Education of Educational data. Currently there are many

Education Data
Mining

Abstract

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Education Data Mining is the field which
uses Data Mining techniques in Educational Environments. There are various
applications and methods in Educational Data Mining which are following both
applied as well as pure research objectives. Applied research objectives
include improvement and enhancement of learning quality on other hand pure
research objectives tends to enhance understanding of learning process.

Introduction

Now a day’s Data
Mining evolves as an emerging field, which is used in almost all areas. Data Mining
has great influence in learning and education. Finding information manually is
hard and takes lot of time and effort. Education Data Mining uses analytical
techniques to get relationships, patterns and structure within dataset and
improve the design of learning models. Educational Data Mining is learning
science in addition to, rich application area for Data Mining, because of
increasing amount of Educational data.

Currently
there are many online learning systems which gather big amount of data such as
Intelligent Tutoring System (ITS), Learning Management System (LMS) etc. LMS
defined as “a centralized web based information systems where the learning
content is managed and learning activities are organized. LMS represents a more
general term for a technology framework that supports all aspects of formal and
informal learning processes, including learning management, content management,
course management, etc”

 Let’s look definitions of Educational Data
Mining to better understand the concept:

“Educational
Data Mining is an emerging discipline, concerned with developing methods for
exploring the unique types of data that come from educational settings, and
using those methods to better understand students, and the settings which they
learn in. Whether educational data is taken from students’ use of interactive
learning environments, computer-supported collaborative learning, or
administrative data from schools and universities, it often has multiple levels
of meaningful hierarchy, which often need to be determined by properties in the
data itself, rather than in advance. Issues of time, sequence, and context also
play important roles in the study of educational data.”

“Educational
Data Mining is an emerging discipline, concerned with developing methods for
exploring the unique types of data that come from educational settings, and
using those methods to better understand students, and the settings in which
they learn.”

A
brief historical synopsis

EDM bridges
between two disciplines: Education on the one hand, computing sciences on the
other, where both machine learning and data mining as subfields of CS are the
focus. In EDM these two fields intertwined and it is important to keep our
focus on how these two fields contributed for advancement in both
learning/teaching and educational research.

Use of
Computers and Computing Science is not recent; it started in mid-20th
century. At that time three cognitive domains: knowledge, application and
comprehension are targeted. Higher level skills weren’t tin picture at the
time.

Up to the
time, isolated computers were being used. Collecting, analyzing and generating
data was difficult. After World Wide Web launched, in 1995 first learning management
system was developed. E-learning and online teaching became reality.

In 2005, first
time Education Data Mining was used. Several conferences and workshops for
Education Data Mining held in USA, Romero and Ventura. During these workshops,
two things were highlighted. First, it was all about technical aspect of
collecting and analyzing data. Second focus was on Computer Education and
Computing Science training.

Methods and
Application

Educational
Data Mining methods are similar of Data Mining. There are several methods to
use in Educational Data Mining for various applications. Most used methods are:

·       Classification
and Regression

·       Clustering

·       Associational
rule mining

·       Discovery
with models

·       Outlier
Analysis

·       Social
Network Analysis

·       Text
Mining

·       Sequential
pattern mining

·       Visualization
techniques

·       Distillation
of data for human management

According
to research most applied Data Mining tasks are Classification and Regression, Clustering
and Associational rule mining.

There
are 13 categories of Educational Data Mining applications, forming new taxonomy
specifically to EDM, thus setting Educational data Mining as subfield of Data
Mining.

Four
applications are under “Student Modeling”, six under “Decision support System”
and last three as “Others” because they are different from other applications.

Fig: Application in Educational
Data Mining

Conclusion

In this study, we
reviewed, analyzed, integrated and introduced tasks specified in existing
surveys and books about Education Data Mining. We grouped similar applications into
categories and sub-categories. Classification of tasks is based on end
objectives. With the increase of Computer based learning and availability of
data, use of Education Data Mining will also grow, leading to further development
of new applications and various research topics.