Cyberloafing in Learning Environments Where Online Social Networking Sites Are Used as Learning Tools: Antecedents and Consequences

Abstract

The objective of this study is to construct a model which explains and predicts the relations of university students’ cyberloafing behaviors with demographic and academic variables at computing courses where online social networking sites are utilized for education and is to review whether there is longitudinal effect on these relations in terms of learning experience. This group of the study is composed of 171 university students. In this study, self-description form, two different success tests, and various scales are utilized as data collection tools. For the analysis of the data, structural equation modeling and multiple hierarchical regression analysis are utilized. The results of this study indicate that variables of information technologies usage status, online learning activities usage status, academic self-efficacy, motivation, and cognitive absorption are predictable of cyberloafing behaviors at Time 2 point, and cyberloafing behavior predicts academic success and academic procrastination at Time 2 point. Although the relations between various study variables and cyberloafing are not meaningful at Time 1 point, it is an interesting finding that these relations are statistically meaningful at Time 2 point.

Keywords

online social networking sites cyberloafing academic self-efficacy cognitive absorption motivation achievement goal orientation academic procrastination academic success

Introduction

Cyberloafing is defined as the use of the Internet and information technologies tools at work/school environment by individuals for personal purposes during work/school hours (O’Neill, Hambley, & Chatellier, 2014). In the studies on cyberloafing in education, it is mainly proposed that cyberloafing behavior in the classroom environment is associated with demographic variables, individual factors, and the use of information technologies (Akbulut, Dönmez, & Dursun, 2017; Gerow, Galluch, & Thatcher, 2010) and causes negative academic performance and academic procrastination acts (Wu, Mei, & Ugrin, 2018). It is expected that cognitive absorption (Agarwal & Karahanna, 2000), which is experienced deeply during practices with technology, the desire of individuals to achievement goal orientation (AGO), and academic self-efficacy (ACE) level will diminish the cyberloafing behaviors exhibited in educational environments. However, online learning environments have a great number of positive impacts on learning in terms of students’ academic success, motivation, participation, and interaction. Yet, what kind of obstacles cyberloafing behaviors generate in online learning environments needs to be studied.

Determining the priorities and consequences of using information technologies out of purpose in educational environments will provide important implications for the quality of teaching. Studies reviewing cyberloafing behavior in educational environments did not address the relationship between academic variables related to academic performance (motivationg, cognitive absorption, AGO, etc.) and cyberloafing. In addition, despite the studies conducted, academic antecedents of cyberloafing and its effects on performances of the students remain uncertain. This study was conducted in order to clarify the relationship among students’ use of online social networking sites (OSNS) as learning tool, cyberloafing acts, and academic performance in learning environments where OSNS are used as learning tool. In this respect, it is thought that they will make important contributions to the literature. As a matter of fact, the longitudinal effect is seen as a neglected element in most studies. When the literature on cyberloafing was reviewed, it was observed that usage period and experience of information technology tools are important variables (Baturay & Toker, 2015). However, in general, related literature is not much focused on variation of cyberloafing behaviors at education environments. Accordingly, the need to longitudinally review the applications run through social networking sites has emerged. In this context, two time points (Time 1 [T1] and Time 2 [T2]) were defined during the academic semester. How and based on what these time points were defined have been explained at Part 2. In addition, this study contributes to updating of the literature related with cyberloafing in education. From all these situations, the aim of this study is to create a model by reviewing the antecedents (gender, usage status of information technologies, usage status of OSNS as learning tool, ACE, cognitive absorption, motivation, and AGO) and consequences (academic procrastination and academic success) of university students’ cyberloafing behaviors who are trained in learning environments where OSNS are used. The research questions and hypotheses determined for this purpose are the following:

At T1 and T2 points, how are students’ activity levels of cyberloafing, ACE, cognitive absorption, motivation, AGO, academic procrastination, and academic success on online learning?

How is the relationship pattern between the variables determined as the antecedents and consequences of university students’ cyberloafing behaviors?

Hypothesis 1 (H1): In learning environments created with online social networks, gender positively predicts subsequent cyberloafing behaviors.

Hypothesis 2 (H2): In learning environments created with online social networks, daily information technology usage status (time and experience) positively predicts subsequent cyberloafing behaviors.

Hypothesis 3 (H3): In learning environments created with online social networks, online learning activities (frequency of performing online learning activities and time spent on online learning activities [weekly]) negatively predict subsequent cyberloafing behaviors.

Hypothesis 4a to 4d (H4a to 4d): In learning environments created with online social networks, ACE, motivation, cognitive absorption, and success intention negatively predict subsequent cyberloafing behaviors.

Hypothesis 5 (H5): Cyberloafing behaviors negatively predict subsequent academic success.

Hypothesis 6 (H6): Cyberloafing behaviors positively predict subsequent academic procrastination.

At the education application in which social networking sites are used are academic variables and online learning activities at T1 point the predictor of cyberloafing level at T2 point, and is cyberloafing at T1 point the predictor of academic success and academic procrastination at T2 point?

° HLE (Hypothesis of Longitudinal Effect): In the teaching practice where OSNS are used, the academic variables and online learning activities at T1 point are negative predictors of cyberloafing level at T2 point. Cyberloafing at the T1 point is the negative predictor of academic success and the positive predictor of academic procrastination at the T2 point.

Conceptual Background and Literature Review

Cyberloafing

Cyberloafing is defined as the use of the Internet out of purpose during working hours for personal browsing or e-mail (Lim, 2002). According to Kalaycı (2010), cyberloafing is the tendency or behavior of students to use the Internet for intentions that are not related to the lesson during class hours.

