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Revista Românească pentru Educaţie Multidimensională ISSN: 2066-7329 | e-ISSN: 2067-9270 Covered in: Web of Science (WOS); EBSCO; ERIH+; Google Scholar; Index Copernicus; Ideas RePeC; Econpapers;
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2021, Volume 13, Issue 4, pages: 239-266 | https://doi.org/10.18662/rrem/13.4/481
An Analysis of the Attitudes of Secondary School Students on Cyber Bullying Behaviors
Erinc ERCAG1
1 Department of Computer Education and Instructional Technology, University of Kyrenia, Via Mersin 10, Northern Cyprus, Turkey, erinc.ercag@kyrenia.edu.tr, https://orcid.org/0000-0002-6681- 7625
Abstract: The utilization of ICT in education has led to the widespread use of these technologies by students. Accordingly, traditional peer bullying has shifted from the school playgrounds to the virtual world and thus created an updated form of bullying called cyber bullying. The purpose of this study is to look into Cyber Bullying Behaviors of Secondary School Students in Northern Cyprus. The randomly selected sample consisted of a total 1206 students attending junior high school, high school and vocational high schools in the 2018-2019 academic year. A descriptive research method of the general survey model was applied in this research. The students were given a questionnaire form that included a ‘Cyber bullying Scale’ and a ‘Personal Information Form’ during the data collection process. The results determined that the students of secondary and vocational secondary education engaged in low level cyber bullying behaviors. It is believed that the results obtained will contribute to the prevention of cyber bullying behaviors and to future studies towards encouraging a boost in healthy internet usage. Keywords: cyber bullying, students, secondary school, education, Northern Cyprus. How to cite: Ercag, E. (2021). An Analysis of the Attitudes of Secondary School Students on Cyber Bullying Behaviors. Revista Romaneasca pentru Educatie Multidimensionala, 13(4), 239- 266. https://doi.org/10.18662/rrem/13.4/481
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Introduction
In the present era of technology, it is evident that Information and
Communication Technologies (ICT) are widely used especially among young
people. Both technology and bullying and cyberbullying cause huge problems
among young people in the world. (Herrera-López et al., 2018). It's not right
not to see these facts. This fact has led to an increase in cases of cyberbullying
among students. It is a type of deliberate violence that cannot easily defend
itself if the definition of traditional bullying is expressed, and which occurs
repeatedly and over time against a bully (Olweus, 2013). This reality has led to
the increase in the incidences of cyber bullying among students. A definition
for cyberbullying can be given as follows; It is the deliberate and repeated
harm of another person by one person or persons using computers, mobile
phones and other electronic devices (Patchin & Hinduja, 2006). Eroğlu (2011)
has established that cyber bullying behaviors have a negative impact on both
the bullies and their victims with respect to their social interactions, their
psychological states of mind and in their educational progress. Slonje et al.
(2013) stated that cyberbullying is a tyranny through digital or electronic
technologies. Many studies have expressed the huge negative consequences of
both traditional bullying and cyber-bullying (Beckman et al., 2012; Campbell et
al., 2012; Schneider et al., 2012).
Cyberbullying has emerged in the last 20 years due to the
advancement and spread of technology. The concept of cyberbullying was
first used by Canadian educator Bill Belsey (2007). Cyberbullying and
victimization is a concept defined in different ways. Cyberbullying is also
defined as embarrassing, harassing, intimidating and threatening the targeted
individuals by using information technologies (Erden, 2017; Smith, 2012).
When we look at the characteristics of cyberbullying, it is seen that it has
characteristics such as persistence, repetition, harming, using computers,
mobile phones and other electronic devices (Eroğlu et al., 2011; Tezer,
2017). It was stated that young people started to use the internet incorrectly
and as a result of this misuse, deterioration in social relations, low school
success, depression, and stress, mental health deterioration occurred. When
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the studies on cyberbullying are examined, it is seen that the bullying
behavior is carried out using digital technologies, it is repetitive, and it has
common features such as the consequences that can affect the health and
psychology of the victim in different dimensions (Semerci, 2017).
