Use of Smartphones in Class: Examining the Relationship Between M-Learning Readiness, Cyberloafing, Nomophobia and Addiction Variables

The aim of this study is to examine the relationship between pre-service preschool teachers' mobile-learning (m-learning) readiness, cyberloafing, nomophobiaand smartphone addiction variables. The study was carried out using 306 pre-service preschool teachers undergoing education at state-run universities in two different cities in Turkey. The most critical conclusion of the study is that the smartphones used by pre-service preschool teachers both for m-learning and for cyberloafing in class have an impact on nomophobia and smartphone addiction. In the study, the effects of smartphone usage on both the learning environment and the individual were discussed in accordance with the proposed model. Suggestions for the use of smartphones were made in line with the study's conclusions.


INTRODUCTION
The Internet today is a communication network that connects people in different environments. Access to the Internet, which is the most important mass communication tool of the 21st century, is easily provided in higher education classes. It is a feature of the nature of education that educators want to benefit from the positive effects of using online technology in the classroom (Baker et al., 2012). In this context, mobile phones that provide online access to the Internet have become m-learning tools used in educational environments (Kukulska Hulme, 2005).
Pre-service teachers can use their smartphones as m-learning tools to make audio or video recordings of their lessons, to browse websites relating to preschool education, or for access to examine activities prepared for children and to use learning materials more effectively (Akkoyunlu, 2018;Nakamura et al., 2015). Smartphones have a critical effect on pre-service teachers' teaching methods and strategies, being used as educational material and for obtaining information and learning about their lessons (Almunawar et al., 2015;Dukic et al., 2015). Therefore, it is important that preservice teachers use smartphones in their fields of education as a m-learning tool (Abachi & Muhammad, 2014).
M-learning eliminates time and space constraints in the individual's learning process (O'Malley et al., 2005;Geddes, 2004). M-learning is not limited only to supporting learning by accessing information (Pachler et al., 2010). As a result of m-learning, the individual discovers their learning style (Shih et al., 2010); furthermore, their attitudes toward learning and their academic success improve (Chen, 2013;Cheon et al., 2012;Ciampa, 2014;Jaradat, 2014;Martin & Ertzberger, 2013) and their interest in and motivation for learning increase as well (Hwang & Chang, 2011). Just as there are advantages to m-learning, there are disadvantages too. According to Shudong & Higgins (2005), the disadvantages of m-learning are grouped under three headings technical, psychologicaland pedagogical. This study considers the psychological and pedagogical aspects of m-learning. Individuals' personality traits are considered the most obvious disadvantage in the psychological aspect of m-learning. The effectiveness of m-learning varies depending on the individual's personality traits (Sha et al., 2012). The second major disadvantage of m-learning concerns the pedagogical aspect. The problems associated with giving feedback to individuals practicing m-learning are a pedagogical disadvantage (Shudong & Higgins, 2005). According to Chu (2014); another pedagogical problem associated with m-learning stems from the design of the m-learning environment. In addition, it is thought that it is important for individuals to be psychologically ready and have m-learning skills in m-learning environments where the technical infrastructure is ready and that are pedagogically well supported (Kalelioğlu & Baturay, 2014). Accordingly, the level of mobile-leaning readiness is expected to be high so that m-learning can be carried out positively (Lin & Hsieh, 2007).
The technical infrastructure of the learning environment is also important in the development of m-learning readiness (Corbeil & Valdes Corbeil, 2007). However, considering the use of wireless Internet facilities and mobile technologies for students to access information in higher education classes, this creates an important environment for cyberloafing as well (Fried, 2008;Lauricella & Kay, 2010). The concept of cyberloafing mainly appears in the literature as not doing work in the workplace (Block, 2001;Greenfield & Davis, 2002;Lim, 2002;Griffiths, 2010;Anandarajan et al., 2014, pp. 4-6;Hassan et al., 2015), although in the field of education (McBride et al., 2006;King, 2007;Ergün & Altun, 2011;Kalaycı, 2010;Yaşar, 2013;Polat, 2018;Arıkan & Özgür, 2019) it has started being defined as activities other than learning carried out in the virtual environment during the learning process. In the study made by King (2007), it was determined that staff and students alike did cyberloafing using technological tools outside of class. In the study by Brubaker (2006), it was determined that students use computers to spend time on different online activities without their teachers' knowledge. Examining the relationship between cyberloafing and the learning process, Polat (2018) stated it is possible for students to cyberloaf in class if there is unplanned or excessive use of technology. Indeed, cyberloafing can be observed distinctly in higher education classrooms today.
The concepts that will explain the psychological aspect of problematic smartphone use and the dilemma between m-learning and the cyberloafing identified with smartphone use during the learning al., Olcay et al., 2018), education level (Nwagwu, 2001;Alsancak et al., 2016), age (MacCallum & Jeffrey, 2009) and Internet usage level (Alsancak Sarıkaya and Yurdagül, 2016) were examined. Advanced studies examining m-learning readiness also exist. Other factors that affect learning practices such as technological learning, identifying students' needsand the pedagogical benefits have also been identified (Cheung et al., 2011;Alzaza & Yaakub, 2011). The relationship with lifelong learning skills has also been examined (Gür Erdoğan et al., 2017). It has been determined that teachers' m-learning readiness is affected by their technical know-how, the awareness levelsand their motivation (Alzaza, 2012;Mahamad et al., 2010). However, no study was found that examined mlearning readiness, cyberloafing, nomophobiaand smartphone addiction at the same time.

