The online education has gain popularity during the last decade or so and led to significant number of students availing these courses. As a result, it makes sense to analyse if the performance of the online universities for which the given task focuses on two measures in the form of RR% (Retention Rate) and GR% (Graduation Rate). Collectively these two are pivotal factors that highlight the extent of drop-outs highlighted in the online universities in US. This is pivotal as higher drop outs reflect potential issues that would need to be sorted in the long run through pragmatic recommendations.
The large scale internet penetration has altered the ecosystem related to product and service delivery. Using internet as the medium, it became possible to transmit the various services to far flung corners and avail these in the comfort of chosen location. Education also witnessed significant changes owing to the growth in online courses which over the time have become popular. This does not come as a surprise considering the benefits in the form of lower costs, increased convenience, better time management and flexibility in time and geography for availing various courses (Lederman, 2018). One key downside of these courses that has been indicated through various empirical evidence is the high rate of dropout that are experienced (Wellman, 2018). In order to ensure that these courses make a significant positive contribution to education and economic development, it is pivotal that the issue of dropouts must be kept within check.
The sample data provided which contains information about RR% and GR% for a set of 29 online US universities acts as the pivotal first step. Using measures of central tendency along with dispersion measures, descriptive statistics are represented that indicate the summary of the sample data. Further, a scatter plot has been drawn to explore the level of association between RR% and GR%. In order to carry out a detailed analysis on the same, regression analysis is carried out based on Excel. Besides, inferential statistical techniques such as hypothesis testing have been used to ascertain the statistical significance of slope and linear relationship.
The first noteworthy aspect is the arrangement of scatter points which exhibit an upward trend which hints at the positive relationship between the two variables under consideration. Further, the position of the scatter points is that the deviation of these points from the regression line is not high which implies that association is strong in strength. This is confirmed from the computation of correlation coefficient which comes out as 0.67 (Hillier, 2016).
In the above equation, 0.285 is the slope coefficient. This may be interpreted as the change which is likely to be witnessed in GR% when there is a one percent change in RR%. Also, this change in the dependent variable (i.e. GR%) would be the same as independent variable (i.e. RR%) as the slope coefficient is positive (Lieberman et. al., 2013).
It is apparent from above that t stat for the slope coefficient is 4.693 with the p value as 0.000.
The decision rule is to reject the H0 when p value fails to exceeds the significance level. This has been achieved in the given case as p value (0.00) < significance level and therefore rejection of H0 is confirmed. This leads to acceptance of H1 (Flick, 2015). The conclusion is that the slope for the regression line is statistically significant thereby reflecting that association between RR% and GR% is also significant and cannot be ignored.
On an average, the sample universities performance is not too bad as the RR% and GR% seem to be satisfactory. However, the concerning aspect is that there is high variation in these performance parameters across the sample universities. Also, a majority tend to default in one of the two parameters which essentially leads to poor performance. Using the scatter-plot, the association between performance parameters (RR% and GR%) is not only positive but strong. The slope coefficient and the statistical significance associated with the same also tend to provide support in this regards.
A particular strength of this exercise is that the conclusions have been based on results produced through statistical techniques based quantitative analysis. However, this objectivity and reliability may have been compromised if the sample used for the exercise was biased. This is possible as the underlying sampling technique used for selection of the included universities is not disclosed (Medhi, 2016). Some concern in relation to reliability of the sample data is resolved by the fact that the results produced through analysis of sample data is similar to recent studies on the topic conducted in a similar context. Considering the potential issues for online universities, policymakers need to address the same through constructive regulation.
The key recommendations are enumerated as follows.
References
Flick, U. (2015) Introducing research methodology: A beginner’s guide to doing a research project. 4th ed. New York: Sage Publications, pp. 56-57
Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., and Page, M. J. (2015) Essentials of business research methods. 2nd ed. New York: Routledge, pp. 105-107
Hillier, F. (2016) Introduction to Operations Research.6th ed.New York: McGraw Hill Publications, pp. 145-146
Lederman, D. (2018) Who Is Studying Online (and Where), Available [online] at https://www.insidehighered.com/digital-learning/article/2018/01/05/new-us-data-show-continued-growth-college-students-studying [Available February 13, 2019]
Lieberman, F. J., Nag, B., Hiller, F.S. and Basu, P. (2013) Introduction To Operations Research. 5th ed. New Delhi: Tata McGraw Hill Publishers, pp. 134-135
Medhi, J. (2016) Statistical Methods: An Introductory Text. 4th ed. Sydney: New Age International, pp. 67-69
Wellman, R. (2018) How to Avoid Dropping Out of an Online College Available [online] at https://www.usnews.com/education/online-learning-lessons/articles/2018-06-01/how-to-avoid-dropping-out-of-an-online-college [Available February 13, 2019]
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