Why FE colleges need “big data” in the battle to recruit apprentices

6 Jul 2019, 5:00

Smart technology that transforms data management is available in an FE college near you, says Richard Alberg

Big data is everywhere; it enables the analysis of how we shop, vote and even make our way around cities. But what can it do for further education, particularly when it comes to the highly competitive apprenticeship market?

Technological innovations have amplified our capacity to store large data sets and use them to understand human behaviour. Whether by default or design, FE colleges, like education in general in the UK, have yet to fully embrace the potential of big data to help recruitment, retention, completion, career, course innovation and business development, to name but a few benefits.

The reluctance in some quarters is understandable. Data, as the academic Rebecca Eynon points out, comes with ethical dilemmas over privacy and its potential use to whittle out failing students, teachers and institutions. Furthermore, making sense of the vast amount of raw data out there is a huge job. You can download local apprenticeship market data from the Education and Skills Funding Agency (ESFA), but these come as massive spreadsheets. Understanding the trends that can inform decisions elude all but the most motivated.

However, I would argue that data should be essential to the work of the FE sector, since apprenticeships are one of the fastest-growing post-16 training routes. Apprenticeships are complicated. Each learner is an individual with her/his idiosyncrasies. Programmes generally last for more than 12 months and are split between workplace and training centre. Add in compliance, audit and Ofsted, and it is easy to see how, with multiple learners, there are many moving parts where things can go wrong.

Colleges must also decide which programmes to offer. Apprenticeship standards require curriculum development, an expensive undertaking. Ensuring that the college invests its scarce resources in the right areas matters.

Using data to analyse learner progress is also a challenge. Data has to be exported from several systems – CRM, ePortfolio, LMS and funding – and then brought together. It can take an analyst several days, so unsurprisingly it is rarely done. Since there are often errors in the data, management can become sceptical about the information and revert to gut instinct.

Is there a solution?

For a while now, some of the more sophisticated training providers have been investing in very smart technology that transforms data management. Two elements are available, and we are on the cusp of a third.

We are on the threshold of using artificial intelligence in education

The first is end-to-end delivery in a single technology platform and one database, which helps administrative efficiency and eliminates double-keying of data. And it means that any required information can be generated instantaneously from this single database. There is no need to bring diverse data sets together because it is all in one place. Many industries, from medicine to banking, use this kind of structured data to help with regulatory compliance.

The second element is about interpreting and processing the data. Business intelligence tools, such as Microsoft’s Power BI and Tableau, are becoming popular. These allow you to select fields from a database and then view them visually, drilling in and out as required, making the intelligent business management decisions that lead to success.

For example, we created an interactive mapping project from ESFA apprenticeship data, that shows which apprenticeship standards and frameworks are being delivered in localities across all providers. Data like this enables college managers to plan around growth areas and market gaps with greater ease.

Finally, I believe we are on the threshold of using machine learning or artificial intelligence in education. Much of how it will evolve is still speculation, but machine learning can draw on the vast array of structured data to identify institutional patterns around efficiency, retention and completion rates.

And much like the recommendations we receive from Spotify or Netflix, algorithms can target students with courses or career options and can deliver personalised learning.

There is no getting away from the inevitable. Colleges that embrace data will be the colleges that prosper.

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