Individuals may suffer from stress at work environments due to various reasons. There are findings available both at positive and negative direction in the literature on the causes and the effects of cyberloafing behaviors. While in some of the studies in literature, there are negative results that cyberloafing decreases performance at work/education environments and prevent integration of information and communications technology (Ergün & Altun, 2012; Kalaycı, 2010; Karaoğlan-Yılmaz, Yılmaz, Öztürk, Sezer, & Karademir, 2015), in some other studies, on the contrary, the result shows that cyberloafing behaviors contribute to decrease in the stress of the individuals and the intentions of leaving work/education environments (Cihan, 2018; Gülduran, 2018). Niaei, Peidaei, and Nasiripour (2014) argue that cyberloafing plays an intermediary role in strengthening organizational commitment senses of the employees, and V. K. Lim (2005, p. 207) argues that as a result of cyberloafing behaviors, target-related usage of information technologies will become more innovative and will contribute to the improvement of Internet efficiency and creativeness. Vitak, Crouse, and LaRose (2011) indicated that cyberloafing behaviors may be beneficial for the promotion of creativeness, decrease of stress, increase of satisfaction, and improvement of psychological wellness. Coker (2013) emphasizes that interrupting the occupied thing apart from planned breaks increases productivity by positively effecting concentration of the individual. Accordingly, cyberloafing behaviors may contribute to performances of the individuals at educational environments, personal and academic developments, and this status may bring about results in favor of academic success. On the other hand, literature demonstrates that individuals will be more prone to cyberloafing behaviors on condition that cyberloafing behaviors are understood and accepted as a cultural norm (D’Abate, 2005). At cyberloafing behaviors, social effect and cultural norms are important. Accordingly, review of cyberloafing behaviors at different cultures is important. For this reason, this study’s review of cyberloafing behaviors at education environments in Turkey is thought to contribute to the literature within cultural context and to the expansion of the related nomological network.

Overview of the Research Works on Cyberloafing

When the related literature is reviewed, cyberloafing behaviors have been emphasized to be able to cause positive and negative results for academic environments and for businesses. However, in the related studies, it is emphasized that current studies are needed in order to understand causes of cyberloafing behaviors, factors contributing to this behavior, and its results.

Thus, variables related with cyberloafing and variations of the relations in it indicate the necessity of the new studies. To reach a better understanding of the structure related to cyberloafing and the variables identified as related, antecedent, and consequent, a nomological network is constructed within context of this study (see Figure 1).

Figure 1.

Nomological network on cyberloafing.

Cyberloafing in OSNS Used as Learning Tool

OSNS are websites which allow individuals to enter into social interaction in an electronic environment, to intervene in the content of ready-made web systems, and to share information (Scearce, Kasper, & Grant, 2010). OSNS are of interest to education because they facilitate collaborative work, enable social interaction to continue in electronic environment, and support different types of information sharing (Arnold & Paulus, 2010; Veletsianos, 2013). OSNS are environments that allow students to create and follow their own content and course sharing (Sánchez, Cortijo, & Javed, 2014). Previous studies emphasized that OSNS in the teaching process increase the academic success of students, facilitate learning (Becit-İşçitürk, 2012), improve interaction (Yildiz-Durak, 2019), increase the students’ motivation and positive attitudes toward courses (Chen, Hwang, Wu, Huang, & Hsueh, 2011), and increase their attendance to classes (Roblyer, McDaniel, Webb, Herman, & Witty, 2010). Therefore, the inclusion of OSNS in education will have many positive reflections on educational environments at all levels of education (Lim & Richardson, 2016). The benefits of these technologies in educational processes depend on the proper use of Internet technologies by students in a specific task (Beaudry & Pinsonneault, 2005).

Cyberloafing behaviors in education environments may vary based on learning environment, its physical and psychosocial conditions, study environment, and learning experience (Akbulut et al., 2017; Akbulut, Dursun, Dönmez, & Şahin, 2016; Kalaycı, 2010; Karaoğlan-Yılmaz et al., 2015; Yaşar & Yurdugül, 2013; Yildiz Durak & Saritepeci, 2019; Yilmaz & Yurdugül, 2018). According to Yildiz-Durak (2019), for acceptance and usage of learning environments, particularly in online learning environments, learning experience, technology usage self-efficacy, technology usage competences, and experiences are regarded as important. Therefore, review of the effect of learning experience in OSNS in terms of time period is needed. Within this context, two time points were determined. At the university where the study was carried out in Turkey, the semesters in the academic year are composed of two parts. At the end of the education lasting 7 weeks, there is exam interval for 2 weeks. Later on, education continues for another 7 weeks. And at the end of the second 7 weeks, exams are applied again. In this study, the application done at the end of the first 7-week part is determined as T1 and the application done at the end of the second 7-week education is determined as T2.

Antecedents of Cyberloafing

Demographic variables (gender) and cyberloafing behavior

Gender is the most discussed variables related to the use of technology in the literature, and according to these variables, technology usage behaviors of individuals may differentiate. When the studies on cyberloafing and gender were overviewed, possibilities of men’s taking part in cyberloafing behaviors and activities were found to be higher (Baturay & Toker, 2015; Dursun, Donmez, & Akbulut, 2018; Henle & Blanchard, 2008; Vitak et al., 2011; Woods, 2014). In addition, in the studies done, it was found that men, compared with women, use the Internet and social networking sites for purposes other than business during the working hours for longer periods or at least several times during the day (Andreassen, Torsheim, & Pallesen, 2014). In the light of all these information, gender is thought to be determined in cyberloafing behaviors displayed at social networking sites used as learning tool.

Technology usage status

When the literature was reviewed, it was observed that usage status of technology was used to explain the scope related to cyberloafing (O’Neill, Hambley, & Bercovich, 2014). The use of technology in work or education environments has advantages such as easy access to information, decrease of work load, and ease of communication (Ugrin, Odom, & Pearson, 2008). The results of the study conducted by Baturay and Toker (2015), show that improvement of Internet usage competence under technology usage status brings about cyberloafing behaviors to be observed in education environments more frequently. Thus, technology usage status is thought to be a possible predictor of cyberloafing behaviors.

Learning activities performed in OSNS and cyberloafing

OSNS offer many new forms of communication that allow faculty members and students to exchange information and ideas inside and outside the classroom (in different contexts of time and place; Yildiz-Durak, 2019). These environments therefore include multiple tools for the organization, exchange of information, and interactive aspects of the learning experience. Besides using mobile, OSNS offer opportunities for discussion boards, simultaneous chat, instant messaging, and tracking of class members (Godwin, Thorpe, & Richardson, 2008). In addition, the use of different types of teaching tools in OSNS and the use of different time periods of these tools will enable students to engage in learning (Yildiz-Durak, 2019). In this context, cyberloafing acts are thought to be at a lower level.

Academic self-efficacy

Bandura (1997) describes self-efficacy as the individual’s belief in his or her abilities to organize and carry out the actions necessary to achieve a particular goal. Self-efficacy is based on our belief in our talents, and a high level of commitment is necessary to organize and deliver a behavior necessary to achieve our goals (Schunk, 1991). ACE affects students’ academic interest, motivation, academic anxiety management, development of their cognitive competencies, and academic success (Zimmerman, 1995). According to Yildiz-Durak (2019), in the learning environments where OSNS are used, ACE affects the purpose and usage behaviors of the students’ usage of information technologies for education purposes.