Cyberloafing
When the literature on the concept of cyberloafing is examined, it is generally defined as workers using their place of work's Internet for personal use in fields that have nothing to do with their job (Robinson & Bennett, 1995;Friedman, 2000, Lim, 2002Griffiths, 2003;Zwass, 2006;Mahatakanoon, 2006;Bock & Ho, 2009). A person's use of mobile technologies at work for purposes other than work is also considered to be cyberloafing (Klotz & Buckley, 2013, p.125). According to Hassan et al (2015), it was determined that cyberloafing takes place under six categories. These categories are checking personal emails, investment and banking activities, social networks, traditional media, shoppingand pornography. According to Henle and Blanchard (2008), cyberloafing is divided into two. The first is the minor cyberloafing which is a term that you check e mails and visit entertainments at work. The second is serious cyberloafing which involves online gambling and spending time on pornographic sites. It has been observed that just as with working environments, providing access to the Internet via smartphones and laptops in learning environments also leads to the emergence of cyberloafing by using the Internet for purposes other than lessons (Fried, 2008;Lauricella & Kay, 2010;Laxman & Holt, 2017). According to Kalaycı (2010), cyberloafing in education is students' tendency or behavior toward using the Internet during the lesson for business not related to the lesson.
When the studies on cyberloafing in the field of education are examined, the study by Kalaycı (2010) carried out on 100 higher-education students showed that cyberloafing by students takes place mostly in the form of mail, discussion groups, blogs, bank transactions, shopping sites, auction sites, travel reservations, sports and news sitesand personal development sites. In the study by Brubaker's (2006) on 236 university students studying in higher education, it was found that during the lesson, students engaged in distracting, extracurricular activities such as surfing the Internet, playing digital games, texting, listening to musicand watching videos. The results of the study by Lauricella & Kay (2010) are similar to those in the study by Kalaycı (2010) and Brubaker (2006). A total of 137 highereducation students participated in the study by Fried (2008). According to the results of that study, it was reported that the use of technological tools during class for purposes other than learning resulted in learning deficiency and attention disorders. According to Wurst et al. (2008), cyberloafing resulted in a fall in student grade averages and academic success. According to the results of the study by Yaşar & Yurdagül (2013) on 215 higher education students, it was argued that cyberloafing may cause addiction.
When the studies on cyberloafing are examined, it is understood from the literature that negative situations do develop. However, it is argued that cyberloafing reveals inhibited creativity (Oravec, 2002), it helps the individual pull himself together when on a break (Polzer-Debruyne, 2008) and get away from the stressful environment (Ugrin et al., 2008) and that learning is supported by improving knowledge (Lavoie & Pychyl, 2001).
Since this study is going to provide an evaluation of the positive and negative features found in the literature together by examining the relationship between cyberloafing, m-learning readiness, smartphone addictionand nomophobia, it is thought it will make a contribution to the literature.

