Public Funding for Securus Software Limited

The aim of the Predictive Analytics for Learning-Safeguarding (PALS) Project is to demonstrate a proactive E-Safety system instead of the purely responsive functionality used in current systems. This will be achieved by creating a new E-Safety analytics service to collect, aggregate and analyse the relevant data from deployed E-Safety systems. The corresponding trend analysis will be used to continually improve the deployed E-Safety system operation capabilities. While the initial use of these new algorithms will be in systems supporting primary and secondary schools, broader market sectors will also be targeted. This will enable the establishment of a first-to-market solution. The innovation is based upon the creation of the new data algorithms for: a) Trend-based data analytics that can be used to improve significantly the identification of cyber-bullying, grooming and other e-safety abuses. These new analytics will also reduce, by 90%, the number of ‘false-positive’ declarations; b) Exploitation in a wider range of market sectors. Versions of the algorithms will be ‘tuned’ for new systems; in Higher and Further Education, Law Enforcement, Health-care and Corporate Human Resource Management markets. A recent internal “state-of-art” product evaluation has shown there is no commercial solution providing the predictive-based learning safeguarding capabilities proposed by this Project. The benefits that accrue to users from this new approach are: a) More accurate identification of cyber-bullying, grooming, etc. so that administrators, mentors, law enforcement, etc. can make timely and better-informed interventions; b) Access to better Reports that allow administrators, mentors, law enforcement, etc. to put events into a broader context and provide information on what future events may be expected. The key objective is to produce and evaluate a ‘demonstrator’ to confirm the approach and ensure that the required predictive functionality and performance is achieved.