in the aim of preventing acts of aggression such as cyberbullying (Veiga Simão et al., 2017). Hence the importance of better understanding the relationship between the shared space of class online social life, classroom climate, and sense of class belonging with respect to cyberbullying (Livingstone, Sefton-Green, 2016).

The Current Study

There is a lack of research evaluating school-based intervention programs against cyberbullying as well as a need for evidence-based strategies (Scheithauer & Tsorbatzoudis, 2016). Against this backdrop, a quasi-experimental study was conducted in the classroom setting, with an experiment and a control group and pre- and post-intervention measurement. The subjects were not randomly assigned to the research groups. The aim of the study was to evaluate the effectiveness of an anti-cyberbullying intervention program, implemented among fourth- to sixth-grade students, in reducing WhatsApp cyberbullying and improving classroom climate and student sense of belonging. The study findings may broaden the understanding of the ways social and emotional variables involved in the process of cyberbullying are connected, and how they are affected by an intervention program against cyberbullying (Aizenkot & Kashy-Rosenbaum, 2018). It is vital to evaluate WhatsApp use in this context in Israel due to the enormous popularity of this SNS in Israel, the high rates of WhatsApp cyberbullying that accompany its use (Aizenkot, 2017b; Aizenkot & Kashy-Rosenbaum, 2018), and the potential influence of cyberbullying on student well-being. Moreover, this may contribute to developing best practices for school-based cyberbullying intervention programs in other countries.

The main hypotheses of this study were that students from the experiment group would demonstrate (a) decreased cyberbullying and cyber-aggression victimization in WhatsApp classmate discourse, (b) an improved perception of classroom climate, and (c) an improved sense of class belonging, while similar changes would not be found among students in the control group.

Method

Participants

Data were collected in the 2017-2018 school year from a total of 533 students (50% females) in 25 classes in six elementary schools (37%—fourth grade, 32%—fifth grade, and 31%—sixth grade) in Israel (An average of 22 students were sampled from each class). Two of the schools were designated as the control group (n = 142, from seven classes) and four as the experiment group (n = 391, from 18 classes). Assignment of the schools to the experiment or control group was not random. Furthermore, the experiment and control groups were not sampled from the same schools although efforts were made to match the socioeconomic level of the school region or neighborhood of the schools assigned to the two study groups. There were no significant differences between the groups at the study variable level prior to the intervention (see “Results” section). Gender and class grade distributions in both groups were similar, χ²₍₁₎ = 3.20, p = .073 and χ²₍₂₎ = .44, p = .803, respectively. Students in the experiment group participated in an intervention program primarily aimed at reducing cyberbullying in WhatsApp classmate discourse. According to the original research plan, all schools were planned to participate in the intervention program as part of a Ministry of Education program. However, in practice, the intervention program was not implemented in all schools as the Meitzav test was conducted in two of the schools that school year and these schools were therefore unavailable. The Meitzav tests are standardized national tests in language, mathematics, and the English language that Israeli school students take once every 3 years (Beller, 2013). These classes served as the control group in this study. Students in this group did not receive intervention during the intervention program and survey completion period. However, the intervention program was implemented in these schools in the following school year.

Procedure

The first measurement was conducted 1 week before the beginning of program implementation, and the second measurement 1 week after program completion. Approximately, 10 weeks elapsed between the two measurements. The intervention program and the follow-up survey were developed for internal use of the Ministry of Education in Israel and monitored by a supervisor from the Ministry. According to Ministry of Education regulations in Israel, internal data collected as part of the Ministry’s regional and national surveys may be used for research purposes (Israeli Ministry of Education, 2015).

Both pre- and post-intervention questionnaires were answered anonymously. The response rate (i.e., the percentage of students who completed both Time 1 and Time 2 questionnaires) was 97%. Student dropout from the program was due to routine reasons of student school absence. Missing data were handled with case-wise maximum likelihood estimation that completed the data set, using the imputing method of regression estimated statistics (Acock, 2012). Given the relatively low rate of missingness (3%), and the fact that missingness was not associated with covariates of interest, statistical bias associated with missing data should not be of significant concern (Bennett, 2001).

