Predictive analytics student dropout rate and academic success rate

Thong, Chee Fei (2025) Predictive analytics student dropout rate and academic success rate. Final Year Project, UTAR.

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Abstract

The student dropout problem is still a critical problem that occurred at many universities, that influencing both institutional reputation and student academic success. The project objective is to design and develop an interactive dashboard that can predict and visualize the student academic success and dropout patterns using Power BI integrated with a trained model which is LightGBM. The dashboard would be applicable to both educators and administrators with a real-time, user-friendly, and personalized insights that support data-driven intervention strategies for at-risk students. Before the development of dashboard, the CRISP-DM process would be started like data collection, preprocessing, modelling, and visualization of model. The dataset is obtained from a Portuguese university was used, collaborating with demographic, financial, and academic data. Data preparation have including handling missing values, perform sampling methods for data. LightGBM has been selected for deployed model in Power BI because it is good for handling multi-class and during handling high-dimensional datasets always can result in good accuracy rate and intpretabiity. The dashboard have contained with seven modules which are General Overview, Demographic Analysis, Financial Analysis, Model Performance Analysis, StudentRISK Navigator, Navigation Guide and Dataset Descriptions. These modules had provided professional insights and predictive accuracy that can reduce the gap between predictive models and user decision making in education domain. It can allowed institutions to take personalized interventions to help the at-risk students that can improve their retention rate and academic success rate.

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