Abstract:
Student absenteeism is a growing
issue, especially in rural and under-resourced areas
like the Manmunai West Educational Zone in Bat
ticaloa, Sri Lanka. Poor attendance has a strong
impact on students’ academic performance and long
term success. Factors such as family financial issues,
school conditions, and personal health or motivation
problems contribute to this challenge. The research
identifies the most important factors influencing ab
senteeism. As well as this study aims to develop an
AI-based system to predict absenteeism risk among
secondary school students and provide personalized
recommendations to help reduce that risk. A struc
tured 29-question Likert-scale questionnaire was ad
ministered to 116 students, covering family, school,
and student-related issues. Data were analyzed using
SPSS and Python. Students were labeled into High,
Medium, or Low absenteeism risk levels based on
their average scores. A Random Forest classifier was
trained to predict the risk level. LIME (Local Inter
pretable Model-agnostic Explanations) was used to
explain the model’s decisions. The model’s accuracy
was validated using Support Vector Machine (SVM)
and 5-fold cross-validation. A Streamlit-based web
application was also developed for practical use. The
Random Forest model achieved 96% accuracy in
classifying absenteeism risk. Important influencing
factors included low family income, parental support,
school safety, and student health issues. The find
ings from SPSS mean analysis were consistent with
Python model outputs. LIME effectively explained
the predictions, helping educators understand the
root causes of absenteeism. The proposed AI-based
model is accurate, interpretable, and practical for
identifying students at risk of absenteeism. The web
based tool allows schools and educational offices to
take early action with personalized interventions.
This system has the potential to improve attendance
and learning outcomes in underprivileged regions
like Batticaloa’s Manmunai West Zone. Future re
search can improve accuracy further by expanding
the dataset and including real-time attendance data.
