Luna -- Student Dropout Platform
Introduction
University student dropout, particularly in Science, Technology, Engineering, and Mathematics (STEM) subjects, represents a significant challenge for both modern economies and the affected individuals. In the German context, for example, approximately 40 percent of students drop out in the early phase of math studies, a rate considerably higher than the average across all subjects. Traditional models of student attrition often rely on factors that are static or measured only periodically, failing to capture the dynamic, time-sensitive psychological processes that immediately precede a student’s decision to quit.
The Project Solution: A Dynamic, Real-Time Forecasting Approach
Our project introduces a new methodological approach designed to forecast critical states related to university student dropout, allowing for real-time inferences and the possibility of intervention based on ongoing data collection. This approach utilizes Intensive Longitudinal Data (ILD) and dynamic latent variable model frameworks, such as the Nonlinear Dynamic Latent Class Structural Equation Model (NDLC-SEM).
Literature:
Kelava, A., Kilian, P., Glaesser, J., Merk, S., & Brandt, H. (2022). Forecasting intraindividual changes of affective states taking into account interindividual differences using intensive longitudinal data from a university student drop out study in math. Psychometrika, 87(2), 533-558. Link
Getting started:
https://methodscenter.mintlify.app/getting-started