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Tomorrow's Teaching and Learning
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Concerns for Online Student Retention
Online student retention has been a major topic of discussion in higher education for more than a decade. This discussion has focused on student dropout (or attrition) and persistence. Most articles have provided anecdotal information or individual studies carried out by universities (Angelino, Williams, & Natvig, 2007). In the past decade, there have been a few national reports on student enrollment, but none has focused specifically on dropout or persistence. What has been widely addressed in the literature is the comparison between the effectiveness of online learning and traditional learning.
Although studies support the effectiveness of learning online compared to learning in the traditional classroom (Hobbs, 2004; Tallent-Runnels et al., 2006), students often fail to complete online courses. In some studies, it is noted that as many as 50-70% drop out of their online courses or programs (Carr, 2000; Roblyer, 2006; Rovai & Wighting, 2005; Simpson, 2004).
Among the reasons for student dropout are feelings of isolation, frustration, and disconnection; technology disruption; student failure to make contact with faculty; inadequate contact with students by faculty; lack of student and technology support; lack of instructor participation during class discussion; lack of clarity in instructional direction or expectation; and lack of social interaction. Another way to view the dropout problem is to look at the factors for student persistence in online education.
These factors can help us determine what strategies are needed to retain students, reduce dropout rates, and help students persist in online courses or programs.
Reasons Online Students Drop Out
A review of the literature reveals many reasons for online student dropout. For example, Hara and Kling (2001) and Palloff and Pratt (1999, 2005) address the physical separation of individual students in online education as a reason for their feeling isolated and a major cause of student confusion and anxiety, leading to problems with course retention. The findings of Motteram and Forrester (2005) and Abel (2005) reveal that technology failure and lack of instructor feedback are also reasons for online student dropout. In the online environment, students tend to become frustrated when technology does not function well and lose confidence in their work when they do not receive instructor feedback. For these reasons, technology and student support are essential.
One way for providing support for students is through contact. Motteram and Forrester (2005) say that students rate contact with faculty as more important than contact with other students. Contact can be either proactive or reactive (Simpson, 2004).
While proactive contact or intervention means "taking the initiative to contact students either in a teaching or an advisory environment" (p. 80), reactive contact involves responding to student-initiated communication. Proactive contact with a student or interventions from the institution can have an impact on the retention of online learners. Although both proactive contact and reactive contact are important, proactive contact is gaining more attention because students who do not make contact with available systems may be more likely to drop out (Simpson, 2004).
Another way to support students is related to instructor assistance. Chyung and Vachon (2005) found that lack of instructor participation during class discussion and lack of clarity in instructional direction or expectations can cause confusion and frustration and are reasons that students drop out. Inadequate assistance from instructors can also create student dissatisfaction in the online environment and has implications for student retention.
Other reasons that online students drop out were described by Muilenburg and Berge (2005), who identified eight barriers to online learning. We grouped the eight barriers into three categories: skill level, motivation, and support. In Muilenburg and Berge's study, students identified the barriers to their skill level as academic and technical. In the academic area they lacked skills in reading, writing, or communication. In the area of technical skills they feared the use of new tools and software and their unfamiliarity with technical tools for online learning.
Motivation barriers were intrinsic and extrinsic. Intrinsic motivation barriers included the characteristics of procrastination, selecting easier aspects of an assignment to complete, or the feeling that the online learning environment was not innately motivating. Extrinsic motivation barriers involved social interaction in which the students felt a lack of peer collaboration online, absence of social cues, or fear of isolation in online courses (Muilenburg & Berge, 2005).
In the area of support, administrative, financial, and technical issues were considered barriers (Muilenburg & Berge, 2005). Administrative issues merge when administration had control over course materials and the materials were not delivered on time, when academic advisors were not adequately available online, and when there was a lack of timely instructor feedback. Financial barriers occurred when access to the Internet was too expensive. Technical issues arose when there was a lack of consistent platforms, browsers, and software; in addition a lack of technical assistance caused obstacles to learning.
The barriers cited in Muilenburg and Berge's (2005) study are basic reasons for online student dropout. These reasons can create student frustration, dissatisfaction, lack of confidence, loss of focus, and lack of motivation and have implications for the ability of students to persist in online courses and programs. Table 1.1. summarizes the common reasons for online student dropout and how they affect students.
