The project’s aim is to carry out a study into human learning trends, in order to drive the development of novel eLearning software systems that provide a learning environment and corporate training within organisations. The goal is to develop a completely new approach to designing eLearning systems using machine learning based computational methods, to learn a mapping between the learning material (content, form, delivery) and its effectiveness in the learning process of the user. Utilising Data captured from Fuse Universal clients and investigating individual and demographic variables (e.g. personality, age, role and tenure), the study will look to determine significant attributes and profiles of individuals using the learning system. This understanding can then be utilised in order to provide data to create a recommendation engine to personalise the learning experience for the user.
Dave Westwood, Head of Learning Sciences at Fuse Universal stated “Organisations are aware that in order to maintain a competitive advantage within their market they need an organisational workforce which is able to adapt and innovate swiftly. Organisations are therefore turning to learning and development in order to develop staff equipped with the necessary skills. New social platforms such as Fuse are being designed to engage and deliver at scale this workforce learning. These platforms are creating millions of data points per month tracking what individuals are utilising and engaging with, within their learning. With further understanding and exploration, machine learning could afford us the opportunity to personalise further the learning experience in order to more swiftly engage and educate individual learners.”
Dr. Ivana Drobnjak, Associate Professor at UCL added “Learning is not a one-size-fits-all type of process. Different people need different learning environments, contents and deliveries in order for them to absorb and apply the learned material successfully. In this project, we are trying to develop a computational tool that learns from its user about his/her’s learning preferences and adapts itself seamlessly to deliver the best, personal learning environment. The computational technology will be based on techniques such as data mining, analytics and machine learning, which is only made possible because of a continuous stream of very rich data from the Fuse platform that will allow us to understand our learners better.”
The project involves analysing large data sets of online learning data from companies such as Vodafone, Carpetright, Spotify and IHG, to learn trends using data analysis techniques such as machine learning.
- The project is funded for 4 years with a tax-free stipend and fees paid at the Home/EU rate
- Due to restrictions placed on the funding this scholarship is only open to applicants from the UK or EU
- Successful applicants will work in both the London Offices of Fuse Universal (Shoreditch) and University College London (Euston)
- The project is supervised by Dr. Ivana Drobnjak
Applications for the programme are available via the UCL programme registration page and applicants will be required to provide their CV and a cover letter
About University College London
UCL is London's leading multidisciplinary university, with more than 11,000 staff and 38,000 students from 150 different countries. Founded in 1826 in the heart of London, UCL was the first university in England to welcome students of any religion and the first to welcome women on equal terms with men.