Motivation

Motivation is an internal force that drives people to a target (Hanus & Fox, 2015). According to Kong and Song (2015), students’ motivation defines the mental status of students who are willing to attend to classes and shows that they are willing to learn. Motivated students are active in participating in classroom activities and more successful in comprehending the content (Giesbers, Rienties, Tempelaar, & Gijselaers, 2013). Investigating individual motivational differences may help to clarify and predict behaviors (Amabile, Hill, Hennessey, & Tighe, 1994). From this point of view, it is thought that motivation toward learning in learning environments will affect the use of OSNS. According to Yilmaz and Yurdugül (2018), motivation is an important variable in determining the levels of cyberloafing behaviors. Furthermore, motivation and cyberloafing are regarded as mutual variables in information technologies classes. Within this context, motivation levels are thought to negatively predict the cyberloafing behaviors in relation with how the environment is perceived and utilized by the students at lessons in which social networking sites are used.

Cognitive absorption

Cognitive absorption is defined as a deep commitment situation during the experiences with technology (Agarwal & Karahanna, 2000; Koçak-Usluel & Kurt-Vural, 2009). The state of deep commitment is expressed as the state of focusing time, curiosity, pleasure, control and attention on something. Koçak-Usluel and Kurt-Vural (2009) explained these concepts as follows: Therefore, it is thought that cognitive absorption levels will affect students’ use of information technologies out of purpose. As a matter of fact, Tanrıverdi and Karaca (2018) stated that in order to reduce or prevent cyberloafing activities in educational environments, it is important to determine the levels of cognitive absorption, which is among the social and psychological factors that are effective in such activities of students.

Achievement goal orientation

AGO refers to the strategies and orientations that an individual uses to achieve various activities (Akın, 2006). It was detected that individuals having high achievement orientation are more determined to achieve their goals, avoids procrastination behaviors to achieve their goals and are eager to spend time and effort to achieve their goals (Diehl, Semegon, & Schwarzer, 2006). According to Prasad, Lim, and Chen (2010), individuals who are overachievement oriented will divert their efforts to the direction of achieving their goals and by this way they will display less cyberloafing behavior compared with the individuals less achievement oriented. Due to the fact that individuals strongly believe in their skills to fulfill their tasks, emergence of their relations with cyberloafing and achievement orientation are possible. On the other hand, control theory depicted that achievement of targeted goals depends on the perception of preparedness to endeavor without losing time to achieve this goal (Diehl et al., 2006). Within this context, tendency of the students to be seen as successful by their friends, to avoid seeming unsuccessful or their true tendency to learn are thought to affect their cyberloafing behaviors in learning environments.

Cyberloafing Outcomes

Academic success

Current research on cyberloafing behaviors in educational environments is focused on the measurement of cyberloafing activity and its relationship to demographic variables, individual factors, and ICT use (Akbulut et al., 2016, 2017; Baturay & Toker, 2015; Gerow et al., 2010). Despite the emphasis in some past studies that cyberloafing has an impact on academic achievement, the effects of cyberloafing on students’ academic performance remain unclear. For example, Wentworth and Middleton (2014) and Junco and Cotten (2012) could find no relationship between the duration of technology usage and the academic grade averages. Wu et al. (2018) state that the main effect of cyberloafing behavior is lower performance in the classroom and lower academic success. The underlying condition is that cyberloafing behavior forces students to perform multitasking and this situation decreases time, energy, and commitment in the educational environment (Junco & Cotten, 2012). According to Ravizza, Uitvlugt, and Fenn (2017), the use of information technologies out of purpose in learning environments distracts the student and prevents the motivation for in-depth learning. Reducing cyberloafing behavior will enable students to participate in learning process more productively and more actively (Wu et al., 2018). Therefore, it is important to examine the relationship between cyberloafing and academic success.

Academic procrastination

Procrastination is a common concept expressing the postpone of the activities voluntarily or compulsorily despite the negative results caused by procrastination are known (Klingsieck, 2013). According to Tuckman (1991), procrastination is identified as “lack or absence of self-regulation performance. (p. 474)” Çok (2018) states that procrastination behavior is a cultural concept, and depending on level of development and cultural activities of the societies, procrastination behavior is perceived positively or negatively and its causes or effects are differentiated. Rothblum, Solomon, and Murakami (1986) indicated that procrastination has a complex structure and elements of the procrastination have emotional (situational anxiety and anxiety related to physical symptoms), cognitive (fear of failure, ignoring task, discordant thinking), and behavioral (indolence and not taking action on working behavior) scopes. Yildiz-Durak (2018) emphasized that behavioral scope (indolence and not taking action on working behavior) of procrastination is particularly reviewed very often in Turjey and negative perception is dominating academically. According to Odaci (2011), misuse of Internet and staying online for long periods at social media environments without any purpose cause individuals to delay academic tasks which they are supposed to achieve within a given period. Therefore, procrastination behavior is observed to be in relation with cyberloafing behaviors and academic performance. Academic procrastination is the delay of homework, educational tasks, studying for the exams, or responsibilities related to education, which are their academic responsibilities, until the last moment or postpone of the behavior by the students (Solomon & Rothblum, 1984). According to Tice and Bratslavsky (2000), individuals give up or avoid academic tasks for the sake of activities with short-term positive effect. In relation with the details of this process, Sirois and Pychyl (2013) say that emotional and behavioral states which may promote procrastination should be detected. Accordingly, highness of individuals’ self-control and highness of inner-control focus may prevent academic procrastination behaviors. Particularly self-regulation in online learning and inner-control skills of the student reflect on his or her academic behavior (Goulão & Menedez, 2014). Weaknesses of self-control and self-regulation are shown as a reason for emergence of cyberloafing behaviors (Çok, 2018). It is thought that there is a negative relation between cyberloafing behaviors and self-control, and academic procrastination behaviors emerge as a result of failure to achieve self-control.

Method

The aims of this study are to bring out a model which explains and predicts the relations between the determined variables in order to investigate cyberloafing levels in computing courses where university students use OSNS and to determine the antecedents and consequences of the cyberloafing behaviors.

Study Model and Hypotheses

The study model, variables, and the study hypotheses other than H_LE are shown in Figure 2.