To enable linkage of pre- to post-intervention measurement for each student, the students received a random ID number from their homeroom teacher. Students were requested to enter the ID number when filling out the questionnaire, to save the number, and to enter it once again when completing the post-intervention questionnaire. Additional identifying details collected from students were school name, grade, class number, and student gender. Students filled the questionnaires voluntarily, the questionnaires did not include mandatory questions, and penalties were not imposed on students who chose not to complete the questionnaires or not to participate at all. A teacher was present in the classroom when students filled the questionnaires to supervise and ensure order. Student privacy was strictly maintained and the teacher was instructed not to walk among the students while they completed the questionnaire and to respect the privacy of the information provided. Students completed the questionnaires online at school, and the few students who were absent from school on the day the questionnaire was administered were given the option to complete it online at home.

Measures

The following questionnaires were used to measure intervention effectiveness: (a) cyberbullying victimization in WhatsApp classmate discourse, (b) student perception of classroom climate, and (c) student sense of class belonging. All questionnaires were administered pre- and post-intervention.

Questionnaire—Cyberbullying victimization in WhatsApp classmate discourse

The questionnaire consisted of 19 items: exposure to cyberbullying in WhatsApp classmate groups (11 items) and exposure to cyberbullying in WhatsApp private discourse between classmates (eight items). Questionnaires were administered before and after the intervention. Students were asked to report how many times they had been victimized by means of each of the cyberbullying behaviors in the past month, only referring to discourse conducted on WhatsApp between classmates. For example, “Have you been insulted in your WhatsApp classmate groups?” A 4-point scale was used for the response options: 0 = it has never happened to me, 1 = it happened to me once, 2 = it happened to me several times, and 3 = it happened to me many times. The 19 items in this questionnaire represent expressions of cyberbullying that can appear in a WhatsApp classmate discourse, based on previous questionnaires from studies that examined cyberbullying on social networks (Aizenkot, 2017b; Heiman & Olenik-Shemesh, 2015; Willard, 2007). It should be noted that the expressions that appear in the questionnaire are not representative events but rather encompass the entire phenomenon. Internal reliability for the 11 items pertaining to exposure to cyberbullying in WhatsApp classmate groups was found to be α = .80 and α = .88, at Time 1 and Time 2, respectively, and for the nine-items regarding exposure to cyberbullying in WhatsApp private discourse between classmates α = .86 and α = .89, at Time 1 and Time 2, respectively. Scores for cyberbullying in WhatsApp classmate groups ranged from 0 to 33 and for cyberbullying in WhatsApp classmate private discourse from 0 to 26. A higher score indicates reported higher cyberbullying victimization in WhatsApp classmate groups or in WhatsApp private discourse between classmates.

Two additional dichotomous variables were calculated based on the two definitions (Dooley et al., 2009; Hinduja & Patchin, 2008; Tokunaga, 2010): cyberbullying that is repeated at least twice (Hinduja & Patchin, 2008; Tokunaga, 2010) and cyber-aggression that occurs at least once (Dooley et al., 2009). Student responses were classified into two groups, those who claimed to have never experienced cyberbullying and those who reported experiencing cyberbullying or cyber-aggression. This coding enabled us to calculate the percentage of students who were victimized by cyberbullying and cyber-aggression in each class.

Questionnaires—Perception of classroom climate and student sense of class belonging

Perception of classroom climate and student sense of class belonging were measured using a five-item questionnaire (divided into two subscales) taken from a widely used questionnaire compiled by the Israeli National Authority for Measurement and Evaluation in Education (The National Authority for Measurement and Evaluation in Education [RAMA], 2015). This organization is authorized by Israel’s Ministry of Education to conduct national surveys and tests in the education system.

Perceived classroom climate

Students’ perceived classroom climate was assessed using a two-item questionnaire. Spearman–Brown correlations between items were as follows: r_s = .49, r_s = .69, at Time 1 and Time 2, respectively. A small number of questions were selected to measure perceived classroom climate to reduce the probability of nonresponse in filling the questionnaires due to questionnaire length. The two items were as follows: “There is a good atmosphere among students in my class.” “My class is cohesive and unified.” Response options for each statement were based on a 5-point Likert-type scale ranging from 1 (don’t agree at all) to 5 (strongly agree). Principal component factor analysis supported the convergence of the two items to one factor that explains 81% of the variance (eigenvalue = 1.62, loading for each of the two items was 0.90). Item responses were averaged, with higher scores indicating the perception of a more positive classroom climate.

