Does identifying and addressing academic difficulties early on contribute to enhanced student success and higher retention rates for a distance learning course?

In UK universities there is a problem with academic under-performance, failure and dropout of students enrolled on programming-based courses such as computer science & software development. One way to address the issue of high dropout rates in these courses is to implement targeted interventions for students who are at risk of failing or dropping out. By providing timely interventions to students who are struggling, it is possible to improve academic performance and decrease dropout rates. This requires the ability to quickly and accurately identify these students and provide them with the support they need. One challenge with current approaches for identifying students at risk of academic failure or dropout is that they often do not identify these students until it is too late to provide meaningful interventions. To improve the effectiveness of interventions and support for at-risk students, it may be necessary to consider additional sources of data and to implement interventions earlier in the academic process. When working with students in a distance learning programme the problem is more complex than when working with those enrolled on campus-based programmes. The nature of distance delivery means that academic staff are often denied the opportunity to regularly observe a student's performance in a classroom or computer laboratory setting. Furthermore, the literal remoteness of a distance teaching modes often stands between an academic and a struggling student and often blocks the possibility of a quick and informal chat where the student might have outlined their academic difficulties. These are both classic examples of on-campus triggers for intervention that could help to support a student; in a distance learning setting these triggers are much less likely to happen. Our approach to identifying students at risk of academic failure or dropout involves using a wide range of data sources, including pre-matriculation socio-demographic data, aptitude test scores, assessment results, attendance data, and Learning Management System (LMS) activity data. This diverse range of inputs can provide a more comprehensive and accurate picture of a student's academic performance and risk of struggling in their studies. We frequently recalculate the prediction of likely academic success for each student, which helps to avoid the issue of "staleness" by using the most up-to-date data available. This can help to ensure that interventions are timely and tailored to the needs of each student.