Data Mining in Enrollment Management

Topics: Data mining, Decision tree learning, Tree Pages: 6 (2994 words) Published: October 15, 2014
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
of
teaching,
continuous
evaluation,
categorization of student into similar type, so that students have similar objectives, demographic, educational background etc.
Advent of computer opens a new era in the field of
information because of high storage capability and complex
study. Huge number of data can be organized in any order
with the help of computer. Now we can explore a new
knowledge on these data which was either impossible or a
very time consuming process for a person [1], [2], [3].
Education sector has a lot of data in the form of student‟s information. Application of computer in the education can
extract valuable information to provide quality education. Due to this combination of education and computer (data mining) a new research community is growing i.e. educational data
mining [13].
Data mining, which is the science of filtering data for
information and knowledge retrieval, has recently developed
new album of applications and engendered an emerging

discipline, called Educational Data Mining (EDM). EDM
carries out tasks such as prediction (classification and
regression), clustering, relationship mining (association,
correlation, sequential mining, and causal data mining),
distillation of data for human judgment, and discovery with
models [4]. Moreover, EDM can solve many problems based
on educational domain. Data mining is non-trivial extraction of implicit, previously unknown and potentially useful
information from large amounts of data. It is used to predict the future trends from the knowledge pattern. Remarkable
amount of EDM endeavors have been conducted and
published in many journals and conference proceedings
related to, but not limited to, Artificial Intelligence, Learning Systems, Education, and others.
The main objective of this paper is to use data mining
methodologies to select student for enrollment in a particular course (MCA). Data mining provides many tasks that could
be used to select good student for a particular...
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