Students’ Class Performance Prediction Using Machine Learning Classifiers

Adeel Ahmed,Rafaqat H Arain,Imtiaz A Halepoto,Asif A Wagan,Mansoor A Khuhro,Kamlesh Kumar

doi:10.52584/qrj.1901.16

Abstract

Nowadays, educational data mining is being employed as assessing tool for study and analysis of hidden patterns in academic databases which can be used to predict student’s academic performance. This paper implements various machine learning classification techniques on students’ academic records for results predication. For this purpose, data of MS(CS) students were collected from a public university of Pakistan through their assignments, quizzes, and sessional marks. The WEKA data mining tool has been used for performing all experiments namely, data pre-processing, classification, and visualization. For performance measure, classifier models were trained with 3- and 10-fold cross validation methods to evaluate classifiers' accuracy. The results show that bagging classifier combined with support vector machines outperform other classifiers in terms of accuracy, precision, recall, and F-measure score. The obtained outcomes confirm that our research provides significant contribution in prediction of students’ academic performance which can ultimately be used to assists faculty members to focus low grades students in improving their academic records.

Highlights

Data mining is the process of extracting valuable, explicit, and nontrivial knowledge from large data depositories
Our focus is to use it in education which is termed as Educational Data Mining
The Education Data Mining (EDM) is becoming an emerging discipline, concerned with developing methods for exploring the unique and increasingly large-scale data that come from educational settings

Summary

Introduction

Data mining is the process of extracting valuable, explicit, and nontrivial knowledge from large data depositories. The key area of EDM application is to improve the model of the students by identifying various characteristics and attributes which could play a seminal role in the model [3] It is a hot area among the researchers due to its potential benefits in the education sector while developing a better model that can explore the hidden knowledge in the academic data [4]. For this purpose, many supervised and unsupervised machine learning techniques have been proposed in the past for EDM to develop an effective model for prediction students’ performance [5].

Objectives

Methods

Results

Conclusion