Nomophobia
Nomophobia is considered to be a disorder of contemporary digital and virtual society; it means discomfort, anxiety, tension, or suffering caused by the inability to have contact with a mobile phone or computer. It is the pathological fear of being separated from technology (Bragazzi & Puente, 2014). Furthermore, it is a health problem that negatively affects an individual's personality, selfesteem, worry, stress, academic performanceand other physical and mental health conditions; and that causes psychological problems, physical and behavioral changes (Rodriguez-Garcia et al., 2020). When we examine the population groups affected by nomophobia, it can be observed in university students (Bartwal & Nath, 2019;Farooqui et al., 2017) and other education levels, especially young people (Rosales-Huamani et al., 2019).
The study by Ferdous et al (2020) aimed to determine the perceived effects of mobile phone use by university students on their academic performance, their personal hygiene factorsand the psychological effects related to form of use. As a result of that study, it was determined that the majority of the participants stated that they used mobile phones in the toilet, they did not disinfect their phones, they used their phones until late at night, they put their phones under the pillow when going to bedand they stated that the use of mobile phones could prevent them from working. The results of the study also describe the characteristics of individuals who experience smartphone addiction and fear of being parted from their phone (nomophobia). The excessive and impulsive use of smartphones has produced many observable negative results in individuals. In particular, interpersonal communication and socialization processes are negatively affected Pavithra, et al., 2015;Park & Park, 2014;Billieux, et al., 2008).
According to Yıldırım & Correia (2015), what individuals using mobile phones experience if they stay away from the phone anxiety about not having access to information, being out of touch, being unable to communicate, or not feeling at ease is called nomophobia.
In the study by Rodriguez-Garcia et al (2020), the databases relating to nomophobia (Scopus and Web of Science) were scanned and the work systematically examined. As a result of the study, it was determined that current research is still in the discovery phase, mostly working on adolescents and university students. The review determined that studies looking into the prevalence of nomophobia were descriptive and cross-sectional. When studies investigating the prevalence of nomophobia were examined, it was seen that in the study by Cheever et al. (2014) on 163 university students in the United States, half of the participants were asked to turn off their mobile phones and the other half were asked to hand over their phones. The anxiety levels of the participants were measured during their stay away from their phones and it was seen that their anxiety increased over time. The study by Tavolacci et al. (2015) in France investigated the prevalence of nomophobia using 760 college students. According to the results of that study, it was determined that almost one-third of the students suffered from nomophobia. The study conducted in Turkey by Adnan & Gezgin (2016) investigated the prevalence of nomophobia using 433 college students and determined that the students had higher than average levels of nomophobia.
According to Dixit et al. (2010), when an individual whose nomophobia level is above average is away from their phone or if their phone's battery dies, or if they are outside of coverage, their anxiety levels increase. As the level of anxiety rises, it becomes difficult for the individual to carry out their daily business. In this context, it is important to identify the relationship between nomophobia, which causes increased anxiety levels in university students, who extensively use smartphonesand m-learning readiness, addictionand cyberloafing.

Smartphone Addiction
Smartphone addiction is defined as when an individual feels disconnected and offline when they don't use a smartphoneand is anxious, irritableand angry when they have no smartphone (Duke & Montag, 2017). There is empirical evidence that most young people with smartphone addiction prefer to communicate with others over their smartphones, because of their shyness and insecurity (Walsh, White ve Young, 2007 ). Kamibeggen and Sugiura (2005) found in their study that high school students use smartphones to make friends and check their e-mails. High school students who use smartphones sleep late and cannot wake up early. The students stated that they could not survive without a smart phone.
In the study by Shahrestanaki et al. (2020) investigating the relationship between university students' smartphone addiction and their quality of life, it was reported that smartphone addiction was more common in women and in married studentsand that it negatively affected their quality of life in physical, mentaland social terms.
Chen (2020) collected data from a large sample of 2,000 adolescents and young adults from three universities in Taiwanand explored the relative contribution of both psychological and social factors in predicting different usage levels of a social mobile application (LINE), which is very popular in Asia. In light of the findings obtained, he concludes that people who are lacking in selfesteem and social skills, but who seek the approval of others and a sense of belonging, are more likely to develop an addition to this app. It is also stated that people who used LINE intensively without it becoming a problem are quite happy with their livesand that this is the result of them not caring about living their lives according to others' expectations and because they have high self-esteem (Chen, 2020).
The study by Lepp et al. (2013) on 496 university students found a negative relationship between the use of mobile phones and academic success, but a positive one with anxiety. Investigating the effects of smartphone addiction in the teaching process, Roberts et al. (2014) emphasized that they observed academic failure in individuals as well as students becoming disconnected from classroom activities and quitting studying. Related studies have also determined that smartphone addiction adversely affects academic achievement (Kibona & Mgaya, 2015;Olufadi, 2015).
Studies showing a relationship between smartphone addiction and both nomophobia and cyberloafing were also found in the literature. On examining these studies, it was found that Yaşar & Yurdagül (2013) reported a significant statistical relationship between cyberloafing and addiction in a study conducted with 215 higher-education students. Gökçearslan et al. (2016) in their study, found a relationship between cyberloafing and smartphone addition. According to the results of their study, cyberloafing is a significant predictor of smartphone addiction. The study by Semerci (2019) determined that there is a relationship between nomophobia and smartphone addiction. According to the results of that study, nomophobia is the most important predictor of smartphone addiction. In this context, cyberloafing and nomophobia are thought to be important variables for smartphone addiction. A review of the literature found no study investigating the relationship between smartphone addiction and both nomophobia and cyberloafing.

