Beyond the average: person-specific analytics for precision education

Abstract

[EN]Educational research has traditionally relied on group-level analyses to identify patterns across learners. While valuable for detecting general trends, such approaches often provide limited insight into how learning processes unfold within individuals. This introduction to person-specific analytics presents recent methodological and conceptual developments that address this gap, including intensive longitudinal designs, idiographic modeling, and person-specific machine learning. The contributing articles examine how individual trajectories in motivation, self-regulation, collaboration, and cognitive development can be studied more accurately when variability is treated as a central feature rather than as noise. Together, the contributions highlight how person-specific methods can complement traditional approaches by enabling more precise, adaptive, and context-sensitive educational practices.