In addition to facilitating daily life, communication tools such as the
Internet, mobile phone, and text message service turn into tools that harm
other people in the hands of malicious users. Anonymous calls, spam e-mails
sent with a secret identity, voices, images and texts that contain insults and
threats, emails or text messages to defame a person or group, infected e-
mails, all these malicious acts it was defined under the name of cyberbullying
(Aricak, 2009). Researchers have stated cyberbullying as deliberately and
repeatedly harming another person or group using the internet or other
digital technologies (Agaston et al., 2007; Belsey, 2007; Patchin & Hinduja,
2006; Strom & Strom, 2005; Willard, 2007).
Hence, it has been stated that cyber bullying should be recognized as
a serious problem which is becoming an epidemic and prevention can only
be achieved by determining the factors causing it. The types of cyber
bullying are presented in figure 1 (Willard, 2007).
Figure 1. Types of cyber bullying
Source: (Source: Willard, 2007)
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Should cyberbullying today be seen as a subcategory of general
bullying? (Slonje et al., 2013) or a different type of bullying emerging in the
new century? (Englander et al., 2017). There is a lot of discussion about this
to answer these questions. Some researchers have stated that cyberbullying is
not associated with emerging new victims, but only with the same victims
who bully using new technological tools (Beltrán-Catalán et al., 2018). From
this perspective, they stated that traditional bullying and cyberbullying
constitute a power imbalance between bully and victim, as well as have
commonalities such as behavior types or motivations (Wolke et al., 2017).
The main source of cyberbullying is traditional bullying grievances
(Espelage, 2014; Lazuras et al., 2017; Livingstone et al., 2011).
Willard (2007) listed the types of cyberbullying as follows; Online
flaming (sending a humiliating or vulgar message directly to himself or to
online groups); harassment (sending aggressive messages to the individual
constantly); denigration (making rumors about the individual to harm the
dignity); impersonation (sending messages that will distress the individual or
make him look bad to others on behalf of that individual); Unauthorized use
of someone else's information on the Internet (outing/trickery - to share
their intimate secrets in a virtual environment, sending their photos and
videos to others or provoking the individual to share personal information);
cyberstalking (threatening or harassing the individual by using public areas
such as message boxes, chat rooms) and exclusion (deliberate exclusion of
the individual from the virtual environment).
Research shows that cyber victims use social media heavily, become
more dependent on the internet, exhibit longer-term online skills and risky
online behaviors, as well as chat more with older online friends as a result of
long-term use in the online world (Dredge et al., 2013; Staksrud et al., 2013).
It is viable to classify any student who engages in any kind of cyber
bullying activity as a bully. It appears that cyber bullying has become an
important problem, especially in schools (Kavuket al., 2016; Tezer, 2017). A
study conducted by Li (2007) found that students engaged in cyber bullying
activities on school premises by means of connecting their smart phone
devices to the internet. It has been determined that the most common places
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that young people display cyber bullying behaviors are in schools and their
surroundings. Young people of this century are totally consumed with
technology and the internet and are observed to employ electronic
communication as a tool for committing heinous acts such as ridicule,
harassment and slander (Willard, 2007).
It is accepted that bullying is a serious trauma for students, its effects
are not limited to the period of bullying, but it lasts for life and negatively
affects public health (Kepenekci & Cinkir, 2006). In children exposed to
peer bullying; Withdrawal problems such as psychosomatic symptoms and
depression are increasing. Also, results such as increased aggression
behavior, decreased coping skills, post-traumatic stress disorder and even
suicidal tendency can be observed in these children (Karatas & Ozturk,
2011; Flannery et al., 2004). Children who bully other children; If left to
themselves, they can continue to abuse their power over others, pose a
threat to those around them, and be isolated from society and remain alone.
Also, it is stated that psychological problems experienced by cyber victims
also cause physiological disorders and diseases, and even lead to suicides of
the victims (Horzum & Tuncay, 2014; Olweus & Breivik, 2014). In recent
years, cyberbullying has manifested itself widely, especially in adolescence
and pre-adolescence. Mishna et al., (2010) found that approximately 50% of
the students were cyber victims and 34% were cyberbullies in their study on
middle school and high school students. Akbulut et al. (2010) stated in a
study they conducted that 56% of the participants were subjected to
cyberbullying at least once.