THE STUDY'S MODEL AND HYPOTHESES Smartphone Cyberloafing-M-learning Readiness
When the relationship between the m-learning readiness of students who do cyberloafing in class was examined by Tindell & Bohlander (2012), they determined that 95% of university students have their smartphones with themand that 92% of them do cyberloafing in class by texting. In the study by Jacobsen & Forste (2011), it was found that 96% of university students have a smartphone and that 62% of them use the phone for cyberloafing in class. In the study by Berry & Westfall (2015) it was determined that 37.6% of university students tend to check their mobile phones one or two times during their lessons. In the study by Gözüm et al. (2019), it was determined that there is a significant and positive relationship between m-learning readiness and cyberloafing. In the study by Varol & Yıldırım (2017), it was determined that cyberloafing takes place in environments where mlearning is carried out. Studies have been found advocating that cyberloafing has a positive effect on creativity, stress avoidanceand learning (Oravec, 2002;Polzer-Debruyne, 2008;Ugrin et al., 2008;Lavoie & Pychyl, 2001). A review of the literature found a significant relationship between m-learning readiness and cyberloafing. Accordingly, the following hypothesis is proposed: H 1 : Smartphone cyberloafing in class (SPCSC) has a significant effect on m-learning readiness (MLRS).

Smartphone Cyberloafing -Smartphone Addiction
When the relationship between the smartphone addiction levels of students cyberloafing on their smartphones in class was examined, Kalaycı (2010), reported that students cyberloafing in class were using social media. Examining the relationship between smartphone addiction and social media use, (Roberts et al., 2014;Salehan & Negahban, 2013) reported a relationship between smartphone addiction and the emergence of cyberloafing (Rozgonjuk et al., 2018). There also exist studies examining the relationship between cyberloafing and Internet addiction (Demir & Seferoğlu, 2016;Keser et al., 2016). According to the literature review, there is thought to be a significant relationship between cyberloafing and smartphone addiction. Furthermore, according to Gökçearslan et al. (2016), cyberloafing causes smartphone addiction. Accordingly, the following hypothesis has been proposed, in view of the literature: H 2 : Smartphone cyberloafing in class (SPCC) has a significant effect on smartphone addiction (SA).

M-learning Readiness -Smartphone Nomophobia
When the relationship between m-learning readiness and nomophobia is examined, Spitzer (2015), who reviewed the results of 22 studies carried out in different countries, highlighted the risks that could arise if smartphones are used in educational environments. Some of these risks involve the possible development of anxiety and addition in students. In the study by Ak & Yıldırım (2019) with the participation of 146 university students, a positive and significant relationship was found between nomophobia and attitudes toward m-learning. A study involving 104 university students was conducted by Davie & Hilber (2017). As a result of that study, they pointed out that the use of mobile technologies in m-learning environments can cause nomophobia. They also proposed an investigation into what the mobile devices used by students in m-learning environments might cause. Accordingly, it is thought that the detection of a significant relationship between m-learning readiness and nomophobia will contribute to the literatureand so the following hypothesis has been proposed: H 3 : M-learning Readiness (MLR) has a significant effect on nomophobia (NMP).

Smartphone Nomophobia-Smartphone Cyberloafing
When studies on the relationship between smartphone cyberloafing and nomophobia in class were examined, it was seen that those university students prone to nomophobia tend to check their phones during the day (Akıllı & Gezgin, 2016) and it was determined that students with high levels of nomophobia use their smartphones more (Erdem et al., 2017). Şumuer et al. (2019) reported that among the factors leading to the use of mobile devices for extracurricular purposes, nomophobia was a significant predictor. Accordingly, the following hypothesis has been proposed in light of the relationship found in the literature between cyberloafing and nomophobia: H 4 Nomophobia (NMP) has a significant effect on smartphone cyberloafing in class (SPCC).

Smartphone Nomophobia-Smartphone Addiction
When studies examining the relationship between nomophobia and addiction were examined, the study by Durak (2019) reported a significant relationship between nomophobia and smartphone addiction in adolescents. The study by Semerci (2019), examined the relationship between smartphone addiction and nomophobia in secondary school students. As a result of the study, it was determined that there is a moderately significant relationship between nomophobia and smartphone addictionand that nomophobia is the strongest predictor of smartphone addiction. Accordingly, the following hypothesis has been proposed in light of the relationship between nomophobia and smartphone addiction reported in the literature: H 5 : Nomophobia (NMP) has a significant effect on smartphone addiction (SA).
It has been determined in the literature that there are significant relationships between the variables of addiction, nomophobia, cyberloafingand m-learning readiness in connection with preservice preschool teachers' use of smartphones during bachelor degree education. In this context, a model proposal to determine the relationships between the variables of the study is presented (see Figure 1). The aim is to answer the research questions by testing the hypotheses identified in accordance with the model proposal.

Fig1. The proposed structural model
In this model, the relationships between the variables as shown by one-way arrows form the hypotheses for the study (see Figure 1).These hypotheses are as follows:

METHOD Participants
The research was carried out with 306 preschool pre-service teachers who were studying in the same departments at two state-run universities in the Eastern Anatolia Region of Turkey and selected using the purposeful sampling method. All of the participants stated they used smartphones in class. The distribution of the demographic characteristics of the participants is given in Table 1. According to the demographic characteristics in Table 1, 75.8% of the participants are female and 24.2% are male. Of the pre-service teachers participating in the study, 33.7% were in their first year, 29.1% in the second year, 20.9% in the third yearand 16.3% in the fourth year. More than half of the pre-service teachers (54.3%) were in the 19-21 age group.

