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

Abstract

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 this 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.

Keywords: GIMME; Macro-level stressors; Multilevel models; Regression analyses; Verbal recall.

Figure 1.

Scatterplots depicting results of a regression analyses used to analyze 75-day intensive longitudinal data (aggregated across individual students and days). Dots represent individuals’ paired scores between delayed verbal recall and a psychological predictor (i.e., somatic symptoms, anxiety, or intellectual interests), and lines represent the group-level trends between delayed verbal recall and the predictors. Black dots and lines reflect the 2016 election group, and gray dots and lines reflect the 2017 control group. R² reflects the proportion of variance in delayed verbal recall explained by each predictor for each group.

Figure 2.

Line graphs depicting results of a multilevel model used to analyze 75-day intensive longitudinal data. Thick lines represent the fixed effects between delayed verbal recall and a psychological predictor variable (i.e., somatic symptoms, anxiety, or intellectual interests), and thin lines represent individual deviations from those group-level estimates. Black lines reflect the 2016 election group, and gray lines reflect the 2017 control group. R² reflects the proportion of variance in delayed verbal recall explained by each predictor for each group via the variation in fixed and random slopes as well as the variation of the random intercept (calculated using r2.MLM in R; https://cran.r-project.org/web/packages/r2mlm/index.html).

Figure 3.

Networks depicting results of a person-specific model (i.e., GIMME) used to analyze 75-day intensive longitudinal data. Circles reflect nodes/variables, solid lines reflect same-day relations, dashed lines reflect lagged next-day relations, black lines reflect group-level relations in summary networks, gray lines reflect individual-level relations in summary networks (thickness is proportion of students with a given relation), blue lines reflect negative relations in person-specific networks, and red lines reflect positive relations in person-specific networks; for blue and red lines, thickness reflects relation magnitude. All networks fit the data well; see Supplemental Materials Table S3 for fit indices. A.) Summary network for students during a period of heightened sociopolitical stress (i.e., the 2016 election group) with two group-level autoregressive effects for somatic symptoms and anxiety. B.) Summary network for students in 2017 (i.e., the control group) with one group-level autoregressive effect for somatic symptoms. C.) Group differences in network complexity (i.e., the number of connections per network). D.) Example student-specific network from the election group with a negative, contemporaneous relation between verbal recall and anxiety (among others). E.) Example student-specific network from the control group with a positive, lagged relation between verbal recall and anxiety (among others). F.) Example student-specific network from the election group demonstrating denser relations with four edges and two autoregressive effects. G.) Example student-specific network from the control group demonstrating more sparse relations with two edges and two autoregressive effects. The R² reflects the strength of the relation between delayed verbal recall and anxiety for an individual student (calculated using a formula developed by Peterson & Brown, 2005). VR = delayed verbal recall; SS = somatic symptoms; ANX = anxiety; INT = intellectual interest.