Student Dropout Risk Assessment in Undergraduate Course at Residential University

Student dropout prediction is an indispensable for numerous intelligent systems to measure the education system and success rate of any university as well as throughout the university in the world. Therefore, it becomes essential to develop efficient methods for prediction of the students at risk of dropping out, enabling the adoption of proactive process to minimize the situation. Thus, this research work propose a prototype machine learning tool which can automatically recognize whether the student will continue their study or drop their study using classification technique based on decision tree and extract hidden information from large data about what factors are responsible for dropout student. Further the contribution of factors responsible for dropout risk was studied using discriminant analysis and to extract interesting correlations, frequent patterns, associations or casual structures among significant datasets, Association rule mining was applied. In this study, the descriptive statistics analysis was carried out to measure the quality of data using SPSS 20.0 statistical software and application of decision tree and association rule were carried out by using WEKA data mining tool. Based on the application of association rule, the highest support value 0.68 was recorded for dropout mainly because of the personal problem. On the other hand ID3 decision tree algorithm was found best classifier with 98% accuracy whereas, discriminant function analysis correctly classified 99.1% of original grouped cases and 98.6% of cross-validated grouped cases. The main reason recorded for dropout of students at this residential university were personal factor (illness & homesickness), Educational factors (learning problems & difficult courses, change of Institution with present goal and low placement rate) and institutional factors (campus environment, too many rules in hostel life and poor entertainment facilities).