Student sense of class belonging

Student sense of class belonging was assessed using a three-item questionnaire, α = .65, α = .73, at Time 1 and Time 2, respectively. The items were as follows: “I have someone to be with during school breaks.” “I feel a sense of belonging to my class.” “My classmates care about me.” Response options for each statement were based on a 5-point Likert-type scale ranging from 1 (don’t agree at all) to 5 (strongly agree). Principal component factor analysis supported the convergence of the three items to one factor, explaining 59% of the variance (eigenvalue = 1.74; loading for each of the three items ranged from 0.64 to 0.86). Item responses were averaged, with higher scores indicating a stronger sense of belonging.

The Intervention Program

Schools that participated in the study were involved in a large-scale Safe Surfing intervention program in the aim of reducing cyberbullying in WhatsApp classmate discourse. Design of the intervention program was based on several existing Ministry of Education intervention programs against cyberbullying. Selected themes were taken from these programs and modified according to evidence-based best practices described in the literature (Barkoukis, Lazuras, Ourda, & Tsorbatzoudis, 2016; Wölfer et al., 2014), and from the theory of planned behavior (Ajzen, 1991). The rationale of choosing this theoretical approach derives from studies that successfully explained cyberbullying behavior with the help of this model (Heirman & Walrave, 2012; Roberto & Eden, 2010).

The intervention program examined in this study was the second and updated version of a specific intervention program first implemented in 2017 and described by Aizenkot and Kashy-Rosenbaum (2018). The current version of the intervention program consisted of similar topics, but with updated lesson plans and contents.

The current program included eight weekly lesson plans that addressed the following topics: understanding the definition of cyberbullying; expressions and implications of cyberbullying in general, and WhatsApp cyberbullying in particular; the role of bystanders, including developing personal and mutual responsibility; acquaintance with state laws prohibiting cyberbullying; developing skills for judging and self-monitoring contents before disseminating them online; and formulating school rules in workshops attended by the entire school population (teachers, students, and parents). Each topic was covered in either one or two lesson plans.

The intervention program was implemented by the school’s professional staff (school counselors and homeroom teachers). Prior to implementation, the school counselors received training in the intervention program from a regional instructor who supervised the program on behalf of the Ministry of Education. The training included three 1-hour training sessions. School counselors were then requested to conduct the same training in the schools in which they work, in the same way they implement other intervention programs. They conducted the training for the homeroom teachers whose classes would participate in the intervention program. The homeroom teachers were required to implement the program during “Life Skills” lessons. The school counselors submitted progress reports to the regional instructor throughout the homeroom teacher training and the program implementation process. The intervention was implemented during school hours, 1 hour per week, over a 2-month period.

Intervention themes included the following:

Knowledge and competencies. Raise student awareness to relevant legislation and lawbreaking involved in cyberbullying; Develop and practice social skills that foster a positive atmosphere and positive communication in WhatsApp classmate groups.

Attitudes toward WhatsApp cyberbullying. Change student attitudes toward cyberbullying by providing information about WhatsApp cyberbullying expressions and harmful consequences.

Subjective norms. Increase classmate social responsibility in general and of bystanders in particular, develop school rules regarding online communication and digital bullying, define usage norms in WhatsApp classmate groups (including the number of WhatsApp groups in a class, frequency of WhatsApp group turnover, and WhatsApp classmate group composition) and encourage parental involvement. The latter is achieved through workshops and lectures for parents conducted by the school psychologist and designed to provide parents with tools for parental involvement and for monitoring their children’s online activity.

Perceived behavioral control. Develop strategies for self-behavior monitoring and control, and response strategies in case of cyberbullying.

Various methods were used to effectively convey program content, among them short videos, dilemma-based stories, interactive activities, group discussion, cards and information provided and discussed (news items and relevant laws). All lessons began with an informative segment followed by group activities and a discussion. The students’ native language is Hebrew and therefore the survey and the intervention program were both administered in this language) for a full and detailed description of the intervention program, see Aizenkot, 2017a).

Data Analysis

Repeated analysis of variance measures (two-way ANOVAs) were used to analyze pre- to post-intervention effects (student level) on cyberbullying and cyber-aggression or victimization in WhatsApp classmate discourse, perceived classroom climate, and student sense of class belonging among students in the experiment and control groups, adjusted for clustering by demographic characteristics of grade-level and gender. As the Shapiro–Wilk test results for normality of distributions (Shapiro & Wilk, 1965) of pre-post intervention cyberbullying scores, perceived classroom climate, and student sense of class belonging showed that the measures sampled in this study did not have normal data distributions (test results were significant, p < .001), nonparametric statistics were used for follow-up tests to measure intervention effects. The Wilcoxon test was used to analyze whether there were significant differences in pre- to post-intervention scores separately in the experiment and control groups, and the Mann–Whitney test was used to analyze whether there were significant differences between experiment and control group pre- and post-intervention scores (Fay & Proschan, 2010).