Procedure
The data were collected from forms given by the researchers to the pre-service teachers in a classroom environment. The aim of the study was explained to the participants. Volunteers were asked to participate in the study. To protect privacy, the data shared by the participants were collected only by the researchers. The form given to the pre-service teachers took approximately 35 minutes to complete. Practice forms were given to six pre-service teachers to check the comprehensibility of the scale forms.

Instruments
In the study a data collection form consisting of two sections was used.At the start of the form, the question "Do you use a smartphone in class?" was asked. The participants who answered yes to this question were asked to answer the statements in the first and second sections. The first part of the form contained questions about demographic characteristics (age, gender, year of education). The second part included the m-learning readiness scale, smartphone cyberloafing in class scale, smartphone nomophobia scaleand the smartphone addiction scale. The forms used to gather research data had been adapted to Turkish for use with students undergoing education in Turkey. The data collection tools used in the study are described below, respectively.

Mobile Learning Readiness Scale
The Mobile Learning Readiness Scale developed by Lin et al. (2016) was adapted to Turkish culture by Gökçearslan et al. (2017). Lin et al. (2016) and known as MLR was applied to 319 participants in Taiwan. Of the participants, 60% were university students. The scale was prepared in the form of a seven-point Likert scale. The levels of agreement on the scale range from (1) "Strongly Disagree" to (7) "Definitely Agree." As a result of the construct validity studies of the scale, a scale form consisting of 19 items grouped under three headings "self-efficacy,""optimism,"and "Selfdirected learning" was created. With its three-factor structure, the scale explained 68.40% of the total variance. The Cronbach alpha value for the entire scale was found to be 0.93.

The original form developed by
The form as adapted to Turkish by Gökçearslan et al. (2017) was applied to 698 university students in Turkey. As a result of the construct validity studies for the scale, it consists of three subscales and 17 items. When the sub-scale structure of the scale is analyzed, the first sub-scale (optimism) consists of seven items, the second sub-scale (self-efficacy) consists of six itemsand the third sub-scale (self-directed learning) consists of four items. As a result of the Confirmatory Factor Analysis (CFA) performed to test the construct validity of the scale, the fit indices were calculated as [χ2(698)=359.73; (sd=108, p.=0.0000); χ2/sd=3.33; RMSEA=0.058, GFI=0.94, AGFI=0.92, IFI=0.98, CFI=0.98and NFI=0.97]. It was determined that the total variance rate explained by the scale was 76.9%. In the reliability study of the scale, the internal consistency coefficient Cronbach alpha value was found to be 0.95.

Smartphone Cyberloafing Scale in Classes
The SPCSC developed by Blau et al. (2006) was adapted to Turkish culture by Polat (2018) and called the "Cyberloafing Scale." The original SPCSC data collection tool developed by Blau et al. (2006) was applied to 267 participants to determine employees' cyberloafing levels. As a result of the construct validity tests for the scale, 16 factors were identified in three sub-scales, namely, "browsing-related cyberloafing," "non-work-related cyberloafing,"and "interactive cyberloafing." It was determined that the total variance rate explained by the scale was 51.3%. The Dynamic Factor Analysis (CFA) values of the three-factor structure were calculated as X 2 =311.47; df=82; CFI=0.94; AGFI=0.90; RMS=0.032; RMSEA=0.061.
The Turkish adaptation scale developed by Polat (2018) was applied to 217 university students in Turkey. The participants in the study are studying Turkish, Social Studies, Preschool and Classroom teaching. In the Turkish version of the scale, in order to measure the level of cyberloafing in lessons, the expression "From my smartphone in class…" was added to each item taken from the original scale. The scale was prepared in the form of a six-point Likert scale. The agreement levels on the scale range from (1) "Never" to (6) "Always." As a result of the construct validity for the scale, it was determined that 16 items taken from the original form were grouped in a three-factor structure. However, it was determined that items from the original form were not in the same factor. The subscales that emerged as a result of construct validity were named "Browsing-related Cyberloafing," "Interactive Cyberloafing," and "Entertainment Cyberloafing." As a result of the CFA performed to test the construct validity of the scale, the fit indices were calculated as [χ2 (217) = 237.794; (sd=101, p <0.001); χ2/sd=2.35; GFI=0.86, RMSEA=0.085, RMR=0.132, SRMR=0.000, NNFI=0.86, CFI=0.88]. It was determined that the total variance rate explained by the scale was 56.6%. In the reliability study of the scale, the internal consistency coefficient Cronbach alpha value was found to be 0.88.
The original form of the data collection tool (NMP-Q) developed by Yıldırım & Correia (2015) was applied to 300 university students. The questionnaire is a seven-point Likert scale. The levels of agreement on the scale range from (1) "Strongly Disagree" to (7) "Definitely Agree." As a result of the construct validity tests for the scale, a form was created comprising 20 items under four sub-scales, namely, "not being able to access information," "giving up convenience," "not being able to communicate," and "losing connectedness." With its four-factor structure, the scale explained 69.58% of the total variance. The Cronbach alpha value for the entire scale was found to be 0. 94. The original data collection form (NMP-Q) was converted into Turkish by Yıldırım, et al. (2016). In the process of translating from English to Turkish, expert opinion was sought and small adjustments made accordingly. The form adapted to Turkish was applied to 306 university students studying in Turkey. As a result of the CFA performed to test the construct validity of the scale in Turkish culture, the fit indices were calculated as [χ2 (164) = 469.90, χ 2 /sd=2.86, CFI=0.92, RMSEA=0.08]. In the reliability study of the scale, the internal consistency coefficient Cronbach alpha value was found to be 0.92.