Today, experts do not have a common idea of the singularity of
online victimhood. From a scientific point of view, online and offline
bullying has a common understanding of many risk factors, psychological
consequences and frequency of victims suffering from both types of
victimization (Gini et al., 2018). Studies and meta-analyses of victims of
traditional bullying and cyberbullying have shown negative consequences of
bullying (Beckman et al., 2013; Campbell et al., 2012; Schneider et al., 2012)
and online and offline cyberbullying prevention factors. (Kowalski et al.,
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2007; Zych et al., 2019; Zych et al., 2015). However, research on cyber
victim profiles is still not enough.
When the EU Kids Online IV project in Europe is examined, It
stands out that it aims to provide an understanding of online activities and
risks. This project seems to have revised and developed children's
experiences, a theoretical framework for research on children, online
experiences, with a particular focus on 9-17 year olds. In addition, EU Kids
Online members have launched new collaborative international projects on
private issues, some of which are online use and cyberbullying (Zlamal et al.,
2020). In their research, Machackova and Görzig (2015) stated that some
young people are more vulnerable to online and offline bullying than others.
Therefore, policy initiatives have added to their reports that they should
focus on those most likely to be harmed. When the results of the research
were examined, it was seen that girls, young children, and those with
psychological and social problems were more at risk, thus suggesting that
youth should increase their digital skills, coping and resilience, address socio-
demographic groups differently, and offer online opportunities (Görzig and
Machackova, 2015).
The increase in aggressive behaviors during the use of
communication technologies amongst students makes it essential to
investigate the causes of this aggression in the virtual environment and the
ways in which these behaviors can be avoided (Tuna et al., 2017). "Internet
and online technologies", which students use most frequently, continue to
appeal to wider masses day by day (Cicioglu, 2014; Tekakpınar & Tezer,
2020). The unrestrained use of these technologies can have several
undesirable effects on the development of individuals, particularly on young
people in their development stage. The certainty that the internet offers far
more convenience for engaging in the act of cyber bullying than in real life is
indisputable. This is mainly due to the fact that in real life people may be
deemed culpable or may have to face some physical consequences for their
bullying; whereas, the internet provides them a safe environment to hide
their identities which results in more intense attacks (Tezer, 2017). This
suggests that there is a possibility that anyone can be exposed to cyber
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bullying behaviors at any given time, or become a cyber bully themselves. A
review of the research carried out in the international arena shows that
scientific studies on cyber bullying behaviors have notably become
precedent. Regrettably, this is not the case in North Cyprus. Based on this
fact, the research question this study aims to find answers to is as follows:
What are the attitudes of secondary school students in North Cyprus
regarding cyber bullying behaviors.
Aim
The purpose aim of this research was to investigate the attitudes of
students attending schools registered to Ministry of National Education
(MEB) and the Secondary Education Department and Vocational Technical
Secondary Office in Northern Cyprus regarding cyber bullying behaviors in
terms of their demographic variables (gender, school type, grade, school
district and weekly internet access duration).
For this purpose, the following sub-objectives related to the
demographic variables of the students were determined:
1. What is the level of cyberbullying behaviors of secondary school
students in Northern Cyprus?
2. Are the students' attitudes towards cyber bullying behaviors
significantly different with respect to the gender, school type, grade, school
district and weekly internet access duration in secondary education in
Northern Cyprus?
Materials and methods
In this study general survey model was used. General survey model is
the screening arrangements carried out on a group, sample group or a
paradigm or the entire universe in order to draw a conclusion about the
universe which is composed of numerous elements (Karasar, 1995).
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Population and Sample
The population of this study comprised of 18,978 students who were
enrolled during the 2018-2019 academic year in schools registered to the
Ministry of National Education, Ministry of General Education and
Vocational Technical Secondary Education Office. The figures on the
number of students constituting the population were collected based on the
information obtained from the statistics books of the Ministry of National
Education, Ministry of General Education and Vocational Technical
Secondary Education Office. The regions of Northern Cyprus were
identified as the sub-strata. The sampling was determined from these sub-
strata by means of simple random sampling method. From the population
mentioned above,the randomly selected sample consisted of a total 1206
students attending junior high school, high school and vocational high
schools. Figure 2 provides the detail of the study’s population and the
sample groups which have been determined.
