A Study on Predictive Modelling of Student Academic Performance using Machine Learning Method

Abstract

Predicting students’ academic performance is a crucial initiative in the field of education, as it allows educators and administrators to spot students who may require extra assistance, customize educational resources to meet individual requirements, and improve overall educational results. Conventional approaches to forecasting academic achievement, such as statistical analysis and expert evaluation, have certain drawbacks in terms of precision and scalability. The emergence of machine learning (ML) methods provides a possible alternative by utilizing extensive datasets and advanced algorithms to reveal patterns and generate more precise predictions. This investigation’s primary goal is to explore the predictive modelling of student academic performance by improving the accuracy of predictions through the utilization of machine learning techniques. The study was conducted utilizing the Python programming environment. The prediction of student academic performance was carried out utilising the Bidirectional long short-term memory (Bi-LSTM) based Weighted Cost Effective Random Forest algorithm. The study utilized the Deep Encoder CNN-Bi-LSTM for optimal feature extraction to foresee student academic performance. The extracted features were then classified using the Weighted Cost Effective Random Forest (WECRF) classifier, and the classification was evaluated in terms of accuracy, precision, specificity, sensitivity, and recall. The issues addressed include the class imbalance, computational complexity, cost, and huge dimensional issue, among others. The random forest method achieved Precision Score - 0.72, Recall Score - 0.68, F1 Score - 0.69, and Accuracy - 0.77 in this study. Moreover, the suggested technique facilitates the automated forecasting and enhancement of students' future academic performance. Keywords: Predictive Modelling; Students; Academic Performance; Machine Learning; Bi-LSTM; CNN-Bi-LSTM; Deep Learning.