Figure 2.

Default research model.

Study Group and Its Features

First- and second-year students studying in different departments at a university in Turkey during the fall semester of 2018–2019 academic year comprise the study group. The study group was selected by convenience sampling method. Features of the participants are presented at Table 1.

Table 1.

Demographics.

		n	%
Gender	Female	120	70.2
Gender	Male	51	29.8
	Min-Max	Mean	SD
Past information technology usage experience	1–14	5.95	2.706
Daily information technology usage duration	1–13	2.91	2.464

According to Table 1, among the participants, 70.2% were females and 29.8% were males. The participants’ past ICT age experience was about 6 years and daily ICT usage period was approximately 3 hours.

Data Collection Tools

Personal Information Form, two different achievement tests, and different data collection tools were used in this study.

Personal information form

This form was developed by the researcher. This form includes three subsections. In the first part, there are four items that include demographic information (gender, age, etc.). Demographic information was collected in T1. In the second part, there are five items related to the participants’ usage of information technologies (duration of information technology usage, frequency of use, etc.). During the development of this form, two field experts were consulted. Information on the use of information technologies was gathered in T1.

The last part includes 44 activities related to the learning activities carried out in OSNS, types of these activities, frequency of these activities, and total time spent for these activities. During creation of 44-item activity list, explanations on Edmodo features declared at Edmodo official website were referred. In addition, studies in which Edmodo media were utilized were reviewed. After draft item list was created, the items which cannot be used within scope of the course were detected. This activity list was presented to the opinion of an expert lecturer who actively utilizes this media during computing courses. Expert opinion was consulted on the fact that 44-item list was proper and sufficiently extensive. A list of all activities that can be carried out on the Edmodo social networking site is presented to the participants. The participants were asked to answer the questions in this section according to the applications. In addition, students sent reflection report on their activities related to the course to the responsible lecturer of the lesson at the end of each week. In this reflection, information on how many hours’ students planned to study for the related course and weekly how many hours they spared to this course, which of their activities they did and how many times they performed this activity during the related week takes places. Students sent this report to the lecturer of the lesson by private messaging through Edmodo at the end of each week. At the end of the process, correlation of the numbers presented by the students through weekly reflections and the numbers they indicated at T1 and T2 points were calculated. Correlation values, respectively, were .96 and .94 at T1 and T2 points. Highness of correlation values indicates that there is a consistency at self-reporting of the students. On the other hand, the reason for collecting these data from students in this part is due to the fact that Edmodo does not allow access to user logs. This part was applied at T1 and T2 points.

Cyberloafing Activities Scale

Developed by Blanchard and Henle (2008), this scale was adapted into Turkish by Kalaycı (2010). Revised by Yaşar (2013), the scale is in form of 5-point Likert type and consists of 23 items and 4 dimensions (individual, search, social, and news). In this study, Cronbach’s α reliability coefficient was calculated as .85 for T1 and .86 for T2 points. This scale was applied at T1 and T2 points. Examples from the items taking part in the scale are as follows:

I search for interesting (image, video, wise sayings, etc.) sites having no relation with the lesson through search engines.

I try to collect data on concepts having no relation with the lesson.

Computing course achievement test

To determine academic success levels of the participants, two achievement tests were developed by the researcher. To ensure validity and confidence of the achievement test, opinion of a field expert was asked. Based on the feedback from the experts and the scores of the students at previous applications, necessary amendments were done by evaluating values of item difficulty. Kuder-Richardson 20 confidence of the first achievement test was calculated and confidence coefficient was calculated as 0.79. This value exceeded recommended threshold value (Nunnally, 1967). Difficulty indexes of the first achievement test were between 0.25 and 0.89. Distinctiveness indexes of the items in this test were between 0.27 and 0.63. This achievement test consisting of 25 questions was evaluated on the scale of 100. Duration of the test was determined as 50 minutes. This achievement test was applied at T1 point. Kuder-Richardson 20 confidence of the second achievement test was calculated and confidence coefficient was calculated as 0.81. Difficulty indexes of the items in the test were between 0.30 and 0.90; distinctiveness indexes were between 0.21 and 0.54. This achievement test was also evaluated on the scale of 100. This achievement test was applied at T2 point.

Academic Self-Efficacy Scale

This scale was originally developed by Jerusalem and Schwarzer (1992). Turkish adaptation of the scale was performed by Yılmaz, Gürçay, and Ekici (2007). This scale was developed for university students and consists of seven items and one dimension. The scale is in form of 4-point Likert type. In this study, the Cronbach’s α reliability coefficient was .85 for T1 and .78 for T2. This scale was implemented at T1 and T2. Examples from the items taking part in the scale as follows:

I am always capable of achieving tasks to be done at my university education.

I know what to do very well in order to get good scores.

Motivated Strategies for Learning Questionnaire

This scale was originally developed by Pintrich, Smith, Garcia, and McKeachie (1993). It was adapted into Turkish by Büyüköztürk, Akgün, Özkahveci, and Demirel (2004). Developed for university students, the scale consists of 31 items and 6 subdimensions (intrinsic goal orientation, extrinsic goal orientation, task value, control beliefs, self-efficacy, learning and performance, and test anxiety). The scale is in form of 7-point Likert type. In this study, the Cronbach’s α internal consistency coefficient of the scale for T1 was found as .91. The Cronbach’s α internal consistency coefficient of the scale for T2 was found as .92. Examples from the items taking part in the scale are as follows:

I prefer course materials like this one which I believe that it would force me to truly work, by this way I can learn new things.

The most satisfactory thing for me is to get a good score in the class.

Cognitive Absorption Scale

This scale was originally developed by Agarwal and Karahanna (2000) in order to determine cognitive absorption levels of students while performing learning activities in web environments. Turkish adaptation of this scale was done by Koçak-Usluel and Kurt-Vural (2009). This scale consists of 17 items and 4 subdimensions. The scale is in the form of 10-point Likert type. In this study, the Cronbach’s α internal consistency coefficient of the scale was found as .88 for T1. The Cronbach’s α coefficient of the scale for T2 was found as .85. Examples from the items taking part in the scale are as follows:

When I log in web, mostly I spent more time than I planned.

While using web, I can prevent other things to distract my attention.

