Individualized learning potential in stressful times: How to leverage intensive longitudinal data to inform online learning

Societal events – such as natural disasters, political shifts, or economic downturns – are time-varying and impact the learning potential of students in unique ways. These impacts are likely accentuated during the COVID-19 pandemic, which precipitated an abrupt and wholesale transition to online education. Unfortunately, the individual-level consequences of these events are difficult to determine because the extant literature focuses on single-occasion surveys that produce only group-level inferences. To better understand individual-level variability in stress and learning, intensive longitudinal data can be leveraged. The goal of the paper is to illustrate this by discussing three different techniques for the analysis of intensive longitudinal data: (1) regression analyses; (2) multilevel models; and (3) person-specific network models, (e.g., group iterative multiple model estimation; GIMME). For each technique, a brief background in the context of education research is provided, an illustrative analysis is presented using data from college students who completed a 75-day intensive longitudinal study of cognition, somatic symptoms, anxiety, and intellectual interests during the 2016 U.S. Presidential election – a period of heightened sociopolitical stress – and strengths and limitations are considered. The paper ends with recommendations for future research, especially for intensive longitudinal studies of online education during COVID-19. •Stressful societal events impact the learning and education of individuals.•Intensive longitudinal methods can reveal individual-level effects.•Regression analyses facilitate generalization but obscure individual-level effects.•Multilevel models offer within-person insights limited by between-person effects.•Person-specific networks reveal processes unique to individuals.