Educational Use of Social Media in Higher Education: Gender and Social Networking Sites as the Predictors of Consuming, Creating, and Sharing Content

Emel Dikbaş Torun 1
  • 1 Pamukkale University, School of Communication, Department of New Media and Communication, Denizli, Turkey


Introduction:This study investigates the influence of gender and social networking sites (SNSs) such as Instagram, YouTube, WhatsApp, Facebook, and Twitter on consuming, creating, and sharing content within the educational social media usage behaviors of higher education students. The survey method is applied to measure students’ social media usage for educational purposes. So that a more effective use of social media in education can be provided, it is important to understand how university students vary in their educational use of social media. The aim of this study is to examine how higher education students use social media for their educational purposes based on the content and activities with which the students engage. The aim of the research is to determine the correlations, if any, between gender, preferred SNS type, and educational social media in regard to consuming, creating and sharing content.

Methods:The derived scale is administered in Turkey with the participation of a total of 365 university students. Psychometric, validation and reliability analysis of the scale which is used in the study to collect the data were done first. Principal component analysis, exploratory and confirmatory factor analysis, descriptive, correlations and multivariate analysis of variance are applied to analyze the social media usage for educational purposes. Gender and the SNS type were set as the additional predictors of the consuming, creating and sharing content on social media.

Results:The validation and linguistic adaptation of the Inside School Social Media Behavior (ISSMB) scale from English to Turkish is performed first. Results showed that the three factors of the original scale were confirmed. Secondly, the derived scale is administered with the participation of a total of 365 university students. Results indicated that gender difference was a significant factor in explaining the content creation on social media. Instagram, WhatsApp, and YouTube are the most preferred SNSs for educational use among students at the higher education level. No significant effect was reported for the type of the SNS used in consuming, creating, and sharing educational content on social media. The type of the SNS used by the students was not found to influence educational social media usage; accordingly, students consume, create and share content, regardless of the type of the SNS they use.

Discussion:Higher education level students prefer watching videos more than any other social media activity for their educational purposes. The second most frequently preferred social media usage activity was reported as searching for the learning resources or information pertaining to schoolwork. Creating content was the least favorable social media usage. When the social media usage purposes focus on schoolwork and are furthermore educational, males’ social media usage outperforms the females. Thus, males were more likely to create content by using social media for inside schoolwork purposes than the females. Males were also more likely to have sharing habits than the females in sharing learning resources e.g., class notes with their classmates by using social media for their inside schoolwork purposes.

Limitations:The total number of participants used in the research sample is a limitation of this study. The study data were only collected in Turkey, and so the study results are only regionally generalizable.

Conclusion:Higher education students are consumers of the social media when they use it for educational purposes. Accordingly, students prefer being “passive consumer social media users who avoid active content creating”. Students prefer watching the uploaded ready-to-watch videos who avoid instead of creating and uploading their own video content. When sharing items are compared with creating content items, students responded more to the latter. Students do share their information with classmates e.g. exam schedules and lecture notes. Compared to other sharing content usages, students less frequently preferred sharing extracurricular learning resources. The gender difference found herein is a predictor of social networking site usage among young people, and social networking usage changes according to gender. Males are reported as being more “giving” within a school setting when it comes to sharing the educational content with their colleagues and friends. Social media is a reality of our modern lives, one that is growing exponentially; it is highly crucial that researchers facilitate a better understanding of the ongoing changes and developments that are emerging and transforming learning.

Both outside and inside school, the social media usage behaviors of young people can be examined according to different age groups do determine any age-related differences. The subject can be improved with new findings and results from different sample groups.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Baym, N. K. (2015). Social Media and the Struggle for society. Social Media + Society 1(1), 1-2.

  • Bentler, P. M. (1980). Multivariate analysis with latent variables: Casual modeling. Annual Review of Psychology, 31, 419-456.

  • Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606.

  • Boczkowski, P. J., Mitchelstein, E., & Matassi, M. (2018). “News comes across when I’m in a moment of leisure”: Understanding the practices of incidental news consumption on social media. New Media & Society, 20(10), 3523-3539.

  • Bogozzi, R. P., & Yi, Y. (1998). On the Evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.