Achievement Goal Orientations Scale

This scale was originally developed by Midgley et al. (1998). The adaptation of the scale into Turkish was performed by Akın (2006). The scale aims to determine which goal orientations the university students have in case of a task or responsibility and to determine the relation of this goal orientation with other variables subject to the research. The scale consists of 17 items and 3 subdimensions (learning goal orientation: performance-approach goal orientation and performance-avoidance goal orientation). The scale is in the form of 5-point Likert type. In this study, the Cronbach’s α internal consistency coefficient of the scale for T1 was calculated as .76. The scale subdimensions’ reliability coefficients were found as .83, .87, and .71. Cronbach’s α internal consistency coefficient of the scale for T2 was found as .81. The scale subdimensions’ reliability coefficients were found as .89, .80, and .73. Examples from the items taking part in the scale are as follows:

I like school works very much which direct me to think.

It is important for me that other students in the class think that I am successful. Avoiding to look like I am not able to achieve my works is one of the most important goals of me.

Academic Procrastination Scale

This scale was developed by Çakıcı (2003). It consists of totally 19 items including the tasks which students are responsible to perform in their educational life such as studying, getting prepared for the exams, preparing projects. The scale is in form of 5-point Likert type. In this study, the Cronbach’s α internal consistency coefficient of the scale for T1 was found as .75. The Cronbach’s α internal consistency coefficient of the scale for T2 was found as .77. Examples from the items taking part in the scale are as follows:

I start to do my homework/projects so late that I am never ever able to finish in time.

I find myself listing excuses to my lectures because I do not do my homework/projects in time.

Process

Antecedents and consequences of cyberloafing behaviors of university students in computing courses where OSNS were used were analyzed cross-sectionally and longitudinally in this research. Course content in which OSNS were utilized was presented at Online Appendix. To test the relationships between these variables, data were collected twice from September 2018 to December 2018 at two time points. At the end of the first 7 weeks (T1) of this 14-week process, the first application was performed. At the end of 14th week, the second application was carried out.

At the end of the first 7 weeks, data collection tools were applied to the participants, and they were reapplied at the end of the 14th week. A total of 228 students participated in T1. In sum, 171 of these participants attended in T2. The reason for not having the same number of participants at two time points is that the participation was voluntary. The data of 171 participants who answered the data collection tools of this study twice (at T1 and T2) were used as the final research data. In other words, the data of the participants who did not respond to the data collection tool at both time points were excluded from the research.

Data Analysis

To determine the cyberloafing levels of university students and antecedents and consequences of cyberloafing, structural equation model (SEM) was used in the study as a model that describes and predicts relationships between the variables identified in the study. Based on determined two time points, longitudinal relations between study variables and cyberloafing behaviors were analyzed with linear multiple hierarchical regression. Data were analyzed with LISREL8.51 and SPSS18.0 programs.

According to Table 2, the model’s goodness of fit coefficients and allowable value ranges are given with references. Also, the fit indexes (χ², root mean square error of approximation, nonnormed fit index, normed fit index, and goodness-of-fit index) of the model was found to be significant at a 0.05 level.

Table 2.

Goodness of Fit Index in SEM.

Fit values	Good fit values	Acceptable fit values	Values reached T1	Values reached T2
RMSEA	0 < RMSEA < 0.05	0.05 < RMSEA < 0.10	0.032	0.006
NFI	0.95 ≤ NFI ≤ 1	0.90 ≤ NFI ≤ 0.95	0.97	0.99
NNFI	0.97 ≤ NNFI ≤ 1	0.95 ≤ NNFI ≤ 0.97	0.95	0.95
GFI	0.95 ≤ GFI ≤ 1	0.90 ≤ GFI ≤ 0.95	0.90	0.95

Note. RMSEA = root mean square error of approximation; GFI = goodness-of-fit index; NFI = normed fit index; NNFI = nonnormed fit index. Adapted from Schermelleh-Engel, Moosbrugger, and Müller (2003) and Tabachnick and Fidell (2007).

Multiple hierarchical regression analysis was performed to calculate the longitudinal effects of online social networking experience on cyberloafing and the relationship pattern of the model. The gender discrete variable in this study was included to the regression analysis by being encoded as “dummy variable.” The gender variable was in two categories as “women” and “men.” Category of “men” was encoded as “0” and turned into dummy variable. Continuous variables were included to the analysis with their original values. Data were reviewed while meeting the assumptions of linear multiple hierarchical regression analysis. Based on the data, relation between the predictor variables and the dependent variable is linear and scores display normal distribution. Furthermore, “multiple connectedness” was tested among the predictor variables. It is expected that variance inflation factor value related to independent variables is lower than 10 and its tolerance value is higher than 0.10; correlation values between variables are not higher than .80 level (Field, 2009). Calculated variance inflation factor values vary between 0.921 and 2.168. The relation between the dependent and the independent variables was reviewed with Pearson correlation analysis. The relation between variables was observed to vary between 0.000 and 0.559 and it was observed that there is not multiple connection problem between the variables.

Results

Findings on the First Question of the Study

Descriptive values of the levels of students at T1 and T2 points are presented in Table 2. To make the interpretation of the means of the factors and subfactors in Table 3, the values in the “arithmetic mean/item number(k)” column were used to make interpretation easier.

Table 3.

Descriptive Statistics of Variables.

		T1			T2
Scales	k	Mean	Mean/k	SD	Mean	Mean/k	SD
Cyberloafing	23	64.19	2.79	15.80	62.22	2.71	14.65
Individual	9	22.98	2.55	7.23	20.98	2.33	7.17
Search	4	8.97	2.24	3.02	8.15	2.04	3.09
Social	6	19.04	3.17	5.69	18.28	3.04	5.69
News	4	13.20	3.30	3.31	12.19	3.04	3.28
Academic self-efficacy	7	16.64	2.38	4.73	17.25	2.46	4.48
Cognitive absorption	17	136.86	8.05	23.83	118.18	6.95	28.85
Motivation	31	110.65	3.57	45.68	118.16	3.81	44.70
AGO	17	42.04	2.47	7.77	42.22	2.45	7.43
Academic procrastination	19	42.35	2.23	11.84	40.79	2.15	13.68
Academic success	20	76.18	–	17.38	78.33	–	18.62
OLA	22	81.18	3.69	46.54	80.09	3.64	43.53

Note: k = number of items; AGO = achievement goal orientation. The basic variables are highlighted as bold.

