A Model for Understanding Student Status Using Attendance Data in the First Semester of University
DOI:
https://doi.org/10.52731/lir.v005.327Keywords:
Hidden Markov Model, Attendance Patterns, Student State Analysis, Behavior Analysis in University StudentsAbstract
This study developed a Hidden Markov Model (HMM) to analyze attendance behaviors of first-year university students during their spring semester, aiming to identify distinct behavioral patterns and examine their impacts. Weekly attendance data was used to estimate latent states, and clustering revealed four representative attendance patterns, including stable attendance and increased absenteeism. The results highlight the potential impact of specific behaviors on academic outcomes, underscoring the importance of preventive interventions in student support and its applicability to future academic guidance.
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