06 SES 01, Risk, Learning and Orientation in an Era of Digitalisation
Over the past decades, online distance education has a pivotal role in meeting increasing educational demands as an educational technology solution (Hung & Chou, 2015). Previous studies explored that online distance education has been at least as effective as formal education in terms of learning outcomes (Simonson, Schlosser, & Orellana, 2011). In addition, studies on promoting the effectiveness and quality of online distance education has been increasing in recent years. Many studies on distance education students in the literature show that the characteristics of online learners are examined in particular. As an important point in the previous studies, it is stated that distance education students have control focus. However, it is emphasized that the responsibility for learning in distance education is more in the learner. What is noteworthy in this context is the expectation that more students are responsible for learning in distance learning than other types of education and that they have the ability to regulate their own learning processes from distance learners. At this point it comes into the self-regulation skills that qualify as personal property. While self-regulation is the ability to control behaviors to achieve the desired results and to be motivated in this direction, attention control, which is a dimension of self-regulation considered in the study; goal-oriented behavior is to focus the attention of the individual. Self-efficacy, another important personal trait of distance learners; It is defined as an attribute that has an effect on the self-judgment and behavior of the individual about the capacity to organize and perform the activities needed to perform a certain performance (Bandura, 1997). Self-efficacy is seen as an important psychological factor in online learning as it can change the perceptions of individuals towards learning environments. Questions regarding how distance education students with different characteristics can realize a learning process in distance learning environments should be considered in terms of effectiveness and efficiency of the field. At this point, the concept of student engagement, which is a key factor in improving the learning outcomes in distance education and changing from person to person, emerges. Engagement in academic environments means the quality of the students' efforts to perform well and achieve the desired results (Richardson & Newby, 2006). Currently, occupational environments that are effective in the learning of distance education students and as a result of this, the individual students' level of commitment to the learning environment is an important issue and problem in the research of educational technologies. Unlike the formal learners, the distance learners who have their own special time periods, constantly interacts with technology. This interaction is kept alive by the instant notifications of smartphones. As a result of excessive and maladaptive usage of smartphones, negative situations such as addictive behaviors can arise (Park & Lee, 2011; Chen et al., 2017). These kinds of problems, which progress with repetitive and compulsive behavior patterns, are now being described with the term 'smartphone addiction' (Kim & Byrne, 2011). The purposes of the study was to investigate the relationship between the excessive use of smartphone and student engagement in online learning environment in the context of personal characteristics of distance learners. Within the theoretical framework related hypotheses were developed and research model was proposed.
In the path model, the study estimated a) the effects of general self-efficacy and control dimension of self-regulation on excessive use of smartphone b) the indirect effects of general self-efficacy and control dimension of self-regulation on engagement indicators (behavioral, cognitive and emotional) via excessive use of smartphone, and c) the effect of excessive use of smartphone on engagement indicators.
In this study correlational method was used. The correlational method involves looking at relationships between two or more variables (Cohen, Manion, & Morrison, 2002). The research was conducted with 891 distance learners, who were studying in different distance education programmes at seven different universities in Turkey. There were 359 males and 532 females. The model in the study was tested with structural equation modeling. Data were collected with online scale forms for two semesters. Developed by researchers “Demographic Data Form”, originally developed by Sun ve Rueda (2012) and conducted the reliability study of the Turkish version by Ergün and Usluel (2015) “Student’s Engagements Scale in Onlıne Learnıng Environment” (Cronbach’s alpha co-efficiencies of the scale changed between .62 -.90.), originally developed by Schwarzer, Diehl ve Schmitz (1999) and adapted to the Turkish form by Cevik and friends (2015) “Control Dimension of Self-Regulation Scale” (Cronbach's alpha = 0.84), originally developed by Schwarzer ve Jerusalem (1995) and adapted to the Turkish form by Aypay (2010) “General Self-Efficacy Scale” (Cronbach's alpha =0.80), originally developed by Kwon and friends (2013) and adapted to the Turkish form by Noyan and friends (2015) “Smartphone Addiction Scale-Short Version (Cronbach’s alpha=0.91)” used as data collection tool in the research. The collected data were analyzed by path analysis technique. Path analysis is a form of multiple regression statistical analysis and used to evaluate causal models by examining the relationships between a dependent variable and two or more independent variables. The independent (X) variables are called exogenous variables, the dependent (Y) variables are called endogenous variables. In this research, exogenous variables are control dimension of self-regulation, self-efficacy; endogenous variables are excessive use of smartphone and student engagement.