Figure 2. The research population, sample group and regional population distribution ratios
Source: Author own conception
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The target sampling frame of the students constituting the sample
group is 6%. Büyüköztürk et al., (2017) indicate that this ratio is sufficient in
order to reduce the sample error and increase the sample representation of
the sample for researches implementing the general screening method. Table
1 gives the demographic information of the students participating in the
survey.
Table 1. Student distribution by demographic characteristics
Source: Author own conception
Gender N %
Female 608 50.4 Male 598 49.6
Grade N %
Grade 6 Grade 7 Grade 8 Grade 9
177 176 176 169
14.7 14.6 14.6 14
Grade 10 169 14 Grade 11 169 14 Grade 12 170 14.1
School Type N %
Middle School High school Vocational high School
529 389 288
43.8 32.3 23.9
Total 1206 100
According to Table 1, the groups of students who were analyzed
according to gender, grade and school type. 529 (43.8%) of the participants
were in middle school, 389 (32.3%) were in high school and 288 (23.8%)
were studying in vocational high schools.
Data Collection Tool and Application
Aricak et al. (2012) "Cyber Bullying Scale” was used to collect the
data for this research as it was tried to determine the level of cyberbullying
behaviors of students. The construct validity of the scale was examined by
exploratory factor analysis, and it was stated that the scale had a single factor
structure. The internal consistency reliability coefficient of the scale was
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tested with Cronbach's alpha analysis, and the consistency of the scale over
time was examined with the test-retest method. The The scale represents a
single factor construct containing 24 items. Cronbach alpha coefficient
calculated for the whole scale is .95; test-retest reliability coefficient was
specified as .70. The Cronbach alpha coefficient calculated for the whole
scale in this study is .92. This result verified that the internal consistency of
the cyber bullying scale was very high and reliable. In this study, the
questionnaire form is comprised of 2 sections. A ‘Personal Information
Form’ prepared by the researcher was used to get the information about the
demographic variables related to participants and included questions about
gender, school type, grade, school district and weekly internet access
duration in secondary education in the first section. Second section contains
the items of “Cyber Bullying Scale”. Before the questionnaire was applied to
the students, the researchers obtained the necessary permissions from their
university and the Ministry of National Education.
Analysis and Interpretation of Data
The data collected during the study was uploaded on to version 23.0
"Statistical Package for Social Science" (SPSS) program and was analyzed
under the supervision of an authority of this field. In this study, firstly,
frequency, mean and standard deviation were calculated as descriptive
statistics for variables. In demographic variables and subgroups, parametric
tests (Independent t-test, Variance Analysis- ANOVA, Tukey HSD tests)
were used where scores of the scale were normally distributed (p>.05) and
nonparametric tests (Mann Whitney U and Kruskal-Wallis) were used where
they were not normally distributed (p<.05).
Results
This section focuses on the findings obtained from the statistical
analysis of quantitative data about students’ Attitudes on cyber bullying
behaviors and comments related to the findings.
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Descriptive Analysis Results of Students' Attitudes Regarding Cyber
Bullying Behaviors
The results of the descriptive analysis of the data on the bullying
behaviors of the students obtained within the scope of the research are given
in Table 2.
Table 2. Descriptive analysis results of students' attitudes on cyber bullying behaviors
Source: Author own conception
N Mean(M) Sd
Cyber Bullying 1206 1.22 .425
Table 2 displays that students have low level of bullying behaviors
(M=1.22, Sd=.425).
Analysis of Students' Attitudes Regarding Cyber Bullying Behaviors
According to Gender Variable
The results of the analysis of the data on the bullying behaviors of
the students according to the gender of the students are given in Table 3.
Table 3. t-Test analysis of the attitudes of students on cyber bullying behaviors according to
gender variable
Source: Author own conception
Table 3 indicates that the average (M= 1.24, Sd = .474) of male
students engaging in cyber bullying was significantly higher than female
students' cyber bullying average (M= 1.19, Sd = .369). Male students in
secondary and vocational secondary levels who took part in the study engage
in cyber bullying behaviors more than their counterparts.