The students’ academic success levels in the data presented in Table 3 were evaluated on the scale of 100. An investigation of Table 2 gives that the achievement scores at T1 point (M = 76.18; SD = 17.38) are lower than T2 point (M = 78.33; SD = 18.62). Academic procrastination behaviors (M = 42.35; SD = 11.84) are higher than the ones in T2 point (M = 40.79; SD = 13.68). Considering the cyberloafing level scores of the students, it can be observed that the scores at T1 point (M = 64.19; SD = 15.80) are higher than the scores at T2 point (M = 62.22; SD = 14.65). It may be proposed that the students’ cyberloafing behaviors decreased toward the end of the learning process with regard to the initial use stage. ACE, motivation, and AGO behaviors are higher at T2, rather than T1. Cognitive absorption and online learning activities (OLA) are higher at T1, rather than T2.

Findings on the Second Question of the Study

SEM was used in order to test the hypotheses mentioned in the present research. Separate model analyses were performed by using T1 and T2 data. The SEM was created for both data collection points (T1 and T2), and the results are presented in Table 4 with hypothesis acceptance/rejection information.

Table 4.

SEM Coefficients and Hypothesis Acceptance/Rejection.

	T1			T2
Hypotheses	Standard coefficients	t	Acceptance/ rejection	Stand. coefficients	t	Acceptance/ rejection
H1	−.04	−.517	Rejection	−.06	−.777	Rejection
H2	.18	1.987*	Acceptance	.24	3.102**	Acceptance
H3	−.08	−.973	Rejection	−.22	−2.846**	Acceptance
H4a	.20	2.036*	Acceptance	.21	2.077*	Acceptance
H4b	.02	.187	Rejection	−.18	−1.987*	Acceptance
H4c	.03	.409	Rejection	.17	1.959*	Acceptance
H4d	.04	.468	Rejection	.027	.196	Rejection
H5	.07	.868	Rejection	−.30	−5.172**	Acceptance
H6	.19	2.001**	Acceptance	.23	2.876**	Acceptance

Note. SEM = structural equation model. Results with statistically significant values are emphasized as bold.

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

When coefficients of SEM presented at Table 4 were reviewed for T1 point, it was observed that the variable having the highest correlation coefficient (γ = 0.20) and the most important variable was ACE. Then, variable of information technologies’ usage status (time and experience; γ = .18) was observed as important. Prediction of gender, usage of OSNS used as learning tool (time and frequency), motivation, cognitive absorption, and AGO on cyberloafing is not meaningful statistically.

When the variables related to cyberloafing results were reviewed at T1 point, it was observed that the variable having the highest correlation coefficient (γ = .19) and the most important variable was academic procrastination. Prediction of cyberloafing on academic success is not statistically meaningful. At T1 point hypotheses of H2, H4a (partially) and H6 were accepted. When the variables related to antecedents of cyberloafing were reviewed at T2 point, it was observed that the variable having the highest correlation coefficient (γ = .24) and the most important variable was usage status of information technologies, then, respectively, was usage of OSNS used as learning tool (γ = − .22); ACE (γ = .21); motivation toward learning (γ = .19) and cognitive absorption (γ = −.18). When correlation coefficients were reviewed, predictor variables’ relative order of importance on cyberloafing was as “usage status of information technologies, usage status of OSNS used as learning tool, ACE, motivation toward learning, cognition absorption.” Prediction of gender and AGO on cyberloafing is not meaningful statistically. When the variables related to cyberloafing results were reviewed at T2 point, it was observed that the variable having the highest correlation coefficient (γ = −.30) and the most important variable was “academic success,” then was “academic procrastination (γ = .23).” Hypotheses of H2, H3, H4a, H4b, H4c, H5, and H6 were accepted at T2.

Findings on the Third Question of the Study

Linear multiple hierarchical regression analyses were performed to examine the longitudinal relationships between the cyberloafing predictors and their results. The results are presented in Tables 5 and 6.

Table 5.

Longitudinal Association Between Cyberloafing and Antecedent Variables at T2.

	Cyberloafing(T2)
	Step 1	Step 2	Step 3
Variables	Β (t)	β (t)	β (t)
ACE (T1)	.128 (1.420)	.109 (1.231)	.099 (1.118)
Motivation (T1)	.153 (1.680)	.154 (1.716)	.155 (1.731)
Cognitive absorption (T1)	.077 (1.026)	.044 (.599)	.047 (.637)
AGO (T1)	−.125 (−1.655)	−.105 (−1.390)	−.100 (−1.323)
Online learning activities-frequency (T1)		−.241 (−3.050)**	−.235 (−2.980)**
Online learning activities-time (T1)		.061 (.770)	.058 (.727)
Cyberloafing (T1)			.281 (3.194)**
ΔR²	.076	.050	.026
R ²	.076	.126	.152

Note. ACE = academic self-efficacy; AGO = achievement goal orientation. Results with statistically significant values are emphasized as bold.

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

Table 6.

Longitudinal Association Between Cyberloafing and Consequence Variables at T2.

	Academic Success (T2)			Academic procrastination (T2)
	Step 1	Step 2	Step 3	Step 1	Step 2	Step 3
Variables	Β (t)	β (t)	Β (t)	β (t)	Β (t)	β (t)
Cyberloafing (T1)	.073 (.947)	.082 (1.067)	.084 (1.091)	.50 (645)	.056 (.732)	.057 (.737)
Academic success (T1)		.181 (1.994) *	.183 (2.005)*		−.171 (−1.951) *	−.172 (−1.961) *
Academic procrastination (T1)			.015 (.202)			−.022 (−.283)
ΔR²	.005	.018	.006	.002	.010	.003
R ²	.005	.023	.029	.002	.013	.016

Note. Results with statistically significant values are emphasized as bold. *p < .05. **p < .01.

When the variables at Table 5 were reviewed, it was observed that among the variables at T2 point predicting cyberloafing level students, at the first step, explained 7.6% of the variance of academic variables which were included to the analysis, at the second step, this ratio increased to 12.6% with addition of variables related to online learning activities which was included to the analysis at the second step, and at the last step, this ratio increased to 15.2% together with the variables of cyberloafing at T1 point which were included to the analysis at the last step. Models 2 and 3 are meaningful. Model 1 (ΔR²= .076, R²= .076, p < .05) is the most important predictor for cyberloafing level of the students at T2 point. Based on the regression coefficient, predictor variables’ order of relative importance on cyberloafing level of the students at T2 point was cyberloafing (T1) and participation frequency to online learning activities (T1). When the t values were reviewed, it was observed that online learning activities at the second step and cyberloafing variable at T1 point at the third step were meaningful predictors of the cyberloafing levels of the students at T2 point. Therefore, HLE hypothesis in the study was partially accepted. In addition to this, when the direction of regression coefficients was reviewed, variable of “participation frequency to the online learning activities” was observed as negative.

