Online distance education has become another way for institutions of higher learning to reach out to learners. However, learner retention rates in online distance education tend to be well below those in traditional full-time higher education. A clear understanding of what factors contribute to learner attrition, including withdrawal from or non-completion of courses, is necessary in order for online distance learning institutions to formulate appropriate retention strategies. This case study looks at the role of proactive interventions practiced by institutions to retain learners through an examination of data collected from departments within an online distance institution in Malaysia. Results suggest that among the top reasons learners drop out are those related to family matters, job commitments, and poor time management practices. This preliminary study aims to assist online distance institutions in better understanding the pattern of their learners' withdrawal, with the hope that it will lead to the development of guidelines for a more systematic evaluation of proactive retention intervention techniques to be implemented to reduce attrition rates in such institutions. (Abstract by author)

Q&A with University of Phoenix Senior Academic Counselor, and distance education advising thought leader, Steven Starks. We discuss retention best practice, accommodating tight budgets and he suggests some tools that administrators could find helpful.

There is a wealth of both anecdotal and sound research evidence which demonstrates a clear link between a student’s engagement with their learning community and their academic outcomes. Having a sense of connection and engagement with the institution, through their contact with lecturers, fellow students, other university staff, can make all the difference between persistence and academic success; and disillusionment, disappointment and abandonment of studies. With the expansion of online studies, an additional challenge for all of us working with tertiary students is to develop and implement strategies through the use of appropriate technology, by which students can be successfully engaged and supported. This paper discusses some of those challenges for the Student Success and Support Services team at Open Universities Australia and the strategies being developed in order to meet them. (Abstract by authors)

One of the key interests for learning analytics is how it can be used to improve retention. This paper focuses on work conducted at the Open University (OU) into predicting students who are at risk of failing their module. The Open University is one of the worlds largest distance learning institutions. Since tutors do not interact face to face with students, it can be difficult for tutors to identify and respond to students who are struggling in time to try to resolve the difficulty. Predictive models have been developed and tested using historic Virtual Learning Environment (VLE) activity data combined with other data sources, for three OU modules. This has revealed that it is possible to predict student failure by looking for changes in user’s activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour. More focused analysis of these modules applying the GUHA (General Unary Hypothesis Automaton) method of data analysis has also yielded some early promising results for creating accurate hypothesis about students who fail. (Abstract by authors)

Online education is becoming an increasingly important component of higher education. The Sloan Foundation 2010 Survey of Online Learning reports that more than 30% of all students take at least one online course during their college career. Because of this, attention is now turning to the quality of student outcomes that this instructional method provides. However, there is a huge gap in empirical investigations devoted to the link between technology and performance indicators such as grade performance, re-enrollment and course completion (Nora & Plazas Snyder, 2008). This study found that prior online course experience is strongly correlated with future online course success. In fact, knowing a student’s prior online course success explains 13.2% of the variation in retention and 24.8% of the variation in online success in our sample, a large effect size. Students who have not successfully completed any previous online courses have very low success and retention rates, and students who have successfully completed all prior online courses have fairly high success and retention rates. Therefore, this study suggests that additional support services need to be provided to previously unsuccessful online learners, while students who succeed online should be encouraged to enroll in additional online courses in order to increase retention and success rates in online learning. (Abstract by authors)

Improving student retention is a key area where many universities can bolster student satisfaction, graduation rates, and make financial savings. To achieve these goals, it is necessary to understand both why and when students drop out, even when they do not make their problems or intentions clear to the lecturer. Post-module analysis can identify general problems with course structure or content that may be rectified for future sessions. In addition, during a session, a number of the failing students could be retained if they were offered appropriate assistance. The problem is how to identify these students in time to help them, even when they do not seek assistance. (By authors)

In this article, the author describes a story about Bernie, who found a strong mentoring relationship with a faculty member--a sympathetic ear, a willing advisor, someone who cared about whether he stayed or left the institution. The story points to the power of advising, communicating, and mentoring in student success and persistence to graduation. It's about building relationships with students, locating places where they get disconnected, and helping them get reconnected. And it demonstrates the powerful effect that out-of-class interactions with a faculty member can have on student persistence. Academic advising is more than clerical recordkeeping; it is the very human art of building relationships with students and helping them connect their personal strengths and interests with their academic and life goals. (Abstract by author)

As the growth of online programs continues to rapidly accelerate, concern over the retention of the online learner is increasing. Educational administrators at institutions offering online courses, those fully online or brick and mortars, are eager to promote student achievement. Retention is critically important, not just for student success, but also for the success of these institutions of higher education. Models for understanding student persistence in the face-to-face environment are well established; however, many of the variables in these constructs are not present in the online environment or they manifest in significantly different ways. With attrition rates higher than in face-to-face programs, the development of models to explain online retention is considered imperative. This study moves in that direction by exploring the relationship between student demographics and interactions, and retention at a arge online university. Analysis of data, which included an n of 20,569, provides an illustration of the importance of transfer credit and the consistency of activity in predicting continued enrollment. (Abstract by authors)