There has been significant progress in the development of techniques to deliver more effective e-Learning systems in both education and commerce but there are very few examples of comprehensive learning systems that exploit contemporary artificial intelligence (AI) techniques. We have surveyed existing intelligent learning/training systems and explored the contemporary AI techniques which appear to offer the most promising contributions to e-Learning. With the convergence of several of the required components for success increasingly in place the opportunity to make progress now appears to be much stronger.

In the field of education, the mining, extraction and exploitation of useful information and patterns from student data provides lecturers, trainers and organisations with the potential to tailor learning paths and materials to maximize teaching efficiency and to predict and influence student success rates. Progress in the area of student data analytics can provide useful techniques for exploitation in the development of adaptive learning systems. Student data often includes a combination of nominal and numeric data. A large variety of techniques are available to analyse numeric data, however there are fewer techniques applicable to nominal data. We have explored potential correlations between student attributes in a freely available data set, including the application of what we believe to be a novel technique to analyse nominal data, providing the opportunity to focus on promising correlations for deeper analysis.

Date: 8/04/2016
Time: 16:00
Location: LB252