Assisting Education through Real-Time Learner Analytics

Learning analytics has been dominating the education arena for these last few years as the efficacy and applicability of big data in general has been recognized, appreciated and fruitfully employed. Such analytics can assist and support higher education learners to optimize their interaction with available resources, while providing crucial insights on their learning behaviour and study processes. In this paper, we present a case study of how we employ an online environment with mature students within an ambient intelligent classroom. The specifically designed learning space makes use of a number of strategically positioned sensors that provide explicit data to the underlying intelligent system that forms part of the associated online portal. Physically present and remotely connected learners have access to their personalised learning environment that depends on their academic needs, progress, position in class, and the other learners in their group or vicinity. The ambient intelligent system coordinates the interaction between all the students and teacher who is present in class. A real-time machine learning application to detect learners' levels of attention and participation makes use of sensory data generated together with other learning data collected from the portal that collectively assists in the learning analysis process that is looped back to the learners' learning environment, as well as to the educator, in an attempt to improve the delivery and the entire educational process. We report on several issues encountered together with accuracy and reliability concerns as we draw a number of conclusions and offer recommendations.