Distance learning allows adult learners who have employment, family, and/or other responsibilities to update knowledge and skills related to their job by saving travel costs and allowing a flexible schedule. Moore and Kearsely (2005) indicated that most distance education students are adults between the ages of 25 and 50. The number of programs for adult learners delivered online in corporate settings as well as in higher education has steadily increased over the last few years.
According to the results of a survey administered by the National Center for Educational Statistics (NCES), 56 percent of all degree-granting higher education institutions offered distance courses during the 20002001 academic year (Waits & Lewis, 2003). In 2003, 34 percent of 1000 representative higher education institutions offered a complete online degree program (Allen & Seaman, 2004). In addition, Bersin (2005) indicated that online learning continued to grow in 2005 by 25 percent, and comprised 33 percent of all workplace learning. Sugrue and Rivera (2005) reported that training delivery via technology (or online) increased from 35 percent to 38 percent in large organizations, and from 24 percent to 27 percent in relatively smaller organizations from 2002 to 2003.
In spite of the growth in online learning, high dropout rates have been of concern to many organizations and higher education institutions. According to Meister (2002), 70 percent of adult learners enrolled in a corporate online program did not complete it. The Corporate University Xchange (2000) indicated that one of the difficult challenges of online programs is to retain learners. A number of studies have shown that a higher percentage of students participating in an online course tend to drop out compared to students in a face-to-face classroom (Hiltz, 1997; Phipps & Merisotis, 1999). Some consider the higher dropout rate in distance learning a failure while others advise careful interpretation of the issue because of unique characteristics and situations that online learners have. Diaz (2002) indicated that uncontrollable factors influence dropout decisions and a high dropout rate is not necessarily indicative of academic non-success. Nonetheless, it is still not easy to explain to corporate executives that dropout rates do not matter (Alexander, 2002), and it is certain that the issue of high dropout rates in online training should be addressed and dealt with.
Several theories and theoretical frameworks have been proposed to explain why students drop out. In particular, Tinto’s student integration model (1993) and Bean and Metzner’s student attrition model (1985) have guided dropout research studies. Tinto (1993) claimed that attrition is a result of interactions between a student and his/her educational environment during the student’s stay in a program. He indicated that social integration and academic integration produced stronger student commitment to their institutions and increased students’ persistence. However, educators who desire to study the persistence of nontraditional students, who have different characteristics and nature from traditional students, have found that Tinto’s model has limited applicability (Rovai, 2003; Bean & Metzner, 1985). Tinto himself indicated that it was necessary to modify his model when used with nontraditional students (Tinto, 1982).
Bean and Metzner (1985) developed a conceptual model for nontraditional students who drop out that includes academic performance, intent to leave primarily influenced by academic and psychological outcomes, background and defining variables, and environmental variables. They asserted that the main difference between the attrition process of traditional and nontraditional students is that nontraditional students are more affected by the external environment than traditional students. However, Bean and Metzner’s model is unlikely to be applied for distance learners because there is a significant discrepancy between the definitions of distance learners in general and nontraditional students in the Bean and Metzner’s model (Kember, 1989).
Kember (1989), therefore, proposed a longitudinal process model of dropout distance education and made suggestions for testing the model (e.g., developing reliable instruments, conducting both qualitative and quantitative research, etc). Kember’s longitudinal model recognizes that social and academic integration of students should be viewed with intervening variables between initial student characteristics/background and persistence, that components change over time, and that students have to make dropout decisions several times during lengthy courses. Kember, Lai, Murphy, Siaw, and Yuen (1992, 1994) have tested this model in different sets of institutions, courses, and students and emphasized the importance of social and academic integration to student progress in distance learning. Since then, a couple of researchers have committed to comparing those previous models, determining advantages and disadvantages, and finally developing a model explaining the process of dropping out in a particular population and learning environment. Cabrera, Castaneda, Nora, and Hengstler (1992) reviewed Tinto’s and Bean and Metzner’s dropout frameworks, and the results indicated that Tinto’s model is more comprehensive and robust while Bean and Metzner’s model accounts for more variance in persistence.
Rovai (2003) proposed a persistence model to explain factors affecting a learner’s decision to drop out of online learning. The model included two prior-to-admission variables and two after-admission variables. The two prior-to admission variables are student characteristics and student skills prior to admission. The two after-admission variables are external factors (e.g., finances, hours of employment, outside encouragement, etc.) and internal factors (e.g., academic integration, social integration, self-esteem, interpersonal relationships, study habits, advising, absenteeism, etc.). Rovai’s framework is established by a thorough review of the most comprehensive previous frameworks (i.e., Tinto’s student integration model  and Bean & Metzner’s student attrition model ), particularly focusing on nontraditional online learners who have characteristics similar to adult learners in organizations. This model was also tested and expanded by Packham, Jones, Miller, and Thomas (2004).
