Successful retention of students through understanding their motivations and behaviours is a challenge to universities worldwide. Whilst the impact of withdrawals is an issue for all institutions, attrition for distance-learning providers is particularly problematic owing to higher non-completion rates, less physical visibility, and because distance-learners tend to have more complex lives. This paper examines students' personal stories explaining their decisions to withdraw from university study. It considers 641 written discourses initiated by students as part of their requests to withdraw, covering the challenges they face, and the complex combinations of factors that contribute to their decisions to give up. This qualitative approach was adopted as a necessary complement to the quantitative rush of metrics information that universities now provide on withdrawal figures. Three themes selected are: deferral/withdrawal, time available, and preparedness for study. The paper concludes that complementary qualitative insights both add clarity and detail to institutional understanding and reduces oversimplification of complex decision-making from unidimensional quantitative approaches.

Changes to student funding, in parallel with the introduction of new technologies into teaching and learning, and a blurring of traditional boundaries between full and part time study have brought an increased focus on retention in higher education. As more providers look towards offering online portfolio options, this is likely to increase; with some evidence that drop-out on online-led modules can be higher. As many classroom-based behavioural indicators of student retention are reduced or absent in the online environment, institutions can be slower to understand that students are considering dropping out, with the first indication of problems being when withdrawal takes place. This paper draws on data from online interactions among 3000 students studying an introductory Business Studies module at the Open University to identify early online indicators of potential drop-out. Findings suggest that both erratic interactions, and/or marked reduction in activity level from previously active students are predictors of subsequent withdrawal. Existing demographic data (on age, gender, etc.) used by the institution has historically allowed broad 'at risk' categories of student to be identified; coupling this information to some of the online behavioural indicators revealed through new tracking technology allows more precise targeting of individuals more promptly than historically. This may potentially change a student support model from reactive to proactive; allowing the institution to offer additional support as soon as such signs emerge which improve student retention. Universities seeking to increase student retention may find early warning of vulnerable students useful in targeting appropriate interventions.

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