Classification and Regression Decision Tree: A Mining Technique for Students' Insights on the University Services with Text Analysis
Abstract
© 2018 IEEE. According to Gatpandan, P [1], the role of the university as a service provider in education sector considers several aspects from student admission to graduate career, and the student is the primary consumer in Higher Education Institution (HEI) services and has implications for the management of service of quality in higher education organizations.Quality education can be determined thru the quality of services that were given to the students. Satisfaction level of the students can be measured based on their experience all throughout the entire stay in the university.In educational setting, exit interviews are conducted with students who have graduated from an educational institution. The exit interview is intended to gather information about students' experience while attending in the institution, what they benefited from, what was missing, and what could be improved to enhance the experience of the next generation of students. This type of interview can also point to areas in which the institution should invest more or less resources to enhance a student's learning and development experience. [2]A University in Dasmariñas has customized exit interview for their graduating students. This exit interview is in the form of questionnaire and used a five-point Likert scale. The strategic value of this Exit interview can be effectively achieve through applying data mining.Data mining is a process of extracting useful information from huge data [3] and finding patterns. Data mining process can also be applied to educational environment in particular to Higher Education Institutions.With this, the researchers is motivated to come up with a study that would help the education sector especially the management to address improvements in their institution through applying data mining technique on the Students' insight on the University's Academic and Student Services particularly on the areas of: Facilities, Student services, and Teachers.Implementation of cross validation 10-folds logistic regression and decision tree analysis were used in this study.
