Article summary and analysis

¶ … Non-Participanting Online Students

Non-Participants Online Courses

The Case against Non-Participating Online Students

The Case against Non-Participating Online Students

With dropout rates 10 to 20% higher than bricks-n-mortar classes, the future success on online educational programs will depend on lowering these rates (reviewed by Nagel, Blignaut, and Cronje, 2009). The success of online classes has been shown to depend on student-instructor and student-student interactions, which are made more difficult by the long-distance nature of classes conducted over the internet. However, the difficulties associated with online classes are compensated for by opportunities to engage in prolonged online discussions with peers and instructors. The potential benefits of an interactive online learning environment therefore argue in favor of further research into what does and does not work and then making the necessary changes to improve educational outcomes.

Towards this goal, Nagel and colleagues (2009) monitored the experiences of students between the ages of 30 and 50 who were enrolled in a participatory online class. Students were required to produce individual and collaborative work during the 8-week course. Outcome measures depended on student engagement through postings, private student blogs, quizzes, a reflective essay, completion of feedback questions a month after course completion, and instructor observations. Since these outcome measures are both quantitative and qualitative in nature, this was a mixed methodology study.

Based on final student performance in the course, students were allocated to three groups for statistical purposes: Fail (N = 6), Pass (N = 9), and Distinction (N = 6) (Nagel, Blignaut, and Cronje, 2009). Using the Kruskall-Wallis non-parametric test, the authors showed significant differences between the three student groups based on the number of times a student clicked on course webpage material (hits), postings made, replies made, collaboration scores by peers, and voluntary feedback. On all outcome measures, Distinctive students were more active than Pass students and Pass students were more active than Fail students.

Despite the apparent clarity of these findings, Nagel and colleagues (2009) found some association between poor student performance and technical difficulties. Problems with computers, software, connectivity, and the electric supply seemed to interfere with participation levels by some students. The authors also noted that the number of original postings was a poor predictor of student involvement, since a few average students would post indiscriminately and thus create 'noise' and some high performing students tended to remain 'silent' until they had something significant to contribute. In contrast, the content and number of replies, when viewed as a ratio with original postings, provided a better indicator of student involvement. High performing students tended to offer multiple replies to peer queries, whereas low performing students tended to remain silent.

When it came to the collaborative assignments, the validity of an online academic approach seemed to be undermined (Nagel, Blignaut, and Cronje, 2009). Assignments were not done on time and were often of poor quality. In essence, poor performing students tended to let others carry most of the workload and the performance of all students suffered accordingly.