Behavioral Dynamics of Student Alcohol Use: A Markov Chain Analysis of Academic-Period Transitions

Alcohol consumption among secondary school students carries significant risks to academic performance and long-term health. While prior studies have already established static correlations between drinking behaviors and grades, the temporal dynamics of behavioral transitions remain unexplored. This study employs a Markov chain model to analyze students drinking patterns across different academic periods (G1G3), using a dataset of from Kaggle and the UCI Machine Learning Repository. This data contains students Portuguese and math grades, as well as their weekdays and weekends alcohol consumption. This study also quantifies transitions between drinking states (low/moderate/high), examining gender and weekday-weekend disparities, and assessing academic impacts through ANOVA and linear regression. The key findings reveal that, Male students exhibit persistent high-risk drinking states on weekends (male=3 vs. females=2), with a 70% retention probability in Markov transitions. Each 1-level increase in weekend drinking correlates with a 0.35-point grade decline (p-value < 0.001). Weekday drinking is uniformly low (level 1) across all students, suggesting academic routines suppress consumption.