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The present case study describes the results of the educational data mining aimed at predicting the undergraduate courses of computer science (BCA and B.Tech.) students’ instant dropout or after first semester. For this purpose, the preliminary data of 220 students collected randomly in prescheduled format on personal interview to find out dropout rates and dropout reasons. The simple and intuitive classifiers (decision trees) ID3 and J48 were used in this paper. The main reason recorded for dropout of students at this residential university were personal factor viz; illness & homesickness, Educational factors viz; learning problems & difficult courses, change of Institution with present goal and low placement rate and institutional factors such as campus environment, too many rules in hostel life and poor entertainment facilities. The information generated will be useful for better planning and implementation of educational program and infrastructure under measurable condition to increase the enrollment rate of students in ICT courses at this university.

Students' Dropout Risk Assessment in Undergraduate Courses of ICT at Residential University A Case study