Smartphone addiction scale
Developed by , the Smartphone Addiction Scale (SAS) was adapted to Turkish culture by Şata et al. (2016). The SAS data collection tool was developed by  with the participation of 197 participants aged 18 to 53. The original form of the scale is a sixpoint Likert scale. The levels of agreement on the scale range from (1) "Strongly Disagree" to (6) "Definitely Agree." As a result of the construct validity studies for the scale, 33 items were identified grouped under three sub-scales named "daily life disturbance," "positive anticipation,""feeling of withdrawal,""cyberspace-oriented relationship," and "overuse and tolerance." It was determined that the total variance rate explained by the scale was 61%. The Cronbach alpha value for the whole scale was found to be 0.96.
The original (SAS) data collection tool was converted into Turkish by Şata et al. (2016) and applied to 456 high school students. Qualitative methods were used when converting the original form into Turkish. CFA was applied to determine the validity and reliability scores for the Turkish form obtained as a result of expert opinion. When the CFA results are examined, the results [χ2 (217) =1,220.11; (sd=486, p <0.001); χ2/sd=2.51; RMSEA=0.058, GFI=0.84,AGFI=0.82,NNFI=0.96,CFI=0.96] show that it is suitable for Turkish culture. The Cronbach alpha value for the entire scale was found to be 0. 935.

Instrument Validity and Reliability
Validity and reliability analyses of the scales used in this study (MLRS, SPCSC, NMP-Qand SAS) were made using the data set consisting of pre-service preschool teachers. Table 2 CFA results revealed the fit index values and internal consistency coefficient. The criteria accepted for fit values in the literature are given in section assumptions of model suitability. Accordingly, when the fit index values accepted as criteria in section assumptions of model suitability of the article are compared with the model's overall fit values without modification suggestions being made, the model fit index values are considered to be acceptable values.

Table 2. Data Collection Tools fit index and internal consistency values
Cronabach Alpha (α) values were analyzed for the internal consistency coefficient of the data collection toolsand determined to be: Internal consistency coefficient of MLRS (α=0.940), the internal consistency coefficient of the SPCSC (α=0.886), SAS internal consistency coefficient (α=0.934), the internal consistency coefficient of NMP-Q (α=0.923). Accordingly, the data collection tools used in the research (MLRS, SPCSC, NMP-Qand SAS) are valid and reliable.

Investigating assumptions
Before analyzing the dataset, the missing data, sample volume, univariate and multivariate normality, extreme valuesand multiple linearity values were examined (Tabachnick & Fidell, 2013). It was determined that there were no missing data in the dataset. According to Kline (2016), the observation parameter for the sample volume should be at least (10:1). The sample volume of the research meets this rate. In the analysis of univariate normal values, the kurtosis and skewness coefficients were found to be within normal values. Using scatter diagrams for the variables, it was determined that linearity was met. It was determined that the multicollinearity and singularity checks for the correlation matrix were significant. Variance Inflation Factor (VIF) and Tolerance (T) and Conditional Index (CI) values were checked to analyze the multiple linearity problem (Tabachnick and Fidell, 2013). It was determined that the VIF value was less than 10 (Myers, 1990), the T value was different from 0 (Menard, 1995)and the CI value was less than 30 (Paul, 2007). Accordingly, it was determined that there was no multiple linearity problem.