When the values at Table 6 were reviewed, it was observed that among the variables predicting academic success level at T2 point, students explained 0.5% of the variance of cyberloafing at T1 which was included to the analysis at the first step, and this ratio increased to 2.3% with the addition of academic success (T1) variant which was included to the analysis at the second step and it increased to 2.9% with the addition of academic procrastination variable which was included to the analysis at T1 point at the third step. Models 2 and 3 are meaningful. Model 2 (ΔR²= .018, R²= .023, p < .05) is the most important predictor for academic success level of the students at T2 point. When the t values were reviewed, it was observed that academic success variable at Time point 1 at second and third steps was a meaningful predictor of the academic success level of the students at T2 point.

When the values at Table 6 were reviewed, it was observed that among the variables predicting academic procrastination levels at T2 point, students explained 0.2% of the variance of cyberloafing variable at T1 which was analyzed at first step, and this ratio increased to 1.3% with addition of academic success variable (T1) which was included to the analysis at the second step and it increased to 1.6% with addition of academic procrastination variable which was included to the analysis at the last step. Models 2 and 3 are meaningful. Model 2 (ΔR²= .010, R²= .013, p < .05) is the most important predictor for academic procrastination level of the students at T2 point. When the t value was reviewed, it was observed that academic success variable at T1 point at second and third steps was a meaningful predictor for academic procrastination level of the students at T2 point. Therefore, HLE hypothesis in the study was partially accepted.

Discussion

The aim of this study is to generate a model by examining the antecedents and consequences of cyberloafing behaviors of university students who are educated in the learning environments using OSNS. In this context, demographic variables, technology usage status, online learning activities, academic variables were determined as antecedent variables; academic procrastination and academic success were determined as consequence variables. According to the data collected at T1 and T2 points, six hypotheses were tested separately for both T1 and T2 points. Then, H_LE hypothesis and longitudinal predictor relations were examined. The research findings are summarized in Figure 3.

Figure 3.

Summary of research findings.

In this study, no statistically meaningful relationship between the gender and cyberloafing could be found at T1 and T2 (H1). Although there are studies showing that gender is not effective on cyberloafing (Akbulut et al., 2016; Askew et al., 2014), there are many studies that have the reverse findings. It is reported in the aforementioned studies that there may be differences due to differentiation of technology usage self-efficacy, interest, motivation elements, and purpose of use with regard to the usage of technology out of purpose by men and women (Baturay & Toker, 2015; Mcandrew & Jeong, 2012; Yildiz Durak & Saritepeci, 2019). The reason of not being able to reach to a meaningful relationship is thought to be due to the features of the study group. On the other hand, gender differences affect usage target of the media such as social networking sites and the emerging results (Yildiz-Durak, 2018). From this point of view, the use of OSNS in this study may be a factor that prevents the emergence of gender differences.

This study shows that there is a positive relationship between ICT usage experience and its daily usage period at T1 and T2 and cyberloafing (H2). Accordingly, the increase in users’ use of the Internet for noneducational purposes in their daily lives will increase the likelihood of cyberloafing behavior in the course processes. Based on this finding, it is probable that, as the ICT usage experience and daily usage period increases, students will tend to exhibit more cyberloafing behavior. Students with high ICT usage experience and a high daily usage period feeling more comfortable in online environments than others may be seen as a feature that increases their likelihood of display cyberloafing behavior (Yildiz Durak & Saritepeci, 2019). In support of this finding, Baturay and Toker (2015) and Keser, Kavuk, and Numanoglu (2016) concluded that participants with high Internet usage skills tend to display more cyberloafing behavior.

The present research indicates that there is no meaningful relationship between cyberloafing and online learning activities at T1, while there is a negative and meaningful relationship between them at T2 (H3). Any study in the literature, which directly handles the relation of online learning activities and cyberloafing, was not traced. The reason why there is no relation with cyberloafing at the T1 point, which is considered to be the initial use stage, may be due to the fact that students did not begin to use the environment comprehensively.

This research has revealed a positive relationship between ACE and cyberloafing at the T1 and T2 points, and therefore, the hypothesis has been partially accepted (H4a). In the study conducted by Gökçearslan, Mumcu, Haşlaman, and Çevik (2016), it was observed that self-efficacy positively affected cyberloafing and self-efficacy beliefs may intermediate cyberloafing activities in classroom environment. Also, in the study conducted by Prasad et al. (2010), similar results were reached and a medium-level positive relation between cyberloafing and self-efficacy was found.

On the other hand, the hypothesis on the relationship between motivation on learning and cognitive absorption and cyberloafing has been rejected at T1 and accepted at T2 (H4b, c). Motivation is one of the important factors affecting learning performance. Learning motivation of the students is related with the use of technology out of purpose such as cyberloafing (Çok, 2018; Hayıt & Dönmez, 2016). On the other hand, in the study conducted by Tanrıverdi (2017), a positive direction relation between cognitive absorption and cyberloafing activities of the students was found. Similarly, in the study conducted by Hayıt and Dönmez (2016), supporting these findings, they detected that there is a meaningful relation in positive direction between cognitive absorption levels of the university students and cyberloafing activities they did. From this finding, cognitive absorption may be reviewed as an important variable on prediction of cyberloafing behaviors.

Investigated within H4d hypothesis, the relationship between cyberloafing and the intention to be successful is not meaningful at T1 and T2 points. In a way supporting this finding, in the study conducted by Çok (2018), a relation between intention of academic success and cyberloafing could not be found. From this point of view, it may be suggested that the students do not perform academic activities in order to be appreciated or appreciated by others, or it may be suggested that they do not tend to perform learning tasks in order to avoid negative criticism from others. In this study, the relationship between cyberloafing and academic success was examined at T1 and T2 and a negative meaningful relationship was found at T2 (H5). This finding is generally supported in the literature (Wu et al., 2018). The study on cyberloafing in workplaces in the literature suggests that perceived results are related to cyberloafing behaviors (Cheng, Li, Zhai, & Smyth, 2014). If employees perceive cyberloafing behavior as a reward, they will be motivated to do cyberloafing (Cheng et al., 2014). On the contrary, if employees see negative consequences of cyberloafing and its risks worthy of consideration, their intentions to show cyberloafing behavior decrease (Moody & Siponen, 2013). Accordingly, in educational environments, students will exhibit cyberloafing behaviors less if they think that their expectations are met as an outcome of their learning activities. It is thought that cyberloafing does not show a meaningful relationship with the academic success as the students get to know the environment, course, and learning activities at T1 for the first time.

