A series of quantitative non-experimental correlational studies examined the associations among the predictor variables of nontraditional online learners’ self-regulation, self-direction, self-efficacy, and integration with the criterion variable of persistence. The finding of these studies will be discussed and attendees will be engaged in a discussion about implications for faculty, administrators, designers, and students. The development of an online orientation course for non-traditional, online learners based on the studies’ findings will also be presented.
Online education has allowed nontraditional learners to gain skills and knowledge in a manner that is both flexible and convenient, which is essential given their family and career responsibilities. Online nontraditional learners are usually adults between the ages of 25 and 50 with families who are enrolled in online courses and programs (Moore & Kearsely, 2005). According to the U.S. Department of Education’s National Center for Education Statistics (2002, 2015) these nontraditional leaners have specific characteristics, which include part-time or full-time enrollment, delayed postsecondary enrollment, independent for financial aid purposes, employment that exceeds 35 hours per week, primary caregiver of a dependent, and completion of a GED or high school diploma. The population of nontraditional learners in online courses has steadily increased over the years (Seaman, Allen, & Seaman, 2018), with enrollment in online programs among those aged 25 increasing 35% between 2001 and 2015. The number of nontraditional learners is projected to increase by another 11% by 2026 (Hussar & Bailey, 2018) across undergraduate, graduate, and doctoral programs.
Unfortunately, higher education and corporate administrators have indicated that one of the biggest challenges for nontraditional learners enrolled in online courses is attrition (Corporate University Xchange, 2000; Allen & Seaman, 2013). For over two decades, researchers have documented a higher dropout rate for online learners as compared to their face-to-face counterparts (Angelino, Williams, & Natvig, 2007; Bawa, 2016; Bloemer, Swan, Day & Bogle, 2018; Carr, 2000; Heyman, 2010; Hiltz, 1997; Faulconer, Griffith, Wood, Acharyya & Roberts, 2018; Fetzner, 2013), with dropout rates being 7 to 20 percent higher for online courses and programs (Hachey, Wladis & Conway, 2013; Patterson & McFadden, 2009; Smith & Ferguson, 2005).
While some argue that factors related to a nontraditional learner’s decision to drop out are not within the control of the institution (Diaz, 2002), many theorists and researchers have criticized this argument noting that higher education institutions need to understand how to mitigate attrition and promote persistence, given its association with accreditation and funding (Patterson & McFadden, 2009). Tinto (2017) recommended that institutions consider persistence from the perspective of the learner and assist the learners to “seek to persist” (Tinto, 2017, p. 254).
Several theories and theoretical frameworks have been developed to explain the complex factors associated with learner persistence and attrition. Tinto’s learner integration model (1975, 1987, 1993) and Bean and Metzner’s (1985) framework are among the most often applied and extended in research, even in online persistence and attrition studies (Park & Choi, 2009; Rovai, 2003). Rovai (2003), in his synthesis of Tinto’s and Bean and Metzner’s works with online learner literature, suggested non-traditional learners’ needs, skills, and characteristics as well as integration may be central to their persistence. This framework, which has been relatively untested, provides a foundation for the development of a model of non-traditional, online learners’ needs, skills, and characteristics and integration for understanding their persistence.
Based on Rovai’s (2003) model and previous research demonstrating the salience of non-traditional learners’ needs, skills, and characteristics and integration for online persistence, we posited that learner needs, skills, and characteristics of self-regulation, self-esteem, and self-directedness and integration predicts online, nontraditional learner persistence at the undergraduate, graduate, and doctoral level (Baier, et al., 2016; Barnard-Brak et al., 2008; Bradley et al., 2017; Chu & Chu, 2010; Concannon et al., 2018; Drago, et al., 2018; Huang & Mayer, 2018; Means, et al., 2009; Shea, 2010; Shea, et al., 2010; Song & Hill, 2007). We conducted a series of correlational studies and used logistic regression analyses to examine this hypothesis, finding that self-regulation, self-efficacy, self-directedness, and integration are correlated with online learners’ persistence. The findings cohere with traditional persistence and attrition models’ (Bean & Metzner, 1985; Tinto, 1975, 1987, 1993) presupposition that persistence is influenced by personal factors. However, findings also provided evidence that classic theories need to be expanded to more specifically explain and identify, as Rovai (2003) purported, the learner characteristics, needs, and skills that predict persistence of this unique population of nontraditional, online learners. Also, significant to recognize is that the salience of these variables differs across levels of education. In this presentation, we will discuss the results of these studies, recommendations for future research, and how to translate the findings into practice for university personnel for designing and facilitating online courses and programs at the undergraduate, graduate, and doctoral levels. We will engage attendees in an interactive discussion about these implications, soliciting their feedback and ideas. One researcher will share how she developed an online orientation course for incoming undergraduate, non-traditional online learners based on the studies’ findings.