Results

Among the participants, 97% reported that they used WhatsApp through their own (95%) or a parent’s (2%) smartphone and 3% reported that they did not use WhatsApp and therefore did not complete the questionnaire items that referred to cyberbullying.

Intervention Effects on Cyberbullying Victimization in WhatsApp Classmate Groups (Quantitative Scores)

Group discourse

A significant main effect was found for group, F(1, 526) = 4.64, p = .032, µ² = .01, but not for time of measurement, F(1, 526) = 1.49, p = .222, µ² = .00. In addition, a significant Group × Time interaction was found, F(1, 526) = 24.23, p = .000, µ² = .05. Simple main effect follow-up tests, using the Wilcoxon test, indicated that the experiment group demonstrated significantly decreased cyberbullying victimization in WhatsApp classmate group discourse, z(385) = 4.59, p = .001, r = .40, while the control group showed a significant increase, z(141) = 3.35, p = .001, r = .82. Furthermore, Mann–Whitney test results revealed that the experiment and control group mean scores differed significantly for post-intervention scores, z(527) = 2.62, p = .009, but not for pre-intervention scores, z(527) = 0.24, p = .814. Post-intervention cyberbullying victimization mean score in WhatsApp classmate group discourse was significantly lower in the experiment group than in the control group.

Private discourse

A significant main effect was found for group, F(1, 526) = 9.74, p = .002, µ² = .02; but not for time of measurement, F(1, 526) = 1.24, p = .266, µ² = .00. In addition, a significant Group × Time interaction was found, F(1, 526) = 16.08, p = .000, µ² = .04. Simple main effect follow-up tests, using the Wilcoxon test, indicated that the experiment group demonstrated a significant decrease in cyberbullying victimization in private WhatsApp classmate discourse, z(385) = 3.98, p = .000, r = .44, while the control group demonstrated a significant increase, z(141) = 2.70, p = .007, r = .77. Furthermore, Mann–Whitney test results revealed that the experiment and control group mean scores differed significantly for post-intervention scores, z(527) = 3.37, p = .001, but not for pre-intervention scores, z(527) = .24, p = .814. The post-intervention cyberbullying victimization level in private classmate WhatsApp discourse was significantly lower in the experiment group than in the control group (Table 1 and Figure 1).

Table 1.

Mean Scores^a and Standard Deviations for Cyberbullying Victimization in WhatsApp Classmate Group and Private Discourse (N = 528).

Discourse	$\bar{X}$	SD	n
Group
Pre
Experiment	4.95	8.82	386
Control	4.63	10.80	142
Post
Experiment	3.36	7.28	386
Control	7.28	14.44	142
Private
Pre
Experiment	5.46	11.14	386
Control	6.69	15.53	142
Post
Experiment	3.85	9.25	386
Control	9.54	19.37	142

The higher the value, the higher the exposure to cyberbullying in WhatsApp discourses.

Figure 1.

Experiment and control group pre-post intervention scores for cyberbullying victimization in WhatsApp classmate group and private discourse.

Intervention Effects on Percentage of Cyberbullying Victimization in Group and Private WhatsApp Classmate Discourse

The results show a significant decrease in the percentage of reported classmate cyberbullying victimization in group and private WhatsApp classmate discourse in the experiment group, according to both definitions. On the contrary, a significant increase in the percentage of cyberbullying victimization in group WhatsApp classmate discourse was observed among students in the control group, but only according to the cyberbullying definition (at least two occurrences). In addition, differences between the experiment and control group were only observed post-intervention, but not pre-intervention. The percentage of post-intervention WhatsApp cyberbullying victimization in group classmate discourse was lower in the experiment group than in the control group (Table 2 and Figures 2 and 3).

Table 2.

The Percentage of Pre-Post Intervention Cyberbullying Victimization in WhatsApp Classmate Group and Private Discourse in the Experiment and Control Groups, According to Both Definitions.