Gender N M Sd t p
Cyber Bullying Female 608 1.19 .369
-1.790 .002 Male 598 1.24 .474
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Analysis of Students' Attitudes Regarding Cyber Bullying Behaviors
According to School Variable
Descriptive statistics and variance analysis results according to
school type variable of students' attitudes on bullying behaviors are
presented in Table 4 and Table 5.
Table 4. Mean and standard deviation values in relation to school type variable on attitudes of
students on cyber bullying behaviors
Source: Author own conception
School Type N M Sd
Middle School 529 1.20 0.412 High school 389 1.23 0.405 Vocational high School 288 1.22 0.472 Total 1206 1.22 0.425
Table 5. ANOVA analysis results according to school type variance of attitudes of students on
cyber bullying behaviors
Source: Author own conception
Source of Variance
Sums of Squares Df Means of Squares F p
Between-Group .141 2 .070 .388 .678 Within-Group 217.894 1203 .181
Total 218.035 1205
p<.05
The results of variance analysis (ANOVA) of students' attitudes on
bullying behaviors according to school type variable yielded no significant
statistical difference [F(2-1203)=.388; p=.678>.05]. However, although there
was no statistical discrepancy between the variables of middle school
(M=1.20, Sd=.412), high school (M=1.23, Sd=.405) and vocational high
school (M=1.22, Sd=.425), it was determined that the group mean was
generally low.
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Analysis of Students' Attitudes Regarding Cyber Bullying Behaviors
According to Grade Variable
Descriptive statistics and variance analysis results of students'
attitudes on bullying behaviors according to Grade variable are given in
Table 6 and Table 7.
Table 6. Mean and standard deviation values in relation to grade variable on attitudes of
students on cyber bullying behaviors
Source: Author own conception
Grade N M Sd
6 177 1.17 .329 7 176 1.21 .427 8 176 1.24 .468 9 169 1.22 .405 10 169 1.22 .452 11 169 1.26 .449 12 170 1.19 .431
Total 1206 1.22 .425
Table 7. ANOVA results according to the grade variable related to cyber bullying behaviors of
students
Source: Author own conception
Source of Variance Sum of Squares df Means of Squares F p
Between-Group 1.018 6 .170 .938 .467 Within-Group 217.016 1199 .181
Total 218.035 1205
p<.05
No statistically significant difference was ascertained in the results of
variance analysis (ANOVA) of students' attitudes on bullying behaviors
according to grade variable (p>.05).
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Analysis of Students' Attitudes Regarding Cyber Bullying Behaviors
According to School District Variable
The Kruskal-Wallis test results on students' attitudes on cyber
bullying behaviors according to the school district variable are presented in
Table 8.
Table 8. Kruskal-Wallis Test for School District Variable Related to Cyber Bullying Behaviors
of Students
Source: Author own conception
School District N Mean Rank df X2 p
Nicosia 439 569.97
4 31.930 .000
Kyrenia 180 661.12
Famagusta 250 564.54
Güzelyurt 181 590.10
İskele 156 709.34
p<.05
The Kruskal Wallistest test result yielded statistically significant
differences between the group averages according to the school district
variable (p=.000; p<.05). According to the results of the Mann Whitney test
that aimed at determining the differences between the regions, it was
established that the students in İskele (709.34) had higher levels of Cyber
Bullying behaviors than the students in the Nicosia (569.97), Kyrenia
(661.12), Famagusta (564.54) and Güzelyurt (590.10) regions.
Analysis of Students' Attitudes Regarding Cyber Bullying Behaviors
According to Weekly Internet Access Duration Variable
As a demographic question, the students were asked how long they
used the internet in general (playing games, listening to music, doing
homework, watching movies and other purposes) in a week and the data
were obtained by asking them in line with their opinions. Descriptive
statistics and variance analysis results of the attitudes of students on cyber
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bullying behaviors according to the weekly internet usage period variable are
presented in Tables 9 and 10.
