Exploring the value of Learning Analytics to enhance student learning and to enable more effective module management in large-class teaching environments

The increasing use of digital Learning Management Systems provides educators, students and management in higher education with very significant amounts of data. However the effective use of this data, in the form of Learning Analytics, is still very limited in many institutions. In particular, the potential to use LA to support module delivery in large-class environments has not been significantly researched. This study examines the LA available on the Canvas LMS for a class of over 300 students, delivered fully online, to test the effectiveness, accuracy and usability of this data. There is a very strong correlation identified between the student engagement reflected in the Canvas analytics data and individual student performance, in the form of their final grade. However the analytics are more limited in terms of supporting self-regulated learning and identifying more nuanced student behaviour online. This study provides recommendations on how the LA data provided might be improved to enhance effectiveness for large-class teaching environments. This research also reveals other interesting patterns of learner behaviour that could assist educators with large classes, such as attention spans online and consistency of engagement throughout a module. The findings support the provision of lecture recordings online, as a revision tool, to enhance learning outcomes and engagement.