Training company Smart Apprentices seek to develop innovative software platform using machine learning and predictive analysis to encourage apprentices to complete their training.

The Company

Smart Apprentices Ltd (SAL), based in Street Ashton in Warwickshire, provides innovative software solutions to support the delivery of apprenticeship schemes, delivered as a Software-as-a-Service (SaaS) business model. Its software platforms cover all aspects of apprenticeships such as recruiting, Virtual Learning Environments, compliance tools and endpoint assessments. Their clients are approved UK apprenticeship providers, ranging from further education colleges, employer providers, independent training organisations and endpoint assessors.

SAL has an established reputation in the UK apprenticeships market and their current key target clients are UK apprenticeship providers with >500 apprentices. The company currently supplies 140 out of a total 400 of these. 

The Problem

The market has become more complex since reforms to the Apprenticeship Levy (May 2017) resulted in declining numbers of larger providers, increasing competitor products and new market entrants, such as universities and employers delivering smaller volumes of degree apprenticeships. Further market changes are on the horizon with the introduction of T Levels (a new alternative to A Levels offering a mix of classroom/industry placement).

To continue to grow in an ever-competitive market, SAL needs to react quickly to future market opportunities and offer a unique service from its competitors. Apprentice completion rates are a key driver for the company, with drop-out rates just over 25%. SAL has identified an opportunity to improve completion rates by developing an innovative software platform that provides additional learner support and increases engagement, but the company does not have the required expertise in machine learning and predictive analysis.

The Solution

The expertise of academics as Aston University will address the technical challenges SAL has been unable to overcome itself by providing in-depth analysis of its learner data to identify patterns that will lead to better understanding, modelling and prediction of learning behaviours to create a highly innovative first-to-market product.

The University's knowledge in machine learning and predictive analysis will be used to develop an innovative software platform that will ensure that apprentices are engaged throughout their learner journey. The unique application's key features will be based on in-depth data analysis of learner records, enabling the prediction of learner behaviour and identifying areas for closer learner support (for example, sending automated reminders to learners or suggesting extra reading material).

In the longer term, the partnership will enable SAL to grow as a business through increased revenue and embedding tools and skills to develop new products that can address future educational needs.

Team Aston

The academic team from Aston University will be supervised by Dr Felipe Campelo, whose research expertise focuses on the development of integrated solution frameworks for prescriptive data analytics, seamlessly connecting data mining, statistical modelling, optimisation and multi-criteria decision-making, which will be
invaluable in supporting the data analysis and predictive modelling required in this KTP. He will also focus on the development of algorithms tackling the challenging aspects of applied optimisation, which will ensure
a robust solution to the KTP goals.

Dr Campelo will be joined by Dr Elizabeth Wanner, academic lead for the KTP. Dr Wanner’s carries out her research as part of the Aston Lab for Intelligent Collectives Engineering (ALICE). Her main interests are in multi-objective optimisation, evolutionary algorithms, experimental assessment of algorithms, dynamical systems and engineering design optimisation. She will apply her expertise in this KTP to enable the assessment of varying machine learning algorithms to support the Smart Coach product development.

Next steps

The KTP is expected to complete towards the end of 2022 – look out for more updates by following us on Twitter and checking our webpage

For more information about Knowledge Transfer Partnerships or an informal chat, email or call 0121 204 4242.