An Investigation of Dropout Assessment Methods for College Students in Madhya Pradesh | Original Article

ABSTRACT:

Student dropout prediction is an indispensable for numerous intelligent systems to measure the education system and success rate of any colleges well. 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 paper 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 discriminate analysis and to extract interesting correlations, frequent patterns, associations. In this study, the descriptive statistics analysis was carried out to measure the quality of data using SPSS 24.0 statistical software. The main reason recorded for dropout of students at this college were dropout factor (illness homesickness, poor economic condition), 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).