  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assesing model fit. In K. A. Bollen, & J. S. Long (Eds.), Testing Structural Equation Models (pp.136-162). Bevelry Hills, CA: Sage.

  • Büyüköztürk, Ş. (2010). Sosyal Bilimler İçin Veri Analizi El Kitabı. Ankara: Pegem Akademi Yayınları.

  • Dikbaş Torun, E. (2019). Adapting Social Media Behavior Scale to Turkish: Reliability and Validity. Sosyal Medya Davranışları Ölçeklerinin Türkçe’ye Uyarlanması: Geçerlik ve Güvenirlik Çalışması. Akdeniz Üniversitesi İletişim Fakültesi Dergisi, 32, 217-234.

  • Dikbaş Torun, E. (2019). Students’ social media use as a new communication environment: The case of Pamukkale University. In E. Doğan (Ed.), Current Debates in Film and Media Studies (pp. 43-51), London: IJOPEC Publication.

  • Fox, J., & Moreland, J. J. (2015). The dark side of social networking sites: An exploration of the relational and psychological stressors associated with Facebook use and affordances. Computers in Human Behavior, 45, 168-176.

  • Gismondi, A., & Osteen, L. (2017). Student activism in the technology age. New Directions for Student Leadership, 2017(153), 63-74.

  • Huang, C. (2018). Social network site use and academic achievement: A meta-analysis. Computers & Education, 119, 76-83.

  • Hung H.-T., & Yuen, S. (2010). Educational use of social networking technology in higher education. Teaching in Higher Education, 15(6), 703-714.

  • Ifinedo, P. (2017). Examining students’ intention to continue using blogs for learning: Perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior, 72, 189-199.

  • Junco, R. (2012). Too much face and not enough books: The relationship between multiple indices of Facebook use and academic performance. Computers in Human Behavior, 28(1), 187-198.

  • Kirschner, P. A., & Karpinski, A. C. (2010). Facebook and academic performance. Computers in Human Behavior, 26(6), 1237-1245.

  • Lu, J., Hao, Q., & Jing, M. (2016). Consuming, sharing, and creating content: How young students use new social media in and outside school. Computers in Human Behavior, 64, 55-64.

  • Lu, J., Luo, J., Liang, L., & Jing, M. (2018). Measuring adolescents’ social media behavior outside and inside of school: Development and validation of two scales. Journal of Educational Computing Research 57(5) 1108-1130.

  • Özen, Ü., Çam, H., Can, D., & Dönmez, Ö. (2018). Uzaktan Yükseköğretim Öğrencilerinin Sosyal Medyanın Eğitim Boyutu Konusundaki Algıları ve Eğitim Amaçlı Sosyal Medya Kullanımlarının Belirlenmesi. The Journal of International Scientific Researches, 3(1), 64-72.

  • Poellhuber, B., Anderson, T., & Roy, N. (2011). Distance students’ readiness for social media and collaboration. The International Review of Research in Open and Distributed Learning, 12(6), 102-125.

  • Reagle, J. M. (2015). Reading the Comments: Likers, Haters, and Manipulators at the Bottom of the Web. Cambridge, Massachusetts: MIT Press.

  • Rideout, V. (2015). The Common Sense Census: Media Use by Tweens and Teens. Common Sense Media. Retrieved from

  • Rideout, V. J., Foehr, U. G., & Roberts, D. F. (2010). Generation M 2: Media in the Lives of 8-to 18-Year-Olds. Henry J. Kaiser Family Foundation.

  • Roberts, D. F. (2005). Generation M: Media in the lives of 8-18-year-olds. Henry J. Kaiser Family Foundation.

  • Manca, S., & Ranieri, M. (2016). Facebook and the others. Potentials and obstacles of social media for teaching in higher education. Computers & Education, 95, 216-230,

  • Selwyn, N. (2012). Social media in higher education. The Europa World of Learning, 1, 1-10.

  • Wang, S. K., Hsu, H. Y., Campbell, T., Coster, D. C., & Longhurst, M. (2014). An investigation of middle school science teachers and students use of technology inside and outside of classrooms: considering whether digital natives are more technology savvy than their teachers. Educational Technology Research and Development, 62(6), 637-662.


Journal + Issues