The Potential of Learning Analytics for Intervention in ODL

This chapter addresses the potential of learning analytics for intervention in ODL. It first reviews intervention in ODL as a basis of the discussion, covering the relevant theoretical foundation, as well as intervention practice in terms of the problems to be dealt with, existing approaches, and the emerging use of learning analytics over the past decade. It then discusses the potential of learning analytics within ODL and the challenges which should be tackled. The review findings suggest the development of an emerging approach of intervention in the past decade which is driven and supported by learning analytics. The findings highlight the ongoing trends of intervention practice in ODL, which cover the changing modes of ODL with technological advances, the cost-effectiveness of intervention, and personalisation in intervention. Despite such developments, it should be noted that intervention remains one of the most challenging areas in learning analytics. Future areas of focus to address the challenges include, inter alia, advances in human-algorithmic interaction, the relationship with and application to learning analytics of historical ODL models and theories and developing appropriate measures to evaluate the effectiveness of learning analytics interventions to maximise the benefits in ODL contexts.