Academic Performance Prediction Model for Teacher's Training Colleges Using Machine learning Approach

Abstract

Data mining is the process of extracting novel or previously unknown information from a large amount of data. The purpose of this study is to develop an academic performance prediction model and identifying the factors that affect academic performance of college student using data mining techniques. The data used for this study are 1023 active students from HCTE in 2018/19 academic year. For the consumption of this research, both primary and secondary data was used. Primary data such as age, gender, previous high school, department, library usage, study hours, sport interest, mother education, father education, time spent in social media, family support and economic status of family is collected by means of questionnaire. Secondary data was obtained from the HCTE registrar office. The prediction model was developed using multilayer perceptron (MLP) classification algorithm, Naive Bayes and J48 and correlation based feature selection (CFS) is applied to identify the predictive attributes of academic performance. Finally, Multilayer perceptron, Naive Bayes and J48 is compared using the same dataset. According to the result of the experiments, Multilayer perceptron using all attributes with test method of 10-fold cross validation and accuracy 60.6% gives better result compared to Naive Bayes, J48 and MLP after applying attribute selection. The study findings also showed that sex of the student, total courses credit hours taken by the students, study hours, assignment performance and library usage of the students are identified as a significant factor affecting academic performance. WEKA 3.8.1 tool was used for data mining process.