Unpacking online learning experiences
The author is to investigate the experiences of online learners by establishing their own self-belief in the use online technology, learning and social interaction. They investigate the dimensions of online learning self-efficacy, and explore the variables related to students’ online learning and satisfaction. They also sought to establish the extent to which learners’ online self-efficacy is related to their online satisfaction. They view understanding self-efficacy as very critical in improving online learning and ultimately student achievement. This is principally due to the fact that self-efficacy ensures learner satisfaction which is important for maintaining continuity effective online learning for positive student achievement. The nature of online learning requires learners to be comfortable not only with the technology being used, but also with a wide array of things. Among the variable of self-efficacy in online settings they found out that prior online experience, gender and academic status are predictors of self-efficacy.
The study was to establish the online learning experience of students and make a connection between their online self-efficacy and learning satisfaction. Learner self-efficacy is considered with regard to the use of technology, learning and social interaction. The study’s purpose was also to establish the dimensions of online self-efficacy (Shen et al, 2013). Don't use plagiarised sources.Get your custom essay just from $11/page
A research gap is basically what the study has not sufficiently addressed. The study sample included more female respondents than male. This is because n=104 (25.6%) were males while n=301 (74.1%) of the respondents were females. This comes out as a weakness because gender is considered by the authors to be one the variables relating to self-efficacy. They use several studies to indicate that there is gender difference in self-efficacy (Wesley, 2002; Li, 2007; and Fletcher, 2005). There is significant underrepresentation of males in their study which clearly makes study weaker. Also with regard to academic status of respondents there were more graduates (n=244) than undergraduates (n=155). These means that the researcher had minimal control in selecting respondents according to their academic status and this makes the findings of the study weaker as it is dominated by mainly graduates.
The authors used the Bartlett’s test of sphericity. The Bartlett’s test indicates the strong relationship among the variables. It is normally used to test that variables are equal for all samples.
The results of the study are reliable in that the authors have followed all the necessary steps to justify them. Having defined the variables of self-efficacy questions were developed addressing each of the variables. All the five factors, Factor 1 (Self-efficacy to complete online courses); Factor 2 (Self-efficacy to interact socially with classmates, Factor 3 (Self-efficacy to handle tools in a CMS), Factor 4 (Self-Efficacy to interact with instructors in an online course; Factor 5: (Self-Efficacy to Interact With Classmates for academic purposes; had each a set of items addressing them.
I believe the claims in the article are accurate. For every dimension of self-efficacy the authors have made reference to the adequate data generated through the use of two data collection tools, one for addressing dimensions of online self-efficacy and the other for measuring online satisfaction.
With regard to online self-efficacy to complete an online course, the results indicate that the number of online courses was a significant predictor of self-efficacy to complete an online course, t(391) = 3.48, p<0.01.
Gender was also a significant predictor of self-efficacy to complete an online course, t(391) =-3.13, p<0.01.
Also academic status was not a significant predictor of self-efficacy to complete an online course, t (391), 1.07, p>.05.
With regard to self-efficacy to interact with classmates, the number of online courses, gender and academic status did not significantly predict self-efficacy to interact socially with classmates, F(3, 391) = 1.01, p> .05.
On the variable self-efficacy to handle tools, the number of online courses, gender and academic status together predicted self-efficacy to handle tools in a CMS (F, 3, 391) = 6.75, p<.001. The number of online courses was not a significant predictor of self-efficacy to handle tools in a CMS, 6(391) = -285, p< .01. Also, academic status was not a significant predictor of self-efficacy to handle tools in a CMS, t (391) = 2.48, p< .05.
On the variable self-efficacy to interact with instructors in an online course, the number of online courses, gender and academic status together significantly predicted self-efficacy to interact with instructors in a online course (F, 3, 391) = 3.16, p> . 05. Gender was a significant predictor of self-efficacy to interact with instructors, t (391) = -2.07, p> .05. Academic status was not a significant predictor of self-efficacy to interact with instructors in an online course t (391), = 005, p> .05.
On the variable self-efficacy to interact with instructors in an online course, the number of online courses was a significant predictor of self -efficacy to interact with instructors (t=391) = 5.21, p> .01.
Gender was also a significant predictor of self-efficacy to interact with instructors in an online course t (391) = -281, p< .01.
Academic status was not a significant predictor of self-efficacy to interact with instructors in an online course t (391) = .30, p> .05.
The work is scientifically strong because it conforms to all the rules of writing a scientific paper. It has very clear and well formulated objectives. The study variables are also well chosen and relevant. Over and above the authors have extensively cited the works of other scholars to support their claims. The empirical data generated has been used to explain the trends (online learner experiences) and draw conclusions from it. The data collected was derived from two data collection tools which were constructed basing on the study variables. The importance of the study lies in its inclusion of several factors which looked beyond just the issue of technology in online learning but others such as learning processes and social interaction.
REFERENCES
Fletcher, K.M. (2005). Self-efficacy as an evaluation measure for programs in support of online learning literacies for undergraduates. Internet and Higher Education, 8, 307-322.
Li, H. (2005). Understanding students’ technology appropriation and learning perceptions in online learning environments. (Unpublished doctoral dissertation). Columbia, MO: University of Missouri.
Shen, D. M., Cho, M. H., Tsai, C. L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. Internet and Higher Education, 19, 10-17.
Wesley, J.W. (2002). A study of academic achievement, attitude, motivation, general self-efficacy, and selected demographic characteristics of community college students. (Doctoral Dissertation). Starkville, MS: Mississippi State University.