Application of the Classification Algorithms on the Prediction of Student’s Academic Performance

Abstract

Measuring student performance based on both qualitative and quantitative factors is essential because many undergraduate students could not be able to complete their degree in the recent past. The first-year result of a student is very important because in the majority of cases this drives the students to be either motivated or demotivated. So, the first-year student performance of a renowned university in Bangladesh is investigated in this paper. This research is mainly based on finding the factors for students’ different types of results and then predicting students’ performance based on those 11 significant factors. For this purpose, 2 popular supervised machine learning algorithms have been used for classifying students’ different levels of results and predicting students’ performances, those are support vector machines (SVM) classifier and random forest classifier (RFC) which are tremendously used in classification and regression analysis. The input dataset for both training and testing were taken by merging the values obtained from 2 surveys done on students and experts using an adaptive neuro-fuzzy interference system (ANFIS). RF has outperformed SVM in predicting students’ performances. According to factor analysis, students’ effort (Factor-11) is the significant factor. This proposed model can also be applied to predict course-wise students’ performances and its precision can also be greatly improved by adding new factors.
 HIGHLIGHTS
 
 Identify the significant factors responsible for students’ different levels of performances
 Apply two machine learning algorithms to classify students’ results based on the factors
 Analyze the results obtained from the methods
 Compare the accuracy, and find the top five factors responsible for students’ academic results