Maximising student acquisition and retention activity

A wise CEO with years of Further Education experience once advised me that “to truly understand the learners, you must know their data”. The notion of knowing learner data struck a chord with me – one that has become more and more relevant to maximising student acquisition and retention in Further Education.

The sheer volume of accumulated data that underpins every operation of a Further Education college is staggering!

From initial enquiry to enrolment, all the way through to achievement, course completion and beyond, there is a data story that underpins absolutely everything about every learner in every college.

In fact, it is a really an echo of the world we live in; fuelled by our insatiable appetite for communication, entertainment, education, business and connectivity; resulting in enormous amounts of valuable data– something Niall Sclater refers to as our ‘digital exhaust’ (Sclater, 2017).

Incredibly, our ‘digital exhaust’ is more than just a trail of what we do, when and where. It is a complex interlinked configuration of decisions, notes, information and behaviours spread across multiple systems.

If data analytics is the process of extrapolating predictive and wider insights from such data sources; Learning Analytics is the process of assembling, investigating, optimising and reporting on the learning experience through data insight and the extrapolation of actionable intelligence. In short, the data accumulated by your college in every single system is one of your most valuable and misunderstood assets. (Kelleher et al, 2017)

This notion is unquestionably important to every Further Education college as the financial pillars of acquisition, retention, progression and achievement – in a landscape of hard to engage millennials, diminishing cohorts, progression challenges and year-on-year Government funding changes – dictate almost all the educational funding rules.

The focused level of insight given by predictive learner analytics and a combined system dataset can allow a college to make swift financial decisions, interceptions, interventions and promote key changes within the curriculum that can have huge positive financial implications – as in the world of analytics, small changes can have an enormous impact.

Indeed, it is the popularity of disparate information rich systems in Further Education colleges that have hidden such valuable key insights. In such systems, where the information isn’t linked, all too avoidable issues ‘play’ out in the data and often remain unchallenged, unnoticed or unimportant until it is too late. Perhaps more significantly, the standard cause and effect modelling used in much of educational data reporting is often informed by the wrong data.

Let’s look at an example. One answer to a student acquisition problem could be to just spend more money on advertising courses and measure the impact through application numbers; cause and effect!

How many Further Education colleges do we see advertising course after course after course this way?

Systems that support FE Analytics – such as demographic, social media and website analytics combined and aligned with wider student data, application numbers, labour market information and curriculum planning costs would mean learner analytics could identify issues and data patterns and make allow the college to take immediate decisions and actions.

One college I worked with found their issue with the acquisition and retention of students had a combined cause of:

• Geo-demographic instability (targeting students with the wrong information from the wrong local areas),
• Historical curriculum inaccuracy (courses were not aligned to the local and regional market intelligence and employability trends) and
• Marketing inaccuracy (the website course information and search was killing more leads than it generated).

This was already affecting enrolled learners some of whom had a significantly higher risk of poor achievement or failure. Only by combining and analysing the data using a predicative analytical model did any of these patterns reveal themselves and thus offer potential actions to mitigate.

Case in point; immediate changes to the web site functionality at the same college as previously mentioned on the back of the analysis turned a 20% enquiry to lead and a 2% lead to enrolment conversion into a 30% enquiry to lead and a 20% lead conversion.

That’s the difference between £32K and £480k of potential income – remember small actions based on the data can make a big difference.

Graham Raddings, Associate, MCA Cooper Associates

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