Table 1.1. Common Reasons for Online Student Dropout
Common Reasons for Online Student Dropout How Reasons Affect Students
Physical separation Feeling of isolation and disconnection
Low academic skill level Leading to remediation in reading, writing, or communication
Low technical skill level Fearing technology and new software
Lack of intrinsic motivation Leading to procrastination
Lack of extrinsic motivation Feeling of isolation
Lack of faculty contact with student Leading to dissatisfaction
Lack of clarity in direction Leading to loss of focus
Lack of expectation Feeling of confusion
Technology failure Feeling of frustration and loss of confidence
Lack of administrative, financial, and technical support Causing obstacles to learning
Lack of instructor feedback Feeling of frustration and loss of confidence
Factors for Student Persistence in Online Education
Persistence means continuing decisively on a course of action in spite of difficulty or opposition. Findings from several studies of student persistence in online higher education have helped us look at the factors involved in retaining students and reducing dropout rates. One model that struck us in looking at persistence in the online environment was Rovai's (2003) composite persistence model, a combination of other models related to persistence.
In his model, Rovai (2003) includes the following elements: student characteristics and student skills (prior to admission) and external and internal factors (after admission). Using this model, institutions can detect students who are at risk to become dropouts and determine intervention methods. For example, if an institution knows the deficiencies in an online student's academic preparation and skills prior to admitting the student, the institution can rectify these deficiencies with early intervention.
Once a student is enrolled in an online program, the model can be used to recognize external factors to help with student persistence. External factors include non-school issues that conflict with academic life, such as financial need or child care arrangements. Internal factors are affected by the student's needs and include consistency and clarity of online programs, policies, and procedures; self-esteem; feeling of identify with the school; social integration; and ready access to support services.
One study that addresses persistence from the student's perspective on online participation is Tello's (2007). His study found that student perceptions about their contributions in asynchronous discussion forums and students' frequent use of the forums accounted for 26% of the variance in course persistence rates. This finding shows that the interactive strategies used in the course affected student attitudes and helps explain why students persist or withdraw from online courses.
Another study that caught our attention was Müller's (2008) investigation of undergraduate and graduate women learners' persistence in online degree completion programs. Her findings suggest that multiple responsibilities and insufficient interaction with faculty, technology, and coursework are the major factors for women's lack of persistence. However, motivation to complete degrees, engagement with the learning community, and gratitude for the convenience of completing a degree online supported persistence. It appears that a learning community approach in an online course or program can be a strategy for retaining students (Brown, 2001).
Park and Choi's (2009) study on factors influencing adult learners' decision to drop out or persist in online learning revealed that persistent learners and dropouts differ in their individual characteristics, course design factors, and workplace support factors. In their study, females accounted for 74.5% of persistent learners and 65.3% of dropouts. Learners in both groups ranged in age from 20 to 39 years old. In the dropout group, age ranged between 20 and 29 years old, the equivalent of 26.5%. Their findings showed that by addressing course design, such as enhancing the relevance of the course, institutions could have lower dropout rates. The results also indicated that adult learners need support from their workplace to persist in and complete their online courses.
Another study that addressed factors in online student persistence was McGivney's (2009) investigation of the persistence of online adult students in two community colleges. This study showed that the strongest predictors of course completion were the desire to complete a degree, previous experience in online courses, and assignment completion. These findings give us clues about how important it is to understand learners' characteristics and how prepared students are for the online environment to help them persist in their online courses and programs.
Rovai's (2003) composite model provides us with a framework to create an environment conducive to a successful online learning experience. It is critical for institutions to recognize student characteristics and skills prior to admitting a student to an online program. As McGivney's (2009) findings indicate, previous experience in online courses is a predictor of persistence. It is also essential for institutions to be aware of factors that influence student academic life. Müller's (2008) findings cite students' multiple responsibilities, and Park and Choi's (2009) findings address the importance of workplace support. These factors influence how well a student can do after admission to an online program.
The internal factors in Rovai's (2003) composite model encompass institutional interventions and are the ones over which institutions have the most control. Institutional interventions are based on student needs, pedagogy, and institutional support, which can be translated into design, instructional, and support strategies in the classroom. Based on the persistence literature, there is no simple formula to guarantee student success in online learning because success involves a variety of factors. Institutions control the services they provide, but not external factors. When factors external to the institution come into play with factors internal to the institution, however, the institution needs to understand its learners, use appropriate strategies, and provide effective support in order to retain students and avoid dropout.
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