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More than ever, online classes are becoming a viable approach and solution for students pursuing undergraduate and secondary degrees. However, the accessibility to these classes does not guarantee all online students are receiving a quality education. Recent studies have shown that as online course enrollment increases, issues with assignment completion, lower quality work, and dropout rates have risen. One study revealed the drop-out rate for online students is 20 to 50% (Nagel, Blignaut, & Cronje, 2009), (as cited in Bernard et al., 2004). In their research, Nagel, Blignaut, and Cronje (2009) like Klemm (1998), Rovani, and Barnum (2003) felt it was essential to be participative in online discussions to be successful in a web-based learning environment. The study also revealed that within the virtual community of learners there exist two types of students that pose a risk to the online learning community, legitimate non-participation and inadvertent non-participation read-only participation. Legitimate non-participation students avoid the stage and are content observing others and contribute little to the success of the group.

This type of student does not share personal learning experiences and feeds off group ideas (Nagel, Blignaut, & Cronje, 2009), (as cited in Collins, Brown, & Holum, 1991). Inadvertent non-participation students avoid online participation. Because of technical deficiencies, these at-risk students will put off assignments, which ultimately contributes to the higher drop-out rate (Nagel, Blignaut, & Cronje, 2009), (as cited in Miller, Rainer, & Corley, 2003). Research for the study was performed during an online 8 week computer integrated education course and used the Davies, Graff (2005) model to identify any problematic characteristics of online classes. The sample consisted of 22 students of various ages and geographical locations (Nagel, Blignaut, & Cronje, 2009), (as cited in Oblinger, 2003). Students researched literature on various topics and posted their findings on a Learning Management System (LMS) discussion area. The LMS tracked how many times a student logged- in, messages left, and posted replies to messages. Student online experiences were monitored through the following: online quizzes, essay questions, and course feedback questions.

Facilitators provided feedback to the students throughout course completion (Nagel, Blignaut, & Cronje, 2009). The study demonstrated a strong predictive value at the group level. Students that were highly visible, had significant interaction with the facilitator and contributed to online discussions validated the point that participative students experienced a successful course completion (Nagel, Blignaut, & Cronje, 2009). In order to eliminate read-only participation, the study supports Klemm’s (1998) facilitator recommendations: quality postings versus quantity, grade individual contributions to group independently, rotation of group members, and texting of important class information. In order to enhance the predictive value for individuals, further studies are required. For example, consideration should be given to English as a second language students and include them in a separate study. A larger test sample would be beneficial and provide more feedback. Breaking out age groups would also prove beneficial in order to observe if there is a connection between technical deficiencies and higher age of participants. Finally, system requirements and type of internet connectivity could be included in the evaluating criteria. This would be beneficial in identifying students in poverty from those that have technical issues.


Bernard, R. M., Brauer, A., Abrami, P. C., & Surkes, M. (2004). The development of a questionnaire for predicting online learning achievement. Distance Education, 25(1), 31-47. Collins, A., Brown, J. S., & Holum, A. (1991). Cognitive apprenticeship: Making Thinking Visible. American Educator, 15(3), 6-11. Davies, J., & Graff, M. (2005). Performance in e-learning: Online participation and student grades. British Journal of Educational Technology, 36(4), 657-663. Klemm, W. R. (1998). Eight ways to get students more engaged in online conferences. Technical Horizons in Education Journal, 26(1), 62-64. Miller, M. D., Rainer, R. K., & Corley, J. K. (2003). Predictors of engagement and participation in an on-line course. Online Journal of Distance Learning Administration, 6(1), 13. Nagel, L.,
Blignaut, S., & Cronje, J. (2009). Read-only participants: a case for student communication in online classes. Interactive-Learning Environments, 17(1), 37-51. Oblinger, D. (2003). Boomers, gen-Xers & millennials. Educause, 4, 37-47. Rovai, A. P., & Barnum, K. T. (2003). On-line course effectiveness: An analysis of student interactions and perceptions of learning. Journal of Distance Education, 18(1), 57-73.

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