Researching the application of Artificial Intelligence and Machine Learning Techniques to deploy learning analytics and performance prediction in the fields of Sporting performance and Corporate training. Now focussing upon the importance of assessing key character attributes in the analysis of potential elite transfer targets  at football clubs. 

CEO of Habilitas Analytics. We work with elite sports organisations to review their use of intelligent analytics in all aspects of their business and are independent of all products and bespoke systems providers. (Habilitas Analytics).

Prior doctoral reseach focused upon the prediction of student performance in small student cohorts with very limited student attributes, with accuracies comparing favourably with published results using large cohorts and significantly more attributes. These analyses are useful to support academics in identifying opportunities to make timely interventions.  Prior to this I had a 39 year career spanning operations, programme and strategic management, software and systems development through to board level responsibility. 

Footballer Analytics:

I believe that there is a significant opportunity to dramatically improve the analysis of potential player acquisitions through the deployment of Machine Learning techniques applied to footballer attributes.  In particular, the player attributes/statistics currently in use by management, coaches and scouts can be significantly extended to include novel attributes, specifically character traits.

Training/Learning Systems:

I believe that there is considerable potential to make significant steps forward in the application of Artificial Intelligence (AI) to training systems. A variety of AI techniques can be applied in real-time to analyse learner behaviour, tailor learning components to learner abilities and knowledge, and to exploit the very large quantities of subject and student data available in both the education and commercial sectors. The development of learning systems in conjunction with trainers, teachers and subject matter experts will provide benefits to institutions across the board, from career/vocational development, re-validation and re-training through to higher education and school.

I believe that the opportunity to make step change progress is now much stronger with the convergence of several of the required components for success increasingly in place.  These are:

• The availability of appropriate learning platforms, with almost all learners having computing devices both inside and outside of the learning setting.
• The increasing quantity and quality of the data (subject and analytics) available to the analytical learning systems using AI.
• The technology (hardware and supporting software) is now powerful enough to handle and exploit the quantity and complexity of data and algorithms necessary for success.
• Institutions are putting more emphasis into this area – exploiting e-learning opportunities and looking for efficiency gains.
• Learners are increasingly interested in learning and developing their knowledge on-line at least in parallel with the traditional classroom/campus model.

As a result, I believe that the deployment of AI and ML techniques in Technology Enhanced Learning (TEL) is poised for accelerated growth and adoption.

In particular,  AI and ML techniques can be nowbe applied to the development of adaptive learning systems, including the classification and representation of subject matter knowledge. By the latter I am referring to the organisation of the subject knowledge, the rules and the processes which connect them into a logical structure that:

• Is comprehensive and efficient for the learning system, as well as for the creation, validation and future manipulation by the subject matter expert (SME).
• Is capable of incorporating all the relevant interconnections between the information in a similar way to the way our own brains do so.
• Allows the learning system itself to automatically self-organise and search for further connections and rules.