A positive meaningful relation between cyberloafing and academic procrastination on T1 and T2 points has been found (H6). An investigation of the literature revealed that a relation between problematic information technology usage behaviors and academic procrastination was found (Uzun, 2016). This is due to a decrease in the efficiency and academic performance of the students who spend most of their time in social media, which stems from the attractive nature of social media applications. Paul, Baker, and Cochran (2012) concluded that there was a negative relation between the time spent on Facebook by students and their academic achievement. Akdemir (2013) found a positive relation between the academic procrastination behaviors of students and the time spent in social media. As a result, it is observed that students who exhibit social media addiction have been constantly focusing on social media environments and that they are not able to spare enough time for their academic studies and tasks and postpone their ongoing academic studies. Therefore, students give up their academic duties in order to exhibit cyberloafing activities. The social networking site used in the study was expected to prevent this situation. However, it may be suggested that social networking sites used as learning tool are not effective against other social networking sites or other online activities.

At the education application in which OSNS were utilized, academic variables and online learning activities at T1 point are the predictors of cyberloafing level at T2 point, and cyberloafing at T1 point is partially the predictor of academic success and academic procrastination at T2 point (H_LE). In the studies conducted by Wang, Tian, and Shen (2013) and Yılmaz and Yurdugül (2018), it was emphasized that perceptions related to the environment and psychosocial variables affect cyberloafing intention. Within this context, the result show that as they gain learning experience, there will be changes in cyberloafing intentions and accordingly in cyberloafing behaviors of the students.

Limitations and Future Suggestions

There are some limitations in this study. First of all, there is a limitation in terms of participant profile because the percentage is not shared in balance among gender groups and gender is a study variable. Participants, despite the collection of anonymous data, may provide responses that are “social desirability” by notifying inadequate cyberloafing behavior and over-reporting learning activity performances. It may be proposed that this limitation is prevented by taking data from two time points, indicating a longitudinal effect. To collect unbiased data on students’ performance and levels of cyberloafing, future researchers may use environments where logs can be kept in which participants can record their learning performance. In addition, this research is limited to an application conducted within the scope of a course at a university in Turkey. For the generalization of the findings, it may be suggested to repeat the application in regions with different sociocultural structures. It may be useful for future researchers to verify the research model with data samples from various courses from different regions. In this study, data collection tools were collected at two time points. To overcome the limitations of cross-sectional studies, data were collected at different time points. However, it would have been helpful to determine the cyber activities of the participants before the study. Therefore, it is observed to be important that future studies collect longitudinal data at three or more time points over a longer period in order to verify the effectiveness of cause among variables.

Finally, although OSNS allow learning to be moved out of class, cyberloafing levels of the individuals having obstacle to access out-of-class information technologies will appear lower. On the other hand, there are studies reporting that out-of-class cyberloafing is useful for student’s get rid of study stress, burnout, and fatigue (Wu et al., 2018). In this respect, it is possible that students with high cyberloafing levels can actively participate in instructional activities within the classroom. Therefore, this study has a limitation as it does not distinguish the antecedents and consequences of in-class and out-of-class cyberloafing activities. Future studies may be suggested to examine the use of in-class and out-of-class technology in courses using online environments.

Conclusions

This study reviewed antecedents and consequences of cyberloafing in education environments by focusing on usage of OSNS in computing courses. The study tried to analyze cyberloafing behaviors in education environments, and longitudinally to what extend different time periods are effective in order to gain further information on antecedents and consequences of these behaviors.

Since it reviewed antecedents and consequences of cyberloafing behaviors within frame of academic variables’ relation and in an integrated way, this study expanded the nomological network related to these variables. In the study, with the aim of managing cyberloafing behaviors in education environments, interesting results which can be used by both the researchers and the applicators were reached. In the study, the result shows that usage status of information technologies, ACE, motivation, and cognitive absorption variables were predictor for cyberloafing behaviors at T2 point, and cyberloafing behaviors predicted academic success and academic procrastination behaviors at T2 point. While the relations between most of the study variables and cyberloafing were not meaningful at T1, it is a striking finding that these relations were statistically meaningful at T2. Furthermore, it can be said that learning experience gained through explanation of cyberloafing behaviors’ levels of users who uses social media as learning tool in education environments is important. Within this context, while taking precautions on cyberloafing behaviors not reaching to levels which will prevent education, educators need to pay more attention to the issues of students’ usage status of information technologies and their taking part in further interaction in online learning environment. Even there are differences statistically in terms of relations; role of ACE, motivation, and cognitive absorption in cyberloafing behaviors to emerge in online learning environments should be kept in mind. Negative direction relations between cyberloafing and academic success warn us in terms of taking precautions for the students who uses online social media as learning tool and cyberloafing behaviors not reaching to a level which will prevent education. Even though there are studies in the literature which prove the relation between various academic and demographic variables, any study which reviews this relation within frame of social networks in computing course and longitudinally was not traced. Finally, we were able to demonstrate that cyberloafing is related with demographic and academic variables, and demographic and academic variables may be the driving forces behind cyberloafing behaviors.

Supplemental Material

Supplemental material for Cyberloafing in Learning Environments Where Online Social Networking Sites Are Used as Learning Tools: Antecedents and Consequences

Supplemental Material for Cyberloafing in Learning Environments Where Online Social Networking Sites Are Used as Learning Tools: Antecedents and Consequences by Hatice Yildiz Durak in Journal of Educational Computing Research

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Hatice Yildiz Durak is a researcher in the Department of Computer Technology and Information Systems, Bartin University. She received her MSc and PhD degrees in Computer Education and Instructional Technologies from Hacettepe and Gazi Universities in 2011 and 2016, respectively. Her research interests include programming training in K12, use of technology in education, flipped learning, e-learning, teacher training, preservice and in-service training, educational use of social media, digital divide, Internet threats, and problematic technology use.

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