Idiographic learning analytics: A definition and a case study

Abstract

Idiographic methods have emerged as a way to examine individual behavior by using several data points from each subject to create person-specific insights. In the field of learning analytics, such methods could overcome the limitations of cross-sectional group-level data that may fail to capture the dynamic processes that unfold within each individual learner and less likely to offer relevant personalized learning or support. In this study, we provide a definition of idiographic learning analytics and we explore the possible potentials of this method to zoom in on the fine-grained dynamics of a single student. Specifically, we make use of Gaussian Graphical Models - an emerging trend in network science - to analyze a single student’s dispositions and devise insights specific to him/her. Our findings offer a proof of concept of the potential of this novel method in revealing personalized valuable insights about students’ self-regulation. While our specific findings apply to a single student, our method applies to every student regardless of context. © 2021 IEEE.