Idiographic learning analytics: A single student (N=1) approach using psychological networks

Abstract

Recent findings in the field of learning analytics have brought to our attention that conclusions drawn from cross-sectional group-level data may not capture the dynamic processes that unfold within each individual learner. In this light, idiographic methods have started to gain grounds in many fields as a possible solution to examine students’ behavior at the individual level by using several data points from each learner to create person-specific insights. In this study, we introduce such novel methods to the learning analytics field by exploring the possible potentials that one can gain from zooming 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. The results of our study revealed that the student under examination may be in need to learn better self-regulation techniques regarding reflection and planning. © 2021 Copyright for this paper by its authors.