Using Learning Analytics to Investigate Patterns of Performance and Engagement in Large Classes

Abstract

Educators continue to face significant challenges in providing high quality, post-secondary instruction in large classes including: motivating and engaging diverse populations (e.g., academic ability and backgrounds, generational expectations); and providing helpful feedback and guidance. Researchers investigate solutions to these kinds of challenges from alternative perspectives, including learning analytics (LA). Here, LA techniques are applied to explore the data collected for a large, flipped introductory programming class to (1) identify groups of students with similar patterns of performance and engagement; and (2) provide them with more meaningful appraisals that are tailored to help them effectively master the learning objectives. Two studies are reported, which apply clustering to analyze the class population, followed by an analysis of a subpopulation with extreme behaviours.