Assumptions of model suitability
CFA was applied to the dataset of the validity studies for the data collection tools used in this study. The fit indexes determined to evaluate the results of CFA and path analysis in the study are as follows: The ratio of chi square value to degree of freedom ( 2⁄ .), Root Mean Square Error of Approximation (RMSEA), Normed Fit Index (NFI), Comparative Fit Index (CFI), Goodness of Fit Index (GFI), Adjustment Goodness of Fit Index (AGFI)and Incremental Fit Index (IFI). According to the literature, the good and acceptable fit value ranges of the determined fit indices are given in Table  3.  (Marsh & Hau, 1996)

Analysis Method
Structural Equation Modeling (SEM) analysis is a multivariate regression model that reveals causal relationships between observed variables (Kline, 2016). SEM is the examination of covariance among observed variables to make inferences about latent variables (Schreiber et al., 2006). SEM analysis offers an opportunity to examine the cause and effect relationship of the variables in mixed hypotheses relating to proposed models. The data obtained from the model research were analyzed using path analysis. The results obtained from the analysis were evaluated according to the model fit assumptions and various good fit indices. The AMOS 23 program was used for CFA and path analysis; the SPSS 21 program was used for dataset entry, item statisticsand test statistics.

RESULTS
In the findings, the relationships between the research variables are given first, then the results of the fit of the measurement modeland finally the path analysis values for the model. The relationship between the variables forming the model of the research and descriptive statistics are given in Table 4.  Table 4 shows a significant relationship in the relationships between m-learning readiness, smartphone cyberloafing, smartphone nomophobiaand smartphone addiction (p <0.01). In addition, it was determined that the kurtosis and skewness values, which are indicators for the normal distribution of variables, lie between -1 and +1. In light of the results of this analysis, other analyses were begun.
Model fit indexes for testing the structural equation model proposed in the research are shown in Table 5. The assumption of the suitability of the model fit values is given in section assumptions of model suitability of the article. Accordingly, when the model fit values of the research are analyzed according to Table 5, since (χ2=4.027; df=1; p=0.045) χ2/sd=4.027 is less than 5, this indicates an acceptable fit. The RMSEA value (RMSEA=0.069) shows an acceptable fit. The NFI value (NFI=0.987) shows a good fit. The CFI value (CFI=0.990) shows a good fit. The GFI value (GFI=0.972) shows a good fit. The AGFI value (AGFI=0.935) shows an acceptable fit. The IFI value (IFI=0.935) shows a good fit. When Table 5 is examined, it is understood that the fit index values of the model are good fit values. Accordingly, the model fit values support the acceptability of the model. The AMOS output showing the parameters in the model is shown in Figure 2.

Fig 2. Path analysis results of the model's standardized estimates values
Path analysis was applied to test the hypotheses established between smartphone addiction, nomophobia, cyberloafingand m-learning readiness, which are the variables of the study. Path analysis results are given in Table 6. The variables in the model (see figure 2) explain (R 2 =0.48) 48% of the variance in smartphone addiction explains; (R 2 =0.14) 14% of the variance in nomophobia; (R 2 =0.14) 14% of the variance in smartphone cyberloafing in class; and (R 2 =0.05) 5% of the variance in m-learning readiness.