This paper focuses on the main results of doctoral research. Results of the model in the research are presented. Preliminary results from the path model indicate that (1) control dimension of self-regulation directly and negatively effects excessive use of smartphone, (2) control dimension of self-regulation directly and positevly effects behavioral, cognitive and emotional engagement, (3) general self-efficacy directly and positively effects smartphone addiction, (4) general self-efficacy directly and positively effects behavioral, cognitive and emotional engagement, (5) smartphone addiction directly and negatively behavioral, cognitive and emotional engagement, (6) control dimension of self-regulation indirectly and negatively effects behavioral, cognitive and emotional engagement via excessive use of smartphone, (7) general self-efficacy indirectly and negatively effects behavioral, cognitive and emotional engagement via excessive use of smartphone. The results of the study will be discussed within the context of the negative effects of smartphone addiction and the nature of online distance learning considering individual differences of online learners.
AYPAY, A. (2010). Genel Öz Yeterlik Ölçeği’nin GÖYÖ Türkçe’ye Uyarlama Çalışması. İnönü Üniversitesi Eğitim Fakültesi Dergisi, 11(2). Bandura, A. (1997). Self-efficacy. e exercise of control. NY: W.H. Freeman and Company. Chen, C., Zhang, K. Z., Gong, X., Zhao, S. J., Lee, M. K., & Liang, L. (2017). Examining the effects of motives and gender differences on smartphone addiction. Computers in Human Behavior, 75, 891-902. Cohen, L., Manion, L., & Morrison, K. (2002). Research methods in education. routledge. Ergün, E., & Usluel, Y. K. (2015). Çevrimiçi Öğrenme Ortamlarında Öğrenci Bağlılık Ölçeği’nin Türkçe Uyarlaması: Geçerlik ve Güvenirlik Çalışması. Eğitim Teknolojisi Kuram ve Uygulama, 5(1). Gökçearslan, Ş., Mumcu, F. K., Haşlaman, T., & Çevik, Y. D. (2016). Modelling smartphone addiction: The role of smartphone usage, self-regulation, general self-efficacy and cyberloafing in university students. Computers in Human Behavior, 63, 639-649. Hung, M., & Chou, C. (2015). Students' perceptions of instructors' roles in blended and online learning environments: A comparative study. Computers & Education, 81, 315-325. doi: http://dx.doi.org/10.1016/j.compedu.2014.10.022 Kwon, M., Lee, J. Y., Won, W. Y., Park, J. W., Min, J. A., Hahn, C., ... & Kim, D. J. (2013). Development and validation of a smartphone addiction scale (SAS). PloS one, 8(2), e56936. NOYAN, C. O., ENEZ DARÇIN, A., NURMEDOV, S., YILMAZ, O., & DİLBAZ, N. (2015). Akıllı Telefon Bağımlılığı Ölçeğinin Kısa Formunun üniversite öğrencilerindeTürkçe geçerlilik ve güvenilirlik çalışması. Anatolian Journal of Psychiatry/Anadolu Psikiyatri Dergisi, 16. Park, B.-W., & Lee, K. C. (2011). The effect of users' characteristics and experiential factors on the compulsive usage of the smartphone. In Ubiquitous computing and multimedia applications (pp. 438e446). Springer. Richardson, J. C., & Newby, T. (2006). The role of students' cognitive engagement in online learning. American Journal of Distance Education, 20(1), 23-37. Schwarzer, R., Diehl, M., & Schmitz, G. S. (1999). Self-regulation scale. 15.04.2018 http://userpage.fu-berlin.de/~health/selfreg_e.htm. Simonson, M., Schlosser, C., & Orellana, A. (2011). Distance education research: A review of the literature. Journal of Computing in Higher Education, 23(2-3), 124. Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self‐efficacy and self‐regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191-204. Schwarzer, R., & Jerusalem, M. (1995) General self-efficacy scale. In J. Weinman, S. Wright and M. Johnston (Eds.) Measures in health psychology: A user portfolio. Causal and control beliefs. Windor, England, NFER-Nelson.
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