Discourse	Cyberbullying victimization (once or more)			Repeated cyberbullying victimization (at least twice)
Discourse	Experiment	Control	z(p)^a	Experiment	Control	z(p)^a
Group
Pre	47.3%	36.6%	2.20 (.028)	24.0%	12.7%	2.84 (.004)
Post	35.5%	43.0%	1.56 (.119)	14.6%	24.6%	2.72 (.007)
z(p)^b	4.07 (.000)	1.62 (.106)		3.97(.000)	3.16(.002)
Private
Pre	40.7%	39.4%	.26(0.798)	16.6%	14.1%	0.71(.479)
Post	28.4%	37.3%	1.97(.048)	10.7%	19.0%	2.51(.012)
z(p)^b	4.62(.000)	0.54(0.590)		2.85(.004)	1.61(.108)

Note. Mann–Whitney and Wilcoxon test results for group and for pre-post intervention differences. Two-tailed tests.

Mann–Whitney test.

Wilcoxon test.

Figure 2.

Percentage of pre-and post-intervention cyberbullying victimization (once or more) in WhatsApp classmate group and private discourse in experiment and control groups.

Figure 3.

Percentage of pre-post intervention cyberbullying victimization in WhatsApp classmate group and private discourse (more than once) in experiment and control groups.

Intervention, Perceived Classroom Climate, and Student Sense of Class Belonging

Correlations between a more positive perceived classroom climate and student sense of class belonging to cyberbullying victimization in pre- and post-intervention group and private WhatsApp classmate discourse showed significant, negative, low-medium intensity correlations (Table 3). This means that the higher the level of exposure to cyberbullying in WhatsApp classmate discourse, the lower the student’s pre- and post-intervention sense of class belonging and perceived positive classroom climate.

Table 3.

Correlations Between Pre- and Post-Intervention Perceived Classroom Climate and Student’s Sense of Belonging to Cyberbullying Victimization in WhatsApp Classmate Discourse (N = 529).

	Cyberbullying
	Group	Private
Pre
Perceived classroom climate	−.24***	−.29***
Sense of belonging	−.33***	−.38***
Post
Perceived classroom climate	−.33***	−.34***
Sense of belonging	−.35***	−.37***

***

p < .001.

Mean scores of perceived classroom climate and student sense of class belonging were analyzed using two-way repeated measure ANOVAs tests, with groups (experiment vs. control) as the between-group factor, and time of measurement (pre- to post-intervention) as the within-group factor. Results are reported in Table 4.

Table 4.

Mean Scores^a and Standard Deviations for Pre- and Post-Intervention Perceived Classroom Climate and Student Sense of Belonging in the Experiment and the Control groups (N = 533).

	$\bar{X}$	SD	n
Perceived classroom climate
Pre
Experiment	3.41	0.94	391
Control	3.44	0.93	142
Post
Experiment	3.58	1.05	391
Control	3.33	1.02	142
Sense of belonging
Pre
Experiment	4.16	0.87	391
Control	4.24	0.90	142
Post
Experiment	4.23	0.88	391
Control	3.99	1.07	142

Scores range from 1 to 5, with a higher score indicating a stronger sense of belonging and more positive perceived classroom climate.

Perceived classroom climate

A significant main effect was not found for group, F(1, 531) = 1.77, p = .184, µ² = .00, or for time of measurement, F(1, 531) = .31, p = .577, µ² = .00. However, a significant Group × Time interaction was found, F(1, 531) = 7.83, p = .005, µ² = .02. Simple main effect follow-up tests, using the Wilcoxon test, indicated that the experiment group demonstrated a significant improvement in perceived classroom climate, z(391) = 3.76, p = .000, r = .52, while no significant change in perceived classroom climate was found in the control group, z(142) = 103, p = .303, r = .29. Furthermore, Mann–Whitney test results revealed that the mean scores of experiment and control groups differed significantly in post-intervention scores, z(531) = 2.93, p = .003, but not in pre-intervention scores, z(531) = 0.54, p = .590. Post-intervention perceived classroom climate mean score was significantly higher in the experiment group than in the control group.