Park (2007) reviewed studies that focused on identifying factors affecting non-traditional and non-degree online program students who drop out and proposed a framework based on Rovai’s model for understanding adult dropouts (Figure 1). Based on the review, she indicated that the significance of the four factors from Rovai’s model is supported from many studies with a variety of research methods. However, she suggested revision of the structure of the model and elimination of some of the variables. Specifically, learner skills are in a grey box because these have found little empirical support in previous studies, and their inclusion can be determined only through relevant further investigation. The external factors are moved between prior to and during the courses because these affect student decisions not only during the course but also prior to the course. Adult distance learners may drop out of the course due to increased workload or job change that happens during the course, but some learners may drop out of a course even before they start because of such external reasons.
In addition, external factors and internal factors are likely to interact with each other. For example, when learners have a heavy workload and little time for study, they are more likely to drop out of a course when they cannot get feedback or if it is hard to contact the instructors than when they can easily communicate with them and get more responses. If proper course design and technology are being used, some external problems are likely to be mitigated. So the relationship between internal factors and external factors are expressed as inter-correlation rather than as a one-sided influence. In addition, it appeared that only internal factors would have a direct influence on persistence decision, and others have an indirect affect through internal factors in Rovai’s model. However, many studies have reported that some external factors have been major reasons why online learners decided to drop out, particularly in relation to adult distance learners. Therefore, a direct line from external factors to dropout/persistence has been added.
Even though numerous studies have tried to identify factors affecting learners’ decision to drop out, only a dozen research studies have empirically explored this issue, and no consensus has been reached for which factors have definite influences on the decision (Park, 2007). Although Park and Choi (2007) presented a research study investigating the effects of individual characteristics, external factors, and internal factors on non-traditional adult learners’ decision to drop out, the study was limited in that the sample size was too small (n = 47) so that the results could hardly be reliable. In addition, relevance, one of the most crucial motivational factors considered to affect adult learners’ decision to drop out, was omitted.
Accordingly, the purpose of this study was to identify meaningful factors affecting learners’ decision to drop out of online courses and ultimately to shed light on how we can retain students in online courses by involving a significant number of research participants and adding meaningful factors. Particularly, this study focused on the three main categories: individual characteristics, external factors, and internal factors. To be more specific, age, gender, educational background, and employment status were chosen as individual characteristics because these four are the most often cited factors in previous studies (Park, 2007). External factors consist of family support and organizational support. Most adult learners have many responsibilities for their family as well as for their job, and these two are key factors affecting adult learners’ decision to drop out of online courses (Park, 2007). Motivation is one of the most frequently studied variables in relation to dropout (Chyung, 2001; Chyung, Winiecki, & Fenner, 1998; Doo & Kim, 2000; Jun, 2005; Levy, 2007; Menager-Beeley, 2004). In particular, relevance and satisfaction are the sub-dimensions of motivation that have frequently been studied (Chyung et al., 1998; Doo & Kim, 2000; Levy, 2003, 2007; Shea, Pickett, & Pelz, 2003) and are known to be highly correlated with various course-related issues such as instructional design, organization of the online courses, instructors’ facilitation, and interaction (Shea et al., 2003). This study could not include other internal factors such as social integration, academic integration, and technology issues shown in the above framework because the courses investigated in this study were developed before conducting this study, and the researchers did not have access to the course contents and were not involved with the design and development process.
This study aimed to determine whether or not there were differences between dropouts and persistent learners in online courses in their individual characteristics, the perceptions of family support and organizational support, and the level of motivation (i.e., satisfaction and relevance). This study also intended to find factors to predict dropouts and persistent learners in online courses to help stakeholders associated with online courses for adult learners find ways to lower the high dropout rates.
Previous studies have reached no consensus on the influence of learner characteristics on adult learners’ decision to drop out of online courses. Some reported that learner characteristics have significant influence on the decision (Brown, 1996; Jun, 2005; Meneger-Beeley, 2004; Osborn, 2001; Packham et al., 2004), while others claimed those characteristics have only minor or indirect effect (Kember et al., 1992, & 1994; Willging & Johnson, 2004). This study added additional evidence for the latter by showing that the persistent learners did not differ from the dropouts in their individual characteristics. In other words, learners’ age, gender, and educational level did not have a significant and direct effect on the dropout decision. Although the result does not claim that individual characteristics should be ignored, it can be concluded that individual characteristics have little influence on the decision to drop out and thus can be considered as trivial.