Table 9. Mean and standard deviation values of weekly internet access duration variable related
to cyber bullying behaviors
Source: Author own conception
Weekly Internet Usage N M Sd
Less Than 4 Hours 295 1.15 .248 4 to 12 Hours 376 1.13 .235 13 to 27 Hours 251 1.16 .281 More Than 28 Hours 284 1.40 .665
Table 10. ANOVA Analysis Results According to Weekly Internet Access Duration
Variable Regarding Cyber Bullying Behaviors
Source: Author own conception
Source of Variance Sums of Squares df Means of Squares F p
Between-Groups 15.506 3 5.169 30.675 .000 Within-groups 202.529 1202 .168
Total 218.035 1205
p<.05
The variance analysis (ANOVA) on the attitudes of the students
regarding cyber bullying behaviors according to the Weekly Internet Access
Duration variable showed statistically significant difference between the
group averages [F(3-1202)=30.657; p<.05]. According to the results of the
Tukey HSD test to establish which groups yielded different results, the
students' whose weekly access duration to the internet was more than 28
hours (M=1.40, Sd=.665) had significantly higher levels of cyber bullying
than those who accessed the internet between 13 to 27 hours (M=1.16,
Sd=.281), 4 to 12 hours (M=1.13, Sd=.235) and less than 4 hours (M=1.15,
Sd=.248).
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Discussion and Conclusion
The result of the study determined that the students participating in
the study overall had low levels of cyber bullying behaviors. However, it was
observed that male students had significantly higher cyber bullying behaviors
than female students. The study found that one of the main reasons for this
is that the male students who are educated in schools registered to the
Ministry of National Education in Northern Cyprus spend more time on the
internet than girls. When examined, the literature review shows that the
results obtained are consistent with similar studies. Serin (2012) stated that
male students' bullying behaviors and cyber victim statuses were significantly
higher than female students. Similarly et al., (2012) conducted a survey in
Spain with 1431 students between the ages of 12 to 17 and it revealed that
male students had higher levels of cyber bullying behaviors than their
counterparts and that 44.1% had engaged in the act of cyber bullying at least
once. On the contrary, in some European countries, female students are
more exposed to cyberbullying. However, it has been stated that the teachers
at their schools give more homework to the female students studying in
these countries (Zlamal et al., 2020).
In another study, Chi et al., (2020) showed a significant increase in
school-age cyberbullying, with 45.1% of students participating in the study
falling victim to at least one type of online bullying in the past year. This rate
is higher than some previous studies that reported 11% (Kowalski and
Limber, 2007) and 21% prevalence (Patchin & Hinduja, 2010) in terms of
prevalence from other previous studies. In addition, many recent systematic
reviews of the prevalence of cyberbullying vary greatly in studies due to two
important reasons, such as the prevalence of cyberbullying victimization,
inconsistency in measuring cyberbullying, and variability over time. The first
is the methods and techniques used by the researchers, and the other is the
time period in the reporting process. This study used cyberbullying
measurement from Patchin and Hinduja and showed that the prevalence of
cyber victims can range from 4.3% to 4.6%.
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The study also showed that the results did not show any significant
difference according to the school type variable and that the cyber bullying
behaviors were at a low level. In a study conducted in different secondary
schools and on students attending different (Baker & Kavsut, 2007) found
that there was no significant relationship between the school type variable
and cyber bullying and victimization. However, different results have been
obtained from other studies. Horzum and Ayas (2011) stated that the
students who are studying in Anatolian high schools and private high
schools display higher levels of engaging in cyber bullying than the students
attending other high schools.
Villora et al., (2020) have all found results that support these
findings. They stated that there is a negative relationship between subjective
well-being and different forms of victimization. Once the acceptance of
cyber-dating violence is checked, the university student aims to examine the
welfare differences between cyber dating harassment and bullying moguls.
The sample of the study found 1,657 university students studying in Spain,
who reported low victimization of bullying and low acceptance of dating
violence, and reported higher emotional, social and psychological well-being,
although the relationship between bullying and well-being was poor. In the
same study, there was no corral between the victimization of cyber dating
abuse and the dimensions of well-being examined.
Another result obtained from the study is that besides not displaying
any significant difference according to the Grade variable, the students had
low levels of cyber bullying behaviors regardless of grade. Similarly, a study
carried out on 336 students by Özdemir and Akar (2011) found that there
wasn’t any significant difference in the students' bullying behaviors
according to the Grade variable. However, different results have been
obtained from other studies. Firat and Ayran (2016) found that the results of
the research conducted on 6,625 students showed that the students had
significant differences in bullying behaviors according to the Grade variable.
