Download Data Mining In Education

International Journal of Multidisciplinary Research and Development
Online ISSN: 2349-4182 Print ISSN: 2349-5979
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Volume 3; Issue 3; March 2016; Page No. 43-48; (Special Issue)
Data Mining In Education
Selvapriya M, Dr. J Komala Lakshmi
Research Scholar, Department of Computer Science, S.N.R SONS College (Autonomous), Coimbatore, Tamil Nadu, India
Abstract
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where data mining is the core
of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may
then be converted into knowledge. Data mining is the process of extracting the information and patterns derived by the KDD
process which helps in crucial decision-making. Selection, transformation, mining and results interpretation. In this paper, it is
reviewed that Knowledge Discovery perspective in Data Mining and consolidated different areas of data mining, its techniques
and methods in it. Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and
techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving
learning experiences and improving institutional effectiveness. EDM methods are useful to measure the performance of students,
assessment of students and study students’ behavior etc. In recent years, Educational data mining has proven to be more
successful at many of the educational statistics problems due to enormous computing power and data mining algorithms. This
paper describes how to apply the main data mining methods such as prediction, classification, relationship mining, clustering, and
social area networking to educational data.
Keywords Decision, Knowledge, Mining, Selection, Transformation, Warehouse, educational data mining, academic analytics,
learning analytics, institutional effectiveness
1. Introduction
Knowledge Discovery in Databases (KDD) is the process of
finding useful knowledge from large dataset. Data
preparation, pattern search, knowledge evaluation and
refinement are steps of KDD. The process of Knowledge
Discovery consists of Data Cleaning, Data Integration, Data
Selection, Transformation, Data Mining and Pattern
Evaluation Phases. In short, data mining is process of
deriving patterns from large databases [3]. DM analyses large
dataset to extract hidden patterns such as similar groups of
data records using clustering technique. This data is used for
machine learning and predictive analysis. Query languages
or graphical user interface are required to express the DM
requests and discovered information,
so that results
obtained from the DM Engine become understandable and
usable for end users.
Machine Learning (ML) [4]. Data mining produce useful
patterns by applying algorithmic methods on observational
data. Data mining algorithms show best results for
numerical data but with the emergence of Statistics and
Machine Learning techniques, algorithms have been
developed to mine non numerical data and relational
databases [5].
DM applications are successfully implemented in various
fields like health care, finance, retail, telecommunication,
fraud detection, risk analysis, education etc [8]. Due to
increasing complexities in various fields and improvements
in technology, there are new challenges to DM.
Fig 1: The steps of extracting knowledge from data.
3. Educational Data Mining
The educational data mining community [16] defines
educational data mining as, “Educational Data Mining
(EDM) 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 setting which
they learn in”. EDM focuses on collection, archiving, and
analysis of data related to students’ learning and assessment
the application of data mining in educational system is an
interactive cycle of hypothesis formation, testing, and
refinement as shown is Fig 2 [10]. Discovered knowledge
should enter the loop of the system and guide, facilitate, and
enhance learning as a whole. The system is used to turning
data into knowledge as well as filtering mined knowledge
for decision making.
2. Data Mining Trends
Data Mining introduced in the year 1990’s and it is the
combination of many disciplines like database management
systems (DBMS), Statistics, Artificial Intelligence (AI), and
4. EDM Methods
Romero and Ventura, [11, 12], and Baker categorize methods
in educational data mining into the following general
categories:
43 




Prediction
Clustering
Relationship mining
Discovery with models
Distillation of data for human judgment
Fig 2: The cycle of applying data mining in educational system
The first three categories of educational data mining method
are largely acknowledged to be universal across types of
data mining. The fourth and fifth categories achieve
particular prominence within educational data mining.
4.1 Prediction
The goal of prediction is to develop a model which can infer
a single aspect of the data (predicted variable) from some
combination of other aspects of data (predictor variables).
Prediction has two key uses within educational data mining.
In the first type we study the features of the model used for
prediction. This can be mainly used for analysis of student’s
performance. In the second type the output values are
predicted based on the context. Prediction can be classified
in three types Classification, Regression and Density
estimation.
4.1.1 Classification
Classification consists of assigning a class label to a set of
unclassified cases. In the Supervised Classification, the set
of possible classes is known in advance. In the
Unsupervised Classification, Set of possible classes is not
known. After classification we can try to assign a name to
that class. Unsupervised classification is called clustering.
Some popular classification methods include logistic
regression, support vector machines and decision trees. The
decision tree algorithm produces a tree-like structure of the
model it produces. Form the tree it is then easy to generate
rules in the form IF condition THEN outcome. It is basically
a predictive model in which an instance is classified by
following the path of satisfied condition from root until
reaching a leaf, which will correspond to class label. Some
of the most well-known decision tree algorithms are C4.5
and ID3.
4.1.2 Regression
Regression is a data mining function that predicts a number.
A regression task begins with a data set in which the target
values are known. Regression models are tested by
computing various statistics that measure the difference
between the predicted values and the expected values. Some
popular regression methods within educational data mining
include linear regression, neural networks and support
vector machine regression. The real-world educational data
mining problems cannot be simply predicted. So some
complex techniques are required to forecast the values using
combination of various techniques. Neural network too can
create both classification and regression models.
4.2