Predictors of Success for Adult Online Learners: A Review of the Literature
February 11, 2010
As an adjunct professor for an online-only graduate school course, I have found myself contemplating the retention rate of adult e-learners.
What are the predictors of adult students' success in online learning environments? Is there a difference in undergraduate versus graduate online learners and their motivations? Does age play a factor? Do the course's characteristics have an impact on performance and learner satisfaction? What are the implications of these findings for online instructors, and how could that affect their practices and approaches to retaining students in the future?
Here, I examine what the literature says about these questions.
Predictors of Adult Students' Success in E-learning Environments
E-learning has become an expected part of higher education in recent years (Larreamendy-Joerns & Leinhardt, 2006; Tallent-Runnels et al., 2006; Zandberg & Lewis, 2008). In fact, more than a decade ago Moore and Kearsley (1996) found that an overwhelming majority of distance education students were between the ages of 25 and 50.
Since online enrollment continues to grow, so does the scholarly interest in students' educational motivations in such courses (Dabbagh & Kitsantas, 2004; Green & Azevedo, 2007). However, to be successful in an online only learning environment, students should be well-motivated, autonomous learners, who are able to self-regulate their learning experiences (Artino & Stephens, 2009).
A motivated student may be defined as one who seizes the opportunity to learn—the opposite of a procrastinator. The motivated learner will stick with the class even in the face of adversity.
Autonomous, self-regulated learners committ to controlling their own learning experiences. Some of the ways that this self-regulation may be displayed is by seeking help when they lack understanding, believing in their own capabilities, rehearsing the material to be learned, organizing the material to be learned, and holding an intrinsic belief in the value of learning (Boekaerts, Pintrich, & Zeidner, 2000; Schunk & Zimmerman, 1998).
Many active, self-regulated learners use their past experiences and the context of their present virtual classrooms to set goals for their learning. Their goals become a standard against which they compare their progress (Green & Azevedo, 2007; Pintrich, 2000). Highly motivated and autonomous, self-regulated learners are needed in e-learning environments because of the autonomous nature of the online classroom, in comparison to a traditional classroom (Dabbagh & Kitsantas, 2004).
Graduate vs. Undergraduate Motivations
Differences have been noted between undergraduate and graduate distance learners and their motivations. Even though many graduate students are less experienced with online learning and technologies, they were more likely to be self-motivated, to utilize critical thinking skills, and were less likely to procrastinate when compared to their undergraduate counterparts (Pintrich, 1999; Wolters, 2003). In contrast, undergraduate students were more likely to procrastinate and less likely to use in-depth critical thinking skills (Wolters, 2003).
Age as a Factor in Online Learners' Success
The literature supports the idea that because adult learners are not as technologically savvy and have more responsibilities toward work and family, online learning is more difficult for them (Dubois, 1996). However, Ke and Xie's (2009) study showed that regardless of an adult learner's age, students self-reported the same amount of effort put into learning tasks and reported comparable levels of satisfaction.
Artino and Stephens (2009) saw differences between undergraduate and graduate online students: even though undergraduates were more likely to procrastinate, they were also more likely to show greater continuing motivation to enroll in further e-learning courses and reported valuing and benefiting from online classroom tasks. The majority of the undergraduates in the study were non-traditional students—working adults between 25 and 50 years old. Their age and circumstance may have played a part in the outcome (Artino & Stephens, 2009).
On the other hand, Hargis (2001) points out that age alone will not predict online learning outcomes. Since more and more online students are between the age of 25 and 50, further studies that explore differences in non-traditional versus traditional learners may be beneficial in helping instructors and universities to better understand the motivational differences of these demographics and compensate practices and design accordingly (Artino & Stephens, 2009).
Design Model Characteristics and the Impact on Performance and Learner Satisfaction
Understanding the nature of online learning helps educators and schools implement online courses (Moore & Kearsley, 1996). High-quality course designs should include certain features within their makeup. Cercone (2008) suggests that course design models:
- connect new knowledge to prior learning
- maintain collaboration and social interaction between students
- promote a self-reflective environment
- include current or immediate applications
- advance self-regulated learning.
These components in the design of a class lead to deep learning as opposed to just surface learning (Fink, 2003; Majeski & Stover, 2007). Deep learning proves successful and provides satisfaction by engaging the whole learner in the learning process, socially, cognitively, and affectively (Fink, 2003; Garrison, Anderson & Archer, 2000). Deep learning permeates across all age groups and all types of learners. Distance learning should be desirable to all adult learners, regardless of age, and promote lifelong learning (Cahoon, 1998).
Implications for Online Instructors: Practices to Promote
In light of the findings, it would be wise for instructors to implement practices that cultivate self-regulation and critical thinking in their students (Green & Azevedo, 2007). Teachers may need to provide varying levels of support and guidance for their undergraduate and graduate students. Undergraduates, for example, may require more explicit support that will help them self-monitor (Artino & Stephens, 2009).
Providing reflective prompts is one way to support all online learners (Davis and Linn, 2000). Making specific and clear syllabi and assignments with progressive calendar deadlines may encourage task completion and improve self fulfillment (Liu, Bonk, Magjuka, Lee, & Su, 2005; McLoughlin, 2002).
Other strategies that have improved self-efficacy in both undergraduate and graduate learners, are to provide students specific performance feedback on a timely basis (Bangert, 2004; Wang & Lin, 2007), as well as assist students in identifying and setting challenging yet reachable goals (Dabbagh & Kitsantas, 2004).
