Open AccessIMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE
Author(s) -
Nibras Z. Salih,
Walaa Khalaf
Publication year - 2022
Publication title -
journal of engineering and sustainable development
Language(s) - English
Resource type - Journals
eISSN - 2520-0925
pISSN - 2520-0917
DOI - 10.31272/jeasd.25.6.6
Subject(s) - oversampling , overfitting , machine learning , artificial intelligence , computer science , multilayer perceptron , classifier (uml) , artificial neural network , bandwidth (computing) , computer network
Classification under supervision is the most common job that performed by machine learning. However, most Educators were worried about the rising evidence of student academic failures in university education. So, this study presents a supervised classification strategy of machine learning algorithm using an actual dataset contains 44 students, fourteen attributes for three previous academic years. We have proposed features that show the relationship among three main subjects which are, calculus, mathematical analysis, and control system in the education course. The objective of this study is to identify the student’s failure in the control system subject and to enhance his performance by Multilayer Perceptron (MLP) algorithm. The dataset is unbalanced, which causes overfitting of the results. Synthetic Minority Oversampling Technique has applied to a dataset for obtaining balance dataset using Weka tool. Several standard metrics used to evaluate the classifier results. Therefore, the suitable results occurred after applying SMOTE with an accuracy of 76.9%.