Today’s students are often labeled as digital natives because they grew up with technology integrated into their daily lives, and as a result, many people assume that it is easy for them to adopt online learning technologies. However, not all students are equally comfortable with digital technology or have reliable access to it,[iii] and there are differences in how technology is integrated, both in schools and in homes, between high- and low-socioeconomic status households.[iv] Not only may students in low-socioeconomic status schools lack some of the skills required to adequately utilize digital tools in their learning, these students also tend to receive fewer opportunities to technology beyond drills and memorization for higher-order thinking skills.[v] If the digital divide between different socioeconomic groups is not addressed, online learning technologies can create, or enhance existing, educational inequalities.[iv]
While social networking platforms are popular among students, their potential as an educational tool is not yet clear.Citations
[i] Evergreen Education Group (2015) Keeping Pace with K12 Digital Learning: An Annual Review of Policy and Practice. [ii] iNACOL (2011). iNACOL National Standards for Quality Online Courses. [iii] The 'digital natives' debate: A critical review of the evidence [Review] Bennett S, Maton K, Kervin L,BRIT J EDUC TECHNOL (2008). [iv] Rideout, V. J. & Katz, V.S. (2016). Opportunity for all? Technology and learning in lower-income families. A report of the Families and Media Project. New York: The Joan Ganz Cooney Center at Sesame Workshop. [v] Reinhart, J., Earl, T., & Toriskie, J. (2011) K-12 Teachers: Technology Use and the Second Level Digital Divide. Journal of Instructional Psychology, 38(3), 181-193. [vi] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319340. [vii] An empirical study of instructor adoption of web-based learning systems [Article] Wang WT, Wang CC, COMPUT EDUC (2009). Understanding pre-service teachers’ computer attitudes: applying and extending the technology acceptance model [Article] Teo T, Lee CB, Chai CS, J COMPUT ASSIST LEAR (2008). De Smet, C., Bourgonjon, J., De Wever, B., Schellens, T., & Valcke, M. (2012). Researching instructional use and the technology acceptation of learning management systems by secondary school teachers. Computers & Education, 58(2), 688696. Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning [Article] Cheung R, Vogel D,COMPUT EDUC (2013). Investigating e-learning system usage outcomes in the university context [Article] Islam AKMN, COMPUT EDUC (2013). [viii] The acceptance and use of computer based assessment [Article] Terzis V, Economides AA,COMPUT EDUC (2011). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation- confirmation… [Article] Lee MC,COMPUT EDUC (2010). [v] An empirical study of instructor adoption of web-based learning systems [Article] Wang WT, Wang CC, COMPUT EDUC (2009). Understanding pre-service teachers' computer attitudes: applying and extending the technology acceptance model [Article] Teo T, Lee CB, Chai CS, J COMPUT ASSIST LEAR (2008). De Smet, C., Bourgonjon, J., De Wever, B., Schellens, T., & Valcke, M. (2012). Researching instructional use and the technology acceptation of learning management systems by secondary school teachers. Computers & Education, 58(2), 688696. [ix] Cheung, R. & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63: 160–175. Wang, Y., Wu, M, Wang, H. (2008). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1): 92–118. Karaali, D., Gumussoy, C., & Calisir, F. (2011). Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Computers in Human Behavior, 27(1): 343–354. [x] Mao, J. (2014). Social media for learning: A mixed methods study on high school students’ technology affordances and perspectives. Computers in Human Behavior, 33: 213-223. Hew, K. F. (2011). Students’ and teachers’ use of Facebook. Computers in Human Behavior, 27(2): 662-676. [xi] Manca, S., & Ranieri, M. (2013). Is it a tool suitable for learning? A critical review of the literature on Facebook as a technology‐enhanced learning environment. Journal of Computer Assisted Learning, 29(6): 487-504. [xii] Kirschner, P. A., & Karpinski, A. C. (2010). Facebook and academic performance. Computers in human behavior, 26(6): 1237-1245. [vi] Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning [Article] Cheung R, Vogel D,COMPUT EDUC (2013). Investigating e-learning system usage outcomes in the university context[Article] Islam AKMN, COMPUT EDUC (2013).