It was noted that the frequency of cyber bullying behaviors increased as the
levels of Grades became higher. In their study Charalampous et al., (2020)
students in early high school classes showed a greater change in all types of
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bullying. In this study, despite a lower prevalence for high school students in
all types of bullying, he stated that fluctuations in education level were more
intense compared to primary school classes. On the other hand, primary
school students have shown that traditional bullying has a higher negative
effect on the victim. This result is also similar to other studies (Lebrun-
Harris et al., 2019). In their research on Korean students in 2020, Kim et al.,
(2020) have identified various risk groups that transcend offline and online
areas, confirming that incidents of bullying and victimization co-existed, as
suggested by general and routine activity theories. In the findings of the
study, he stated that it contributes to the emergence of victims of bullying
for both offline and online reasons, and that special anti-bullying programs
for the function of anti-social peer cohesion in virtual bullying are required
by offering recommendations for prevention and interventions related to
these roles. The result of the study's findings is similar to another study. In
the study conducted by Toraman and Usta (2018), 2% of secondary school
students stated that they were cyberbullies and that their total scores on the
scale of cyberbullying were not very high. A similar study, Bolat and
Korkmaz (2021), found that students in secondary school had low levels of
social media addiction and social media disorders in their cyberbullying
behaviors. Similar studies by Lindfors et al., (2012) and Aktepe (2013) have
shown that cyberbullying behaviors are lower, especially in the early stages of
puberty, but are increasing towards the middle of puberty. In addition,
Bányai et al., (2017) stated that the social media addictions of adolescents
were moderate. Another study conducted by Toraman and Usta (2018)
concluded that the majority of students know about the concept of
cyberbullying. These results support the relevant finding.
The study also concluded that bullying behaviors of students were
significantly different in relation to the school district variable. It was
observed that the students who were educated in schools in İskele had
significantly higher cyber bullying levels than other students in the other
districts (Nicosia, Kyrenia, Famagusta, Guzelyurt). It is believed that the
inadequacy of social activity facilities in the region force students to search
for new means of entertainment thus triggering the need to engage in cyber
Revista Românească pentru December, 2021 Educaţie Multidimensională Volume 13, Issue 4
257
bullying. When examined, the literature review shows that there haven’t
been any studies carried out to investigate the effect of the school district
variable on cyber bullying levels.
Lastly, it was established that as the weekly access duration to the
internet study increased, the increase in cyber bullying behavior levels were
statistically significant. Findings consistent to this finding are present in the
literature review. In a research conducted with 400 secondary school
students, Peker (2015) found that cyber bullying behaviors increased as the
duration of internet access increased. Similarly, in reference to the result
conducted on 269 students, Aricak et al., (2008) indicated that the duration
spent accessing the internet significantly factors in to the onset of cyber
bullying behaviors. However, different results have been obtained from
other studies. The results of a study conducted by Calisgan (2013), showed
that the Weekly Internet Access Duration variable was found not to be a
significant predictor of cyber bullying behaviors. Therefore, the study found
that the cyber bullying behaviors displayed by students did not show any
significant difference with respect to the weekly internet usage hours.
Recommendations
In light of the results obtained from the study, the following
recommendations have been made;
• Seminars to increase the awareness on cyber bullying behaviors
could be organized for students in secondary schools and vocational
secondary schools.
• Studies could be conducted to identify students at risk of engaging
in cyber bullying behaviors could be conducted. Once identified, these
students can receive group guidance and individual interviews to help deal
with their problem.
• Awareness activities, especially aimed at male students, could be
organized aimed at increasing understanding about what stage and level of
cyber bullying students are at.
An Analysis of the Attitudes of Secondary School Students on Cyber Bullying … Erinc ERCAG
258
• Incorporating content that emphasizes the importance of qualified
and purposeful internet usage behaviors into the rudimentary computer
courses in schools may help prevent cyber bullying behaviors.
• As the literature review proves that there is little data on other
variables; this research, in which the demographic variables have been
analyzed, may contribute to the development of new researches which
analyze other variables.
• The data used in the survey was collected from students attending
secondary, high school and vocational high schools. Further research on
students attending primary school and universities could be carried out.
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