Deviating from the characteristic previous approaches in extricating the dynamics of self-efficacy ” which before now have been somewhat limited in scope, the researchers in this study raise the bar in their efforts to analyze the complexities of self efficacy in online scenarios and its influence on learning fulfilment in students taking online learning programs. The researchers applied exploratory factor analysis to recognize five unique expressions of learning self – efficacy in virtual settings. Carrying out a blend of descriptive statistics in addition to using multiple regression analysis to posit five aspects of self efficacy in virtual learning environments.
Also, whether the same posited aspects will have any impact on virtual learning satisfaction.
Probably inspired by works by Artino (2008) and Womble (2008) who both found a positive causative association of increased self-efficacy for virtual learning, and higher probability of experiencing an increment in education satisfaction. 181800523989Towing the path of previous works by Artino (2008) and Womble (2008) who both found a positive correlation with increased self – efficacy for virtual learning and higher levels of achievement of educational fulfilment.
Additionally, prior research works such as that of Cho and Jonassen (2009) identified various facets of virtual self – efficacy which which include self-efficacy to relate with instructors and self – efficacy to participate meaningfully in the online community.
Inclusively, Artino and Stephens in their (2009) study indicated that people with more online learning experience tended to enroll for additional courses online. They also demonstrated increase degrees of task value beliefs and proved that postgrad students proved to have higher levels of critical thinking abilities when compared with undergrads. These prior researches might have largely inspired the authors to apply the exploratory factor analysis methodology to measure the following five facets of virtual learning self – efficacy namely:
- self – efficacy to complete a virtual learning program,
- self – efficacy to relate well with course-mates,
- self – efficacy to efficiently manage course management system (CMS) tools;
- self – efficacy to relate successfully with instructors while attending a virtual learning course and
- self-efficacy to interface well with classmates for academic purposes Shen et al (2013 p.10).
The authors of the study go on to evaluate the effects of three secondary variables regarding self – efficacy namely:
- previous virtual learning experience,
- educational status
These demographic variables were to ideas postulated from prior related studies of fletcher (2005) who posited that being earlier involved in virtual learning situations and gender status does have a significant influence on virtual learning self – efficacy”. Although, similar studies done by Artino and Stephens (2009), did not find any self – efficacy differences between undergrads and graduates”. This study evaluated virtual learning students with varied experiential and biological demographics to identify the implication of the three posited demographic variables on virtual learning self – efficacy. Outcomes of the research showed that jointly, all three factors influenced student’s ability to complete their online virtual courses, it also effected their ability to interact with other course mates for academic reasons, it enhanced their abilities to manage CMS tools and facilitated better interactions between them and their instructors in a virtual educational setting. But in the aspect of interacting communally with classmates, no causative influence was established between the demographic factors and participant’s self – efficacy. Other study outcomes are as follows: former experience in participating in an online learning program and gender significantly influences self – efficacy to conclude a virtual learning program.
Gender by itself showed to significantly influence the ability of students to engage instructors better in an online program, while educational status and gender significantly affected student’s confidence to efficiently manage CMS tools. Gender plus previous virtual learning experience combined also influenced respondents to better interface with other course mates for academic purposes. Interestingly, the three variables did not significantly influence student’s self – efficacy versus their patterns in how they socially relate with other class mates in generally relating with classmates of a social basisShen et al (2013).Capping off, the authors of the study discovered a causative relationship between self – efficacy on virtual learning fulfilment for the self – efficacy variables combined. However, in isolation, self – efficacy to efficiently manage tools relating to CMS did not have any significant impact on virtual learning satisfaction. Summarily the authors conclude that there are five key expressions of virtual self – efficacy and emphasize causative links that self – efficacy induces the likelihood of students completing a virtual learning program; and positively improving their likelihood to experience fulfilment in the course of their virtual learning program.
Finally, Shen et al. (2013), discovered a causative relationship between self-efficacy on online learning fulfilment for all self-efficacy variables combined. But in isolation, self-efficacy to handle tools in a CMS failed to impact online learning satisfaction.”177277999695This research is usefully sets the tone for more holistic approach and directs future research in exploring the multidimensional nature of self – efficacy. The study also goes to postulate that if extra influences such as gender and self – efficacy ideals can be properly managed, online satisfaction and ability to complete virtual learning courses can be improved among students. My personal take is – the manner of research approach, the detail, and outcomes of this research appear largely convincing.
Their attempt to undertake beyond previous research frontiers to explore multi-dimensionality of online self-efficacy is laudable. The major shortcomings of the study may be that the inadequacy of respondents’ sample size and a possible error in clearly distinguishing self-regulation and self – efficacy in their project design. As stated by the authors, Future researchers should restate each item to particularly measure self-regulation bit of self-efficacy’ Shen et al. (2013 p.16).”
(Harvard)Artino, A. R. (2008), Motivational beliefs and perceptions of instructional quality: Predicting satisfaction with online training’, Journal of Computer Assisted Learning, 24, 260″270.Artino, A. R., & Stephens, J. M. (2009) A
cademic motivation and self-regulation: A comparative analysis of undergraduate and graduate students learning online’, Internet and Higher Education, 12(3″4), 146″151.Cho, M. -H., & Jonassen, D. (2009)
Development of the human interaction dimension of the Self-Regulated Learning Questionnaire in asynchronous online learning environments’, Educational Psychology, 29, 117″138.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.Shen, D., Cho, M., Tsai, C. and Marra, R. (2013)
Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction’, Internet and Higher Education, 19, pp. 10″17. Elsevier [Online]. Available at: (Accessed: 25 February 2019).
Womble, J. (2008) E-learning: The relationship among learner satisfaction, self-efficacy, and usefulness’, The Business Review, 10(1), 182″188.
Cite this essay
Works by Artino and Womble. (2019, Aug 20). Retrieved from https://studymoose.com/works-by-artino-and-womble-essay