How do students learn in online environments, and what motivates them?
Technology has opened up a world of opportunities for online learning.[i] Many students now keep track of their coursework, read and watch educational content, and submit assignments through their schools’ online learning management systems (LMS). In addition, students have access to a broader range of classes and content areas through virtual classrooms and online distance learning. Outside the school setting, children informally learn from educational video games and online content, while people of all ages master new subjects in free massive open online courses (MOOCs).
Some forms of online learning translate the traditional class structure into the digital realm, while others open up new methods for communication among educators and students. In synchronous learning, an educator and their students gather online at the same time in a virtual classroom. Asynchronous learning does not require that students and instructors be online at the same time, opening up the possibility of self-paced and personalized learning tailored to individual students. Blended learning combines face-to-face instruction in a physical classroom with online learning activities.
Like the technologies that enable online learning, research on the topic is still fairly new, particularly when it comes to evaluating the outcomes of online learning. The section below highlights key findings from the emerging research on online learning.
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 access to it in their homes.[ii] If the digital divide between different socioeconomic groups is not addressed, online learning technologies can create or enhance educational inequalities.[iii]
According to the prominent technology acceptance model (TAM), there are two key factors that determine whether someone will adopt a new technology: perceived ease of use and perceived usefulness to one’s work.[iv] Research shows that these are two of the most important factors determining whether both students and educators accept and use online learning tools.[v][vi] Another important factor for students is whether they find a program enjoyable and interesting (perceived playfulness).[vii] This research suggests that online educational programming will be most successful when it offers informative content with clear benefits for learning, is easy for both students and educators to use, and engages students. Some studies also suggest that social influence and peer norms can play a role in whether people are willing to adopt online learning technologies.[xi]
While there is extensive research and debate about the negative effects of violent video games, there is also research demonstrating the potential value of educational games. Studies have found that students who play educational games have strong problem-solving skills, a clear understanding of context and the meaning of words and phrases, an awareness of relationships between concepts rather than isolated details, and increased motivation and engagement.[ix] However, the evidence on whether these skills translate into greater academic achievement is mixed.[x] There is also mixed evidence on whether students can apply knowledge or skills learned from video games in other contexts, so games must be carefully designed to support learning transfer. [xii]
The Acceptance of e-Learning subtopic explores the factors that lead educators and learners to accept and use computer assisted or “e-learning” approaches.
The Adolescents & Social Networking subtopic includes research on how adolescents use social networking, and its effects on their social development and relationships.
The Attitudes Toward Computers subtopic includes research on people’s attitudes toward computers and the Internet. It explores how and why attitudes differ, and how an individual’s attitudes affect their use of these technologies.
The Online Learning subtopic explores various types of online learning environments, including distance and blended learning. It includes research on instructional approaches to online learning, and outcomes for student academic success and social interaction.
The Student Technology Use subtopic includes studies on students’ technology use in learning and daily life, and explores concepts such as “digital natives,” the “net generation,” and “digital literacy.”
The Games & Culture subtopic covers research on computer and video games, including their role in sharing cultural values, and the development of user communities and identities.
The Open Educational Resources (OERs) & Policy subtopic explores factors that lead to the adoption and spread of OERs, and its effects on student learning and broader society outcomes such as globalization and equity.
The Environmental Policy & Attitudes subtopic includes research on people’s attitudes toward environmental policy issues, and the role of education in teaching about environmental policy issues.
The Online Communities subtopic explores how people interact and share knowledge within online communities, including both professional and gaming contexts.
[i] Evergreen Education Group (2015) Keeping Pace with K12 Digital Learning: An Annual Review of Policy and Practice.
[ii] The ‘digital natives’ debate: A critical review of the evidence [Review] Bennett S, Maton K, Kervin L,BRIT J EDUC TECHNOL (2008).
[iii] 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.
[iv] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319340.
[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.
[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).
[vii] The acceptance and use of computer based assessment [Article] Terzis V, Economides AA,COMPUT EDUC (2011). Explaining and predicting users’ continuance intention toward elearning: An extension of the expectation- confirmation… [Article] Lee MC,COMPUT EDUC (2010).
[viii] How student’s personality traits affect Computer Based Assessment Acceptance: Integrating BFI with CBAAM[Article] Terzis V, Moridis CN, Economides AA,COMPUT HUM BEHAV (2012). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective [Article] Roca JC, Gagne M,COMPUT HUM BEHAV (2008).
[ix] Gee, J. P. (2003). What video games have to teach us about learning and literacy. Computers in Entertainment (CIE), 1(1), 2020. Gee, J. P. (2009). Literacy, video games, and popular culture. The Cambridge handbook of literacy, 313 325. Myint Swe Khine, ed. (2011) Learning to play : exploring the future of education with video games. NY: Peter Lang.
[x] Perrotta, C., Featherstone, G., Aston, H. and Houghton, E. (2013). Gamebased Learning: Latest Evidence and Future Directions (NFER Research Programme: Innovation in Education). Slough: NFER. Michael F. Young, Stephen Slota, Andrew B. Cutter, Gerard Jalette, Greg Mullin, Benedict Zeus Simeoni, Matthew Tran, and Mariya Yukhymenko (2012) “Our Princess Is in Another Castle: A Review of Trends in Serious Gaming for Education,” Review of Educational Research, 82(1): 6189.
[xi] 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 the determinants and age and gender differences in the acceptance of mobile learning [Article] Wang YS, Wu MC, Wang HY,BRIT J EDUC TECHNOL (2009), Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry[Article] Karaali D, Gumussoy CA, Calisir F,COMPUT HUM BEHAV (2011),
[xii] EgenfeldtNielsen, S. (2006). Overview of research on the educational use of video games. Digital kompetanse, 1(3), 184213. Mitchell, A., & SavillSmith, C. (2004). The use of computer and video games for learning: A review of the literature.