Online Learning Annotated Bibliography 1

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Online Learning Annotated Bibliography

Case, D. E., & Davidson, R. C. (2011). Accessible Online Learning. New Directions for Student Services , 134, 47-58.

This journal article finds inspiration from the rising uptake of online education as a preferred mode of knowledge acquisition. In a demonstration of this fact, the authors note that for every four students undertaking higher education, one among them takes some of their courses through virtual learning. However, the number of students with disabilities pursuing post-secondary education is significant, reaching 11% of all post-secondary learners by 2008. The authors hold that despite these statistics, it is rarely considered if disabled students can access the online courses offered. However, it is the responsibility of the disabled learner to discern whether an online course fits their desired learning style. The authors propose means of improving accessibility to online education, which not only benefits the disabled students but all learners using the online learning platform. While there is no guarantee of total accommodation to all prospective students, prior planning ensures minimal personal customization when a disabled student enrolls in an online course.

Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. Washington D.C: U.S. Department of Education Office of Planning, Evaluation, and Policy Development Policy and Program Studies Service .

The authors of this report developed it from a thorough and systematic evaluation of existing literature, where in excess of 1000 empirical studies were identified. Further evaluation determined which studies contrasted online studying with on-site instruction, determined student learning achievements, employed a robust research system while providing sufficient information to determine the magnitude of the effect. The selection produced 51 self-sustaining variables that were then subjected to then meta-analysis. Analysis of the effects revealed that, on average, online learners reported improved outcomes than students receiving face-to-face instruction. The authors found that learners reported improved outcomes where virtual learning was blended with on-site instruction. The difference was explained by the increased amount of time, as well as additional tools not enjoyed by students in the control settings. However, the authors noted the limited number of rigorous studies comparing online and face-to-face learning, implying the need for further research into the findings published.

Moore, J. L., Dickson-Dean, C., & Galyen, K. (2010). e-Learning, online learning, and distance learning environments: Are they the same? Internet and Higher Education , 129-135.

The authors of this study were motivated by the regularity with which researchers face difficulty when conducting cross-study analysis during the research process. Research pertaining to online learning is of particular interest to this study, given the diversity of environments, as well as their defining qualities. The authors employed a mixed-method analytic process to the research articles in a bid at determining their understanding of the learning environment. Further, the research process surveyed 43 people, an action that led to the determination of discrepancies in the use of terminology in reference to various modes of delivery. The authors determine a variation in the views and expectations on suitable labels for learning environment. Consequently, the terms e-Learning, Distance learning as well as online learning have different definitions in relation to context, objective, audience and access.

Muilenburg, L. Y., & Berge, Z. L. (2005). Student Barriers to Online Learning: A factor analytic study. Distance Education, 26(1) 29-48.

The authors of this article report on a study examining the pertinent factors that make up student limitations to online learning. The researchers established eight factors to be barriers to online learning as follows;

    • administration of the courses

    • social bonding

    • academic amplitude

    • technical ability

    • student motivation

    • time allocation and support for learning

    • access and the internet cost

    • technical challenges to the medium.

The interviewed students exhibited independent variables that consequentially influenced the recorded outcomes. Of these variables included; age, race, gender, the nature of the institution, self-evaluation of virtual learning capability, online learning experience, the amount of courses undertaken as well as instances of prejudice in the traditional classroom.

Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(1) 838-853.

The authors of this report concern themselves with the factors influencing the success of an online scholarship system (VLE), with learner acceptance and adoption of such a system being of supreme importance. The authors analytically evaluate the models of technology assimilation, such as the Technology Acceptance Model, and the Unified Theory of Acceptance and Usage of Technology to develop and theoretical prototype explaining the variations between students in their standard of approval and use of a virtual learning environment. The resultant model incorporates an individual standard, individual creativity in information technology use, as well as computer anxiety. The study uses data composed of 45 Chinese students completing an administrative MBA course. The findings from this study reveal that supposed usefulness has direct ramifications on VLE usage. Ease of application, as well as individual standard, only have an effect on the use of VLE through association to perceived utility.

Reyna Zeballos, J. (2016). Implementing E-learning Across the School of Education: A Case Study. International Journal on E-learning: corporate, government, healthcare and higher education, 15(1) 101-121.

The author attempts to take stock of the progress achieved in implementing and improving online learning environments within University of Western Sydney’s school of education. The improvement efforts commenced with the appointment of an e-learning officer, tasked with the upgrade of online learning settings across the school. This task was met through the development of a strong working relationship with 45 instructors drawn from two of the university’s campuses. They were tasked with the upgrade of primary e-learning ideals, upgrade of information infrastructure while at the same time raising the utility and usability of online courses. The team embarked on the assimilation of technologies such as Google Docs, blogs, and online video into the instructional curricula, as well as in instructional contexts. The author holds that these efforts have led to heightened morale within the school, principally relating to the online instruction. The appointment of the e-learning officer has led to a substantial attitudinal shift, especially concerning the value of online learning.

Tseng, S.-F., Tsao, Y.-W., Yu, L.-C., Chan, C.-L., & Lai, K. R. (2016). Who will pass? Analyzing learner behaviors in MOOCs. Research and Practice in Technology Enhanced Learning, 11(8) <http://link.springer.com/article/10.1186/s41039-016-0033-5/fulltext.html#copyrightInformation>.

Online learning capabilities have led to the emergence of (MOOCs), short for massive open online courses. These courses present an alternative for learning, but their lacks concrete data on the returns presented by the new learning medium. The authors of this report gathered data from three MOOCs from YZU University in China. The analysis considered learning actions exhibited by the 1489 learners evaluated. Further, the exercise classified learners into different categories using ordered and non-hierarchical grouping methods. There emerged three distinct categories of learners, namely active, passive and bystander categories. The active students exhibited characteristics such as timely submission of assignments and frequent checking-up on lecture videos. Further, the active students also displayed a higher completion rate of their courses in comparison to the inactive students. Careful formulation of MOOCs is critical in minimizing cases of dropout. Timely feedback from instructors through the discussion board was found as a suitable enhancement for learner engagement in the MOOCs.

References:

Case, D. E., & Davidson, R. C. (2011). Accessible Online Learning. New Directions for Student Services , 134, 47-58.

Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. Washington D.C: U.S. Department of Education Office of Planning, Evaluation, and Policy Development Policy and Program Studies Service .

Moore, J. L., Dickson-Dean, C., & Galyen, K. (2010). e-Learning, online learning, and distance learning environments: Are they the same? Internet and Higher Education , 129-135.

Muilenburg, L. Y., & Berge, Z. L. (2005). Student Barriers to Online Learning: A factor analytic study. Distance Education, 26(1) 29-48.

Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(1) 838-853.

Reyna Zeballos, J. (2016). Implementing E-learning Across the School of Education: A Case Study. International Journal on E-learning: corporate, government, healthcare and higher education, 15(1) 101-121.

Tseng, S.-F., Tsao, Y.-W., Yu, L.-C., Chan, C.-L., & Lai, K. R. (2016). Who will pass? Analyzing learner behaviors in MOOCs. Research and Practice in Technology Enhanced Learning, 11(8) <http://link.springer.com/article/10.1186/s41039-016-0033-5/fulltext.html#copyrightInformation>.