A Case Study of Career Counseling for ICT

Abstract

In the education system, the students may find counselors, but student-to-counselor ratio is higher, which forces us to implement an automated system for the guidance of the students. Career counseling can be useful for students to evaluate their careers and select the best direction for the future. This chapter aims to explore, develop, and implement the effective means of analyzing student career counseling, guidelines, and decision making. The authors have developed a realistic dataset from a different mindset of students. The research started once the student provides the machine input about the individual choices about taking admission for matriculation, intermediate, and or short course. The machine learning algorithms like logistic model tree, naïve Bayes, J48, and random forest are used to predict career options. In evaluated results, they found the best algorithm based on the accuracy of kappa statistics, mean absolute error, and correctly classified or incorrectly classified for career-related problems.