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A multi-dimensional student performance prediction model (MSPP): An advanced framework for accurate academic

V Balachandar¹, K Venkatesh¹

¹Department of Networking & Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.

|January 27, 2025

Summary

This study introduces a new Multi-dimensional Student Performance Prediction Model (MSPP) to accurately forecast student outcomes. The MSPP model enhances educational data analysis for better learning interventions and reliable classification.

Keywords:

Artificial neural network Classification Deep learning Explainable AI Feature engineering Multi-dimensional Student Performance Prediction Model (MSPP)Personalized learning

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Area of Science:

Educational Data Mining
Artificial Intelligence in Education
Machine Learning

Background:

Traditional student performance prediction models struggle with multi-dimensional, imbalanced, and temporal educational data, leading to suboptimal classification.
Existing methods often provide generalized insights rather than tailored predictions, limiting the effectiveness of interventions.

Purpose of the Study:

To propose a novel Multi-dimensional Student Performance Prediction Model (MSPP) for precise forecasting of student academic performance.
To address limitations of existing models in handling complex educational datasets and improve the accuracy of student classification.

Main Methods:

Developed the MSPP model integrating advanced data preprocessing, feature engineering, and deep learning techniques, including graph neural network layers.
Employed adaptive hyper-parameter tuning and explainable AI (XAI) features to handle imbalanced and temporal data.
Utilized domain-specific preprocessing to structure sparse, heterogeneous academic data for multi-class classification.

Main Results:

The MSPP model achieved high accuracy (76%), precision (0.79), and macro F1-score (0.73), outperforming existing models.
Significantly reduced the False Positive Rate (FPR) to 0.15, enhancing prediction reliability.
Demonstrated improved precision-recall across multiple student performance categories (distinction, pass, fail, withdrawn).

Conclusions:

The MSPP model offers a robust framework for accurate student performance prediction by incorporating contextual information and multi-layered analysis.
The model's ability to handle complex data and provide reliable classifications supports the development of effective, personalized educational interventions.
This approach provides a foundation for generalizing insights from sparse, heterogeneous academic data, improving educational outcomes.