Forecasting intra-individual changes of affective states taking into account inter-individual differences using intensive longitudinal data from a university student drop out study in math

TL;DR

This paper develops a Bayesian forecasting approach for intra-individual affective states using intensive longitudinal data to predict university student drop out, enabling real-time interventions and personalized support.

Contribution

It introduces a novel Bayesian forecasting method within dynamic latent variable frameworks for ILD, applied to predicting student drop out in math.

Findings

Successful forecasting of emotions and behaviors related to drop out

Detection of heterogeneity in individual trajectories

Prediction of critical stress levels and pre-decisional states

Abstract

The longitudinal process that leads to university student drop out in STEM subjects can be described by referring to a) inter-individual differences (e.g., cognitive abilities) as well as b) intra-individual changes (e.g., affective states), c) (unobserved) heterogeneity of trajectories, and d) time-dependent variables. Large dynamic latent variable model frameworks for intensive longitudinal data (ILD) have been proposed which are (partially) capable of simultaneously separating the complex data structures (e.g., DLCA; Asparouhov, Hamaker, & Muth\'en, 2017; DSEM; Asparouhov, Hamaker, & Muth\'en, 2018; NDLC-SEM, Kelava & Brandt, 2019). From a methodological perspective, forecasting in dynamic frameworks allowing for real-time inferences on latent or observed variables based on ongoing data collection has not been an extensive research topic. From a practical perspective, there has been no empirical study on student drop out in math that integrates ILD, dynamic frameworks, and forecasting of critical states of the individuals allowing for real-time interventions. In this paper, we show how Bayesian forecasting of multivariate intra-individual variables and time-dependent class membership of individuals (affective states) can be performed in these dynamic frameworks. To illustrate our approach, we use an empirical example where we apply forecasting methodology to ILD from a large university student drop out study in math with multivariate observations collected over 50 measurement occasions from multiple students (N = 122). More specifically, we forecast emotions and behavior related to drop out. This allows us to model (i) just-in-time interventions, (ii) detection of heterogeneity in trajectories, and (iii) prediction of emerging dynamic states (e.g. critical stress levels or pre-decisional states).