Testing a path-analytic model of adult dropout in online degree programs
Introduction
Online learning is a good option for adults with hectic schedules who cannot participate in conventional face-to-face learning due to work, social, and/or family responsibilities. For this reason, many distance learners are non-traditional adult learners (Gravani, 2015, Moore and Kearsley, 2012, Remedios and Richardson, 2013). To provide more adults with learning opportunities, many higher education institutions, including cyber universities, compete to offer a variety of online degree programs as well as non-degree online courses. As a result, the number of adult students enrolled in online degree programs or courses has continuously increased in the U.S. (U.S. National Center for Education Statistics, 2015). Online adult learning has also undergone remarkable growth in South Korea. For example, the number of adult students currently enrolled in cyber universities providing online bachelor degree programs has increased over 10 fold since 2001, when the first cyber university was established in South Korea (Korean Council for University Education, 2015). However, despite increases in both the number of adult distance learners and online programs, high dropout rates remain a serious issue that must be resolved to ensure the consistent vitality of online learning for adults (Park & Choi, 2009).
Many researchers (Holder, 2007, Lee et al., 2013, Lim, 2016, Morris et al., 2005b, Park and Choi, 2009) have attempted to identify ways to reduce the high dropout rate in online learning. Their studies mainly aimed to reveal the key dropout factors in online programs or courses to induce educational practitioners to appropriately control them and thereby reduce learner dropout rates. Some empirical studies (Choi, 2016, Müller, 2008, Park and Choi, 2009, Rovai, 2003) have focused specifically on the dropout factors of adult students in online learning programs. These studies indicate that adult students' decisions to drop out or persist in online learning is affected by many factors such as learners' scholastic aptitudes, study motives, physical constraints, financial support, encouragement from others, interactions, motivation, academic performance, and so forth. However, most empirical findings provide limited insight into the direct relationships between these variables and dropout decisions and neglect to identify the detailed relationships between the variables. It might be significant to know the direct and indirect effects of key variables on dropout decision because some dropout factors that educational administrators or instructors cannot apparently control can be handled by adjusting intermediate dropout factors. Accordingly, in order to provide educational practitioners with information about the significant factors impacting adult learners' dropout decisions and insights for handling uncontrollable adult dropout factors, this study aims to clearly detail the relationships among the primary factors affecting adult learners’ decisions to persist or drop out of online degree programs.
Section snippets
Theoretical framework
Several researchers have attempted to explain the factors affecting an adult distance learner's dropout and/or completion of a distance education courses or programs by proposing their own conceptual models (Choi, 2016, Kember, 1995, Park, 2007; Rovai, 2003). They aimed to propose valid and logical conceptual models based on empirical data and/or thorough reviews of related research findings and theories. First, Kember (1995) Open Learning Model indicated that adult learners' social and
Population and sample
The target population of this study was non-traditional students registered at a cyber university that grants bachelor's degrees in South Korea. The university was founded in the Spring of 2001 and currently offers 15 online bachelor's degree programs including beauty and health design, counseling psychology, and social welfare. The study sample consisted of 2129 adult students who had been admitted to the cyber university in 2013 and 2014. Of the 2129 students, 856 students (40.2%) were male
Results
Table 1 shows the descriptive statistics for the continuous variables. Because further analysis requires that the normality of each variable be met, we tested normality in terms of its skewness and kurtosis. The criteria of non-normality proposed by West, Finch, and Curran (1995) were skewness > 2 and kurtosis >7. The normality assumptions of all the continuous variables were met.
Table 2 shows the means and standard deviations of scholastic aptitude, interaction with course content,
Discussion
Using large samples from multiple online degree programs, this study aimed to identify the direct and indirect relationships among major adult dropout factors (i.e., basic scholastic aptitude, physical constraints, interaction with course content, satisfaction, and GPA) and provide educational practitioners with insights for indirectly handling uncontrollable adult dropout factors by empirically detailing the relationships between the variables. To achieve these purposes, we formulated and
Acknowledgement
This work was supported by the Hongik University Research Fund.
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- 1
Permanent address: Hongik University, 94 Wausan-ro, Mapo-gu, Seoul, 04066, Korea.
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Permanent address: Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Korea.