Data Mining in Enrollment Management
International Journal of Computer Applications (0975 – 8887)
Volume 41– No.5, March 2012
Data Mining Application in Enrollment Management: A
Case Study
Surjeet Kumar Yadav
Saurabh pal
Research scholar, Shri Venkateshwara University,
J. P. Nagar, (U.P.) India
Head, Dept. of MCA
VBS Purvanchal University, Jaunpur, India
ABSTRACT
In the last two decades, number of Higher Education
Institutions (HEI) grows rapidly in India. This causes a cut throat competition among these institutions while attracting the student to get admission in these institutions. Most of the institutions are opened in self finance mode, so all time they feel short hand in expenditure. Therefore, institutions focused on the strength of students not on the quality of education.
Indian education sector has a lot of data that can produce valuable information. Knowledge Discovery and Data Mining
(KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extract the knowledge.
This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Now-aday a new research community, educational data mining
(EDM), is growing which is intersection of data mining and pedagogy. In this paper we present the data mining method for enrollment management for MCA course.
General Terms
Educational Data Mining
Keywords
Data mining, Knowledge Discovery, Higher Education,
Enrollment Management, ID3 Decision Tree.
1. INTRODUCTION
Quality education is one of the most promising responsibilities of any University/ Institutions to his students.
Quality education does not mean high level of knowledge produced. But it means that education is produced to students in efficient manner so that they learn without any problem.
For this purpose quality education includes features like:
methodology
Volume 41– No.5, March 2012
Data Mining Application in Enrollment Management: A
Case Study
Surjeet Kumar Yadav
Saurabh pal
Research scholar, Shri Venkateshwara University,
J. P. Nagar, (U.P.) India
Head, Dept. of MCA
VBS Purvanchal University, Jaunpur, India
ABSTRACT
In the last two decades, number of Higher Education
Institutions (HEI) grows rapidly in India. This causes a cut throat competition among these institutions while attracting the student to get admission in these institutions. Most of the institutions are opened in self finance mode, so all time they feel short hand in expenditure. Therefore, institutions focused on the strength of students not on the quality of education.
Indian education sector has a lot of data that can produce valuable information. Knowledge Discovery and Data Mining
(KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extract the knowledge.
This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Now-aday a new research community, educational data mining
(EDM), is growing which is intersection of data mining and pedagogy. In this paper we present the data mining method for enrollment management for MCA course.
General Terms
Educational Data Mining
Keywords
Data mining, Knowledge Discovery, Higher Education,
Enrollment Management, ID3 Decision Tree.
1. INTRODUCTION
Quality education is one of the most promising responsibilities of any University/ Institutions to his students.
Quality education does not mean high level of knowledge produced. But it means that education is produced to students in efficient manner so that they learn without any problem.
For this purpose quality education includes features like:
methodology