From Data to Design: Integrating Learning Analytics into Educational Design for Effective Decision-Making

Learning Analytics (LA) aims to provide university instructors with meaningful data and insights that can be used to improve courses. However, instructors are often met with challenges that arise when wanting to use LA to inform their educational design decisions. For instance, there may be a misalignment between instructors’ needs and the data and insights LA systems provide. Further research is required to understand instructors’ expectations of LA and how it can support the diversity of educational designs. This case study addresses this gap by investigating the role of LA in instructors’ educational decision-making processes. The study employs self-determination theory's constructs to examine instructors’ existing practices when using LA to support their decision-making. The study reveals that LA enables instructors to make data-informed iterative educational design decisions, supporting their need for competence and relatedness. The emotional aspect of LA is an important consideration that can easily lead to demotivation and avoidance of LA. Support is needed to address instructors’ psychological needs so instructors can fully utilise LA to make effective educational design decisions. The findings inform a framework for considering how instructors’ data-informed educational decision-making can be understood. The implications of our findings and opportunities for the future are discussed.