Forecasting Intra-individual Changes of Affective States Taking into Account Inter-individual Differences Using Intensive Longitudinal Data from a University Student Dropout Study in Math

The longitudinal process that leads to university student dropout 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 et al. in Struct Equ Model 24:257-269, 2017; DSEM; Asparouhov et al. in Struct Equ Model 25:359-388, 2018; NDLC-SEM, Kelava and Brandt in Struct Equ Model 26:509-528, 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 dropout 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 using a Forward Filtering Backward Sampling method. To illustrate our approach, we use an empirical example where we apply the proposed forecasting method to ILD from a large university student dropout study in math with multivariate observations collected over 50 measurement occasions from multiple students ([Formula: see text]). More specifically, we forecast emotions and behavior related to dropout. This allows us to predict emerging critical dynamic states (e.g., critical stress levels or pre-decisional states) 8 weeks before the actual dropout occurs.

Digital Proficiency and Psychological Well-Being in Online Learning: Experiences of First-Generation College Students and Their Peers

The study examines the role of digital proficiency in relation to psychological well-being of a vulnerable population—first-generation college students (FGS)—and their counterparts, continuing-education students, in online learning during the coronavirus pandemic (COVID-19). Informed by positive computing and FGS studies, this paper empirically tests a model of three key influencing factors by using a data sample (n = 309) collected from a minority-serving university in the U.S. in April 2020. Our data analysis confirms that FGS underperformed in digital proficiency, compared to their peers. Moreover, digital proficiency had a significant effect on individual psychological well-being, regardless of a student’s generational status. Our paper contributes to the positive computing research by empirically demonstrating the association between users’ technology competence and psychological well-being. Practically, our findings highlight the importance of implementing educational programs and policies to promote the digital skills and well-being of underserved students such as FGS in college education.