Abstract
The accurate
prediction of students' academic achievement has garnered considerable
attention in the research community due to its importance in understanding
students' progress and assisting them in achieving success. This study presents
a novel approach for predicting undergraduate student's
performance in the context of Bangladesh. The dataset contains 872
student records from multiple institutions. Initially the dataset was produced
utilizing data-preprocessing techniques such as one-hot encoding, column
remaining, and managing missing values. SMOTE (Synthetic Minority Oversampling
Technique) and normalizing algorithms were employed to attain data balance
and feature
scaling, respectively. Afterwards, a total of seven distinct machine
learning (ML) classifiers, with hyperparameter tuning, were employed
to train and test in order to achieve the prediction of students' academic
performance. Furthermore, a custom stacking ensemble
classifier was utilized, which attained an accuracy of 86.38 %.
This classifier outperformed the machine learning classifiers based on the four
performance evaluation
metrics. Two eXplainable Artificial Intelligence (XAI) algorithms, namely
SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable
Model-agnostic Explanations), were integrated to provide a comprehensible
prediction of the best model and determine the significant factors. This
approach provided transparency, fairness and reliability on prediction that
improved student performance in the classroom and anticipation.