In regard to online discussions or discussion board prompts, undergraduates in particular may benefit from instructor assistance, or scaffolding in such a way that promotes critical thinking. Some examples of instructor-enhanced scaffolding within the prompts includes modeling a response to the prompt, requesting clarification, reinforcing students' ideas, correcting misunderstandings, and asking for consensus within areas of disagreement (Anderson, Rourke, Garrison, & Archer, 2001; Shea, Li, Swan, & Pickett, 2005). These practices may improve learner interaction; increase satisfaction; increase retention; and facilitate critical thinking and self-regulation in students (Shea et al., 2005; Whipp, 2003).
Implications for Online Instructors: Approaches and Techniques
Instructors should consider different approaches and techniques that they may utilize to maximize students' success. Online learning models may incorporate both asynchronous and synchronous communication tools.
Asynchronous tools include applications such as email, discussion boards, newsgroups, and conference rooms where users are allowed to contribute at their leisure, but are not required to be online at a specific time. Asynchronous forms of learning lend more to self-reflection and deep learning as posited earlier (Hiltz & Goldman, 2005; Jaffee, Moir, Swanson & Wheeler, 2006).
Synchronous tools include chat rooms, webcasts, desktop video, and audio technologies. These tools are used to simulate real-time teaching strategies, like meeting with groups of students or delivering lectures or presentations. The synchronous activities may help foster a sense of community to facilitate learning a complex body of knowledge (Schwen & Hara, 2004; Vrasidas & Glass, 2004).
Three different types of learning experiences may be fostered within an online community. These approaches are 1) expository learning, 2) active learning, and 3) interactive learning. The type of learning provided may determine the way the learner acquires knowledge.
Expository learning is a conventional approach to learning where the information is given to the student through a lecture or via written material. Active learning involves the learner having control over how and what she learns. Learning is inquiry-based, such as working on manipulation of artifacts, simulations, web quests, or games (Zhang, 2005). Interactive learning emphasizes collaborative learning activities where the learning develops from interaction with others or other knowledge sources within the course. Teachers may be facilitators in such learning (USDOE, 2009).
In determining which approach to use and when, the instructor should 1) remember what has been said thus far about learning and best practices, 2) consider the student group to be served, and 3) consider how the learning should best emerge. Think of using technology as a tool to foster deep learning and critical thinking skills (Fink, 2003; Garrison, Anderson & Archer, 2000; Majeski & Stover, 2007). With expository instruction, the technology is conveying the content. With active learning, the technology is allowing the learner to be in control of the learning by investigation of information or of problems. With interactive learning, the technology is mediating the interactions of learners and allowing learning to emerge (USDOE, 2009).
Implications of Findings for Online Instructors
The U.S. Department of Education (2009) says earlier online programs typically utilized either asynchronous or synchronous applications within their courses. The Department suggests that combining these types of forums in online classrooms is catching on. It has also become more of a common occurrence to provide a blended model of instruction where, in addition to the online format, an occasional face-to-face class will take place.
However, a U.S. Department of Education study (2009) found that when comparing an online-only versus a blended classroom, the learning outcomes are similar. Thus, learning will emerge and provide a similar success rate in online-only classes with or without a face-to-face component.
With the convenience of distance learning, I see the trend pushing more toward the online-only realm, especially since the study showed comparable learning gains. Additionally, the study found that active learning strategies enhance learning and foster self efficacy and intrinsic motivation, as Shea et al. (2005) and Whipp (2003) say are necessary for critical thinking and deep learning.
Interactive learning is another trend that will prompt learner reflection and assist with developing a sense of community, as noted by Schwen and Hara (2004) and Vrasidas and Glass (2004). Instructors should focus on this trend to promote the community feeling that students tend to express they feel is lacking within online courses. Students say online learning is what they turn to for flexibility and because of their busy lifestyles (Green & Azevedo, 2007). As a result, instructors should change their teaching styles to be just as flexible and accommodating to all learners by incorporating the various strategies discussed and by being easily accessible to students. National University's (NU) School of Education, where I am an adjunct professor, has a 24 hour "return policy": If a student contacts a professor, the professor should get back to the student within 24 hours by email or phone.
In regard to student satisfaction and retention, I suggest that instructors and universities consider promoting an environment of continuous improvement by allowing students to anonymously complete a survey at the end of the class to assess their learning and improvement and the instructor's teaching and course management. The quality of an instructor is an important determinant of an effective learning system (Khan, 2005; Selim, 2007; Wang, Wang & Shee, 2007). If measured, it will get attention (Eccles, 1991).
NU has a policy of asking students to complete course evaluations, and instructors ratings are reviewed by deans and heads of departments. Instructors' ratings play a part in whether they are asked to teach that class again. About half of my students complete the evaluations for my course, but this only gives me and the other faculty a partial picture of my effectiveness as an instructor. I'd suggest universities give students an incentive for completing course evaluations to increase the likelihood of a higher return.
The studies reviewed suggest that students are using their past experiences and the context of their present online environment to set goals for their learning where the goals set become a standard against which to compare their progress (Green & Azevedo, 2007; Pintrich, 2000). Thus, instructors should look to mirror this idea within their own teaching of future classes.
Instructors should also use evaluations or other student feedback to drive their future instruction (Stanford & Reeves, 2005). Just as technology is always changing and evolving to meet the students' needs, so too should the instructor's teaching and management of the course.
About the Author
Elizabeth A. Gruenbaum holds an MS in education leadership from Nova Southeastern University (NSU) and an EdD in organizational leadership and educational leadership also from NSU. Her prior experiences as a program director, coordinator, teacher leader, mentor teacher, and as a current adjunct professor for National University's School of Education give her a distinctive background in understanding and motivating others for success. She recently completed research for her dissertation regarding teacher satisfaction and retention in evaluating a new teacher induction program.
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