External factors such as organizational supports, financial problems, and time constraints have been known to be crucial obstacles to adults’ participation in learning because adult learners are associated with various roles in their lives (Darkenwald & Merriam, 1982; Johnstone & Rivera, 1965). It is true not only in traditional adult learning programs but also in online learning programs. Willging and Johnson (2004) claimed that external factors such as family issues, lack of organizational support, changing job, and workload are the main factors affecting the decision to drop out of online courses. Greer, Hudson, and Paugh (1998) emphasized family and peer support for success of online learners. Rovai (2003) also emphasized the effect of nonschool factors that conflict with academic life on students’ decision to drop out. Although these studies have already claimed the influence of external factors on the dropout decision, these studies are limited in that the first two used only a limited number of subjects and the third one was based only on conceptual analysis. Accordingly, the results were hardly generalizable to learners in different environments, and additional empirical evidence was needed to support the contention. This study showed that dropouts were significantly different from persistent learners in external factors (i.e., family support and organizational support), and the results of this study are consistent with those of previous studies.
Adult learners are more likely to drop out of online courses when they do not receive support from their family and/or organization while taking online courses, regardless of learners’ academic preparation and aspiration. Internal factors such as course design strategies and learners’ motivation should be prioritized at the course development stage in order to make the course participatory and interesting and to keep learners engaged. Once the course is launched and in progress, however, course administrators and instructors should consider external factors that might interrupt learners’ participation and persistence. It would be difficult for them to control the external factors. However, it is important to consider learners’ situation while managing or maintaining the course so that learners can get help if needed. In the event that an instructor knows that learners are not receiving enough support from their family and organization, he/she might help the learners stay in the course by paying extra attention, using appropriate motivational strategies, and providing additional internal support. In addition, course administrators and instructors need to inform learners’ family and organization of the advantages of the course in order to induce their supports. This study concludes that instructional designers should systematically analyze external factors surrounding learners and use the analysis results to initiate learning and motivate learners so that the high dropout rate can be decreased. The results of this study support the significance of those external factors that are easily overlooked by instructional designers.
This study also showed that dropouts had significant differences in perceptions of learner satisfaction and relevance from persistent learners. In other words, learners are less likely to drop out when they are satisfied with the courses, and when the courses are relevant to their own lives. In agreement with prior research (e.g., Levy, 2007; Doo & Kim, 2000), the results suggest that learners’ satisfaction with the online course and relevance to learners’ job, prior knowledge, and experiences are major factors affecting their decision to drop out or persist. From this result, a course designer or instructor might get insight about how to design the course better. In order to enhance satisfaction as a way to motivate online learners, rewards such as a completion certificate, praise, and promotion should be given to learners. By providing opportunities to apply newly acquired knowledge into real situations, learners can feel that the skills and knowledge obtained from the course are useful and satisfactory and thus they can be motivated to persist in the course. Relevance can be achieved by designing a course that contains learning materials and cases closely related to learners’ interests, experiences, goals, and so forth. Keller (1987) suggested that relevance could be established by using learners’ experiences, allowing learners to choose learning methods and strategies, and meeting learners’ expectations and goals. Online learners can easily lose motivation unless the course is designed to stimulate their active participation and interaction and meet their expectations. Therefore, an online course needs to be designed in ways to guarantee learners’ satisfaction and be relevant to learners’ needs.
The result from the logistic regression analysis reported the particular importance of organizational support and relevance, in addition to the class for which they registered. Organizational support and relevance were statistically significant predictors of learners’ decision to drop out or persist in online courses. The result implies that learners are more likely to decide to drop out when they are not getting organizational support for their learning. Often adult learners should be granted released time from their jobs and given encouragement from peers and/or supervisors to participate in the course. It is apparent that organizational support could be a crucial factor to influence learners’ dropout decision. The result also implies that learners who perceive that the course is relevant to their job or life are less likely to make a decision to drop out. Adult learners tend to prefer learning that has a practical purpose to learning for academic purposes only. Therefore, online courses should be designed in ways to allow adult learners to apply their learning to their real lives.
The sample of this study was selected from only one institution in the U.S. Thus, the results from this study may not be generalizable to adult learners in other institutions and/or countries. Further investigation is needed to confirm the generalizability of the results to broader populations. This study includes a limited number of variables even though they were chosen for their importance based on thorough review of the literature. There are many other variables, including two other motivation factors (i.e., confidence and attention), and factors associated with instructional strategies that may affect adult learners’ decision to drop out of online learning. Further research, therefore, is needed to involve additional relevant factors and to expand the model to better explain and predict adult learners’ decision to drop out of online courses.
Acknowledgement: This study was supported by the 2008 Hongik University Research Fund.
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