DISCUSSION
The aim of this study is to determine the relationships between the variables of preschool teachers' m-learning readiness, smartphone cyberloafing, smartphone nomophobiaand smartphone addiction. Taking the relevant literature as a guideline, a model was created and five hypotheses were tested.
The amount of smartphone use and the availability of Wi-Fi access occupy important places in students' choice of location today (Bicen & Arnavut, 2015). So, university classrooms offer the highest level of Wi-Fi access. At this point, just as Wi-Fi access in class can be used by pre-service preschool teachers to increase m-learning readiness, it can also be used for cyberloafing. However, this study's topic of discussion is smartphone addiction and nomophobia, which are caused by m-learning readiness or cyberloafing.
According to the results of the study, (H 1 ) says an increase in smartphone cyberloafing in class leads to an increase in m-learning readiness. It is stated in the literature that cyberloafing has positive effects on personal development opportunities, productivity, stress avoidanceand learning (Anandarajan et al., 2004;Oravec, 2002;Polzer-Debruyne, 2008;Runing Sawitri, 2012;Ugrin et al., 2008;Lavoie & Pychyl, 2001;Yaşar & Yurdugül, 2013). This being the case, the fact that cyberloafing increases m-learning readiness is an extremely important matter. Some studies in the literature (Askew, 2012;Bağrıaçık Yılmaz, 2016;Karaoğlan Yılmaz et al., 2015;Samaha & Havi, 2016) argue that the use of smartphones and cyberloafing may negatively affect the quality of education and prevent learning. In addition, it was found in the literature that cyberloafing leads to Internet addiction (Demir & Seferoğlu, 2016;Keser et al., 2016)and smartphone addiction (Gökçearslan et al., 2016). According to the results of the study, an increase in smartphone cyberloafing in class (H 2 ) causes an increase in smartphone addiction. This being so, the study reached both the conclusions seen in the literature. However, when the effect of cyberloafing on m-learning readiness (β=0.13) is compared with its effect on smartphone addiction (β=0.23), it is seen that the effect on smartphone addiction is greater. As a result of this study, it is thought that smartphone addiction may lead to serious consequences in terms of personal health that are just as important as the effect that smartphone cyberloafing in class has on the quality of education.
M-learning readiness as a result of cyberloafing was considered to be positive feature when this discussion was first undertaken. However, there are studies that highlight the negative aspects of m-learning. Spitzer (2015) argued that using a smartphone in m-learning environments entails risks. One of the risks that emerge when m-learning environments are not planned properly is smartphone nomophobia. According to the results of the research, (H3) says that m-learning readiness directly and positively affects smartphone nomophobia. The results of this study support the conclusion by Davie & Hilber (2017) that m-learning may cause nomophobia. In addition, the level of relationship between the variables of m-learning readiness and nomophobia approximate the correlation values reported by Ak & Yıldırım (2016) in their study. Therefore, it becomes clear how important education environment planning is for the use of smartphones in m-learning environments. Indeed, in poorly planned m-learning environments, increases in nomophobia caused by increases in m-learning readiness can lead to the onset of serious health problems (Rodriguez-Garcia et al., 2020). According to Peters (2009), educators stated that they are aware of the risks that accompany the use of smartphones in the m-learning environment. Nomophobia has been associated with the frequency with which individuals check their smartphones (Akıllı & Gezgin, 2016), duration of smartphone usage (Erdem et al., 2017) and the Internet usage, lonelinessand forms of attachment associated with social networking (Nawaz et al.2017;King et al.2010;Durak, 2018). These related situations may provide important indicators for educators in an m-learning environment. Therefore, educators need to plan seriously how to use smartphones in m-learning environments.
According to the results of the study, (H 4 -H 5 ) say that nomophobia directly and positively affects smartphone cyberloafing in class and smartphone addiction. The fact that nomophobia is a significant predictor of smartphone cyberloafing in class matches the conclusions of the study by Şumuer, Gezgin & Yıldırım (2019). That nomophobia causes smartphone addiction is similar to the conclusions of (Olivencia-Carrión et al. 2018;Semerci, 2019). Therefore, the research results found in the literature are in line with the results of this study. However, when the H 2 and H 5 hypotheses are examined, it can be seen that both nomophobia and cyberloafing cause smartphone addiction, but nomophobia (β=0.58) has a greater effect on smartphone addiction than it does on cyberloafing (β=0.23). In addition, studies showing that smartphone addiction stems from underlying psychological effects must also be taken into consideration. It is also argued that smartphone addiction is related to such psychological problems as social phobia (King et al., 2013) and depression (Chen et al., 2016;Yen et al., 2009).
In light of the results of the study, when nomophobia is examined among the variables of cyberloafing and addiction, it is seen that nomophobia has an effect on both cyberloafing (H4) and addiction (H5). Indeed, this study's conclusion is extremely important because when the effects of nomophobia, smartphone addictionand cyberloafing are examined by age distribution, it appears prevalent in young people and adolescents with a high potential for productivity (Aggarwal, 2013;Gezgin & Çakır, 2016;Netburn, 2012;Lee t al., 2015). Therefore, it is clear that nomophobia is the variable that causes both cyberloafing and smartphone addiction. Based on this, the variable of nomophobia needs to be kept in check to prevent increase in addiction and cyberloafing in both mlearning environments and the most productive years of an individual's life. Olivencia-Carrión et al. (2018) argue that collaboration between people is an important variable for lowering the level of nomophobia.

CONCLUSION AND RECOMMENDATIONS
The conclusion reached by this study is that smartphones, which are used as an m-learning tool in learning environments or as a tool for cyberloafing, cause nomophobia and smartphone addiction. In the literature, it was determined that mental and physical health problems arise as a result of the impulsive use of smart phones by individuals (Aggarwal, 2013;Bian & Leung, 2014;De-Sola Gutiérrez et al., 2016;Haug et al., 2015;Sansone & Sansone, 2013;Spitzer, 2015). In this regard, preventative services to combat nomophobia and smartphone addiction resulting from the impulsive use of smartphones could be provided. Accordingly, software could be developed to regulate the online networks that might lead to cyberloafing as a result of going online using smartphones in higher education classrooms. Rules and planning for smartphone use in m-learning environments could be developed. Accordingly, projects aimed at the sensible use of smartphones in m-learning environments could be prepared. Seminars and training courses explaining the situations that cause nomophobia and smartphone addiction could be given.

LIMITATIONS
This study was conducted with participants consisting of pre-service preschool teachers in only two state-run universities. Considering these limitations, it is recommended that new studies be made with participants from different departments in different state-run universities. In addition, qualitative research could be done to determine in detail the relationship between the variables of the proposed model. In future studies, the model could be restructured according to the personal variables supported by the literature regarding the variables in the model.