Signals from the Silent: Online Predictors of Non-success in Business Undergraduate Students

Abstract

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.