Student sense of class belonging

A significant main effect was not found for group, F(1, 531) = 1.22, p = .270, µ² = .00, or for time of measurement, F(1, 531) = 3.48, p = .063, µ² = .01. However, a significant Group × Time interaction was found, F(1, 531) = 11.93, p = .001, µ² = .02. Simple main effect follow-up tests, using the Wilcoxon test, indicated that the experiment group demonstrated a significantly increased sense of class belonging, z(391) = 2.17, p = .030, r = .49, while a significant decrease was found in the control group, z(142) = 2.36, p = .018, r = .32. Furthermore, Mann–Whitney test results revealed that the mean scores of experiment and control groups differed significantly for post-intervention scores, z(531) = 2.04, p = .041, but not for pre-intervention scores, z(531) = 1.51, p = .132. Post-intervention mean score of student sense of class belonging was significantly higher in the experiment group than in the control group (Figure 4).

Figure 4.

Perceived classroom climate and student sense of belonging pre- and post-intervention scores in experiment and control groups.

Discussion

The current study evaluated the effectiveness of an anti-cyberbullying intervention program in reducing WhatsApp cyberbullying and improving classroom climate and student sense of class belonging. Consistent with the research hypotheses, results indicated a significant decrease in WhatsApp cyberbullying victimization and a significant improvement of classroom climate and student sense of class belonging in the experiment group. This compared with a significant increase in cyberbullying and significant decrease of classroom climate and student sense of class belonging in the control group. The findings expand the limited literature related to school-based intervention programs against cyberbullying (Cross et al., 2016; Palladino et al., 2016) by demonstrating the effectiveness of an anti-cyberbullying intervention program, not only in reducing cyberbullying but also in improving classroom climate and student sense of class belonging. These results validate previous anti-cyberbullying intervention programs that focused on cyberbullying reduction and social climate improvement. For example, Ferrer-Cascales et al. (2019) assessed the effectiveness of the TEI anti-cyberbullying intervention program among high school students. Results indicated a significant decrease in cyberbullying and a significant improvement of school climate and student sense of belonging post-intervention in the experimental group. Aizenkot and Kashy-Rosenbaum (2018) found similar findings assessing the effectiveness of an intervention program designed to reduce cyberbullying in WhatsApp classmate groups. The results indicated a significantly decreased WhatsApp cyberbullying and a significantly improved classroom climate. Taken together, these results highlight the ability of intervention programs designed to reduce cyberbullying to improve socio-emotional aspects in the classroom as well.

Alongside the achievements of the intervention program in the experiment group, changes in the control group between the two measurements should be discussed. The significantly increased cyberbullying observed in the control group can be explained in two ways. First, aggressive behavior may increase over the school year if not properly addressed at the outset by school administrators and teachers (Ioana, Ştefan, Cristian, & Anca, 2012). A second possible explanation of increased cyberbullying in the control group can be derived from the theory of social learning (Bandura, 1977), which proposes that behaviors are learned through a continuous process of observation and reinforcement of other classmates’ behaviors. Applying this theory to cyberbullying, we can say that as it occurs in WhatsApp classmate groups in which all group members are exposed to the aggressive behavior (Aizenkot, 2017b; Aizenkot & Kashy-Rosenbaum, 2018), the students may learn this behavior through the continuous process of observation and its reinforcement by their classmates, which in turn may lead to increased online bullying (Gentile, Coyne, & Walsh, 2011). Future research should examine cyberbullying at several points in time during the school year to assess changes in the rate of cyberbullying. Interpreted in the context of the SNS online arena, these explanations may suggest that exposure to cyberbullying on SNSs holds the potential of exacerbating aggressive online behavior both in the short and the long term. Moreover, when significant measures are not taken against cyberbullying, this may result in greater classmate exposure to the phenomenon over time, potentially increasing cyberbullying.

The improvement of class climate and student sense of class belonging post-intervention can be explained by a change in communication norms in WhatsApp classmate discourse, which may have contributed to a change in overall communication norms in the classroom, thereby improving perceived classroom climate. Furthermore, significantly decreased cyberbullying in WhatsApp classmate discourse may be linked to a more positive perception of classroom social climate (Aizenkot & Kashy-Rosenbaum, 2018). This may explain the decreased sense of class belonging among students in the control group, that is, as cyberbullying increased (in the second measurement) students felt a decreased sense of class belonging. Subsequent studies should further examine the relationship between exposure to cyberbullying and harm to students’ mental well-being and scholastic functioning.

Limitations and Recommendations for Future Research

Some limitations of this study should be considered when interpreting the findings. The main weakness of this study is the lack of an identical control group. Schools that did not implement the intervention program were allocated as control group. Thus, allocation of the schools to the experiment and control groups was not random, which exposed the study results to internal threats. As a result, sampled schools may differ from one another, for example in population type.

Another limitation concerns the proximity of post-intervention measurement to the end of the intervention. Thus, while the findings indicate that cyberbullying indeed decreased during the program, the stability of the program achievements should be examined in subsequent studies.

The measurement tool developed for the purpose of the survey was based on proposals made by Willard (2007) and Aizenkot (2017b) and did not include a definition of cyberbullying. It is important to continue to improve and validate the measurement tool used in this study. One of the ways to do so may be to adjust the tool according to a suggestion made by Tokunaga (2010) that cyberbullying measurement tools should consider including a definition of cyberbullying.

Cyberbullying was only measured based on the participants’ reports—a subjective source of information dependent on the subject’s interpretation and memory of the experience, which may sometimes be biased and different from the experience itself (Kahneman, 2011). To more accurately and extensively validate cyberbullying measurement, additional sources should be used in follow-up studies for data collection, for example, by sampling experiences (Kahneman, 2011) or performing content analysis of actual chats between students in WhatsApp classmate discourse.

Another limitation relates to the exclusive focus of the current study on WhatsApp cyberbullying among classmates, whereas cyberbullying may occur in various SNSs (Gaffney et al., 2019; Scheithauer & Tsorbatzoudis, 2016). Future research should address this limitation by examining cyberbullying experiences among students from the entire school in various SNSs and avoid focusing on just one SNS.

Finally, another recommendation for future research would be to conduct a comparative intercultural study of cyberbullying in WhatsApp classmate groups. Multi-method studies may consider examining cultural differences and practices as well as actual intervention strategies and their outcomes, based on qualitative and quantitative studies.

Conclusion

The Safe Surfing intervention program examined in this study can easily be applied to other SNSs by modifying some of the contents so that they are relevant for the targeted SNS. For example, one of the dilemma-based stories in the current intervention program describes an adolescent girl whose classmates made fun of her in the classmate WhatsApp group. The students are asked to recall a similar event from their own life, and to consider how they and the other bystanders responded, in the aim of drawing attention to and defining the bystander’s role deemed appropriate by the program. The purpose of this program activity and the accompanying lesson plan may also be relevant for other SNSs, with modifications made according to the targeted SNS. Generally speaking, it is suggested that future interventions aimed at reducing cyberbullying in SNSs focus on the main themes of the current intervention program (Barkoukis et al., 2016; Wölfer et al., 2014). Follow-up research is recommended to evaluate the effectiveness of the intervention program in reducing cyberbullying in other SNSs and to examine whether the change is sustainable over time.

The findings of the current research demonstrate the achievements of implementing an intervention program against cyberbullying in early adolescence, alongside the threats and harmful consequences of avoiding intervention. The primary implication of these findings may be the vital necessity for school principals to give high priority to establishing a safe online environment and to eradicating cyberbullying by adopting two core courses of action as part of the school agenda. The first relates to proactive implementation of a spiral and continuous intervention program against cyberbullying. This recommendation refers to a developmental intervention program adapted for students in fourth to sixth grade, which is implemented proactively at the beginning of each school year as an integral part of the school curriculum. The second course of action would be to encourage school principals to formulate a cyberbullying school policy. The policy should clearly define acceptable online behavior, specify measures that will be taken against cyberbullies, and encourage collaboration with parents.

School psychologists, the mental health professionals charged with promoting student well-being in the school setting, can play a significant role in addressing cyberbullying. The current study finding, that a significant percentage of students are cyberbullying victims, raises concerns regarding the risk involved in WhatsApp classmate discourse. This should encourage school psychologists to collaborate with school principals and counselors in promoting a safe online environment, no less than caring for the well-being of cyberbullying victims. In addition, school psychologists can provide parents with tools for parental involvement and for monitoring their children’s online activity.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Dana Aizenkot

Author Biographies

Dana Aizenkot is an associate doctor in the Department of Multidisciplinary Studies in Social Sciences at Ashkelon Academic College, Israel. Her research interests include cyberbullying in social networks among school-age children and adolescents, 21-century skills, and posttraumatic growth following loss and life challenges among children and adolescents.

Gabriela Kashy-Rosenbaum is an associate doctor in the Department of Multidisciplinary Studies in Social Sciences at Ashkelon Academic College, Israel. Her research interests include social networks cyberbullying, learning motivation and achievement, well-being, and emotional resilience.

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