COMPARATIVE AND PREDICTIVE ANALYTICS FOR STUDENTS’ ACADEMIC PERFORMANCE USING SUPERVISED CLASSIFIERS

Authors

  • A.S. BASHIRU epartment of Computer Science, Abdu Gusau Polytechnic Talata Mafara, Zamfara State
  • I.G. BAOKU Department of Mathematical Sciences, Faculty of Physical Sciences, Federal University Dutsin-Ma, Katsina State
  • A.J. BASHIR Department of Cyber Security, Faculty of Computer Science and Artificial Intelligence, Federal University Dutsin-Ma, Katsina State
  • U. ILYASU Department of Computer Science, Faculty of Computer Science and Artificial Intelligence, Federal University Dutsin-Ma, Katsina State

Keywords:

Feature Selection, Logistic Regression, Random Forest, Support Vector Machine, Students' Performance Prediction,

Abstract

The greatest aim of every educational setup is giving the best educational experience and knowledge to the students.
Discovering the students who need extra support and guidance so as to carry out the necessary actions to enhance
their performance plays an important role in achieving that aim. In this research work, five machine learning
algorithms have been used to build a classifier that can predict the performance of students in higher institutions
considering two institutions, which are Abdu Gusau Polytechnic Talata Mafara and College of Education Maru,
Zamfara State are considered. The machine learning algorithms include Random Forest, Support Vector Machine,
Logistic Regression, K-Nearest Neighbour and Naive Bayes. The models have been compared using the Precision,
Recall, F1-Score and Support classification accuracy. The dataset used to build the models is collected from the MIS
centre of each of the institutions. The Random Forest model is found to achieve the best performance.

Downloads

Published

2022-12-22

Issue

Vol. 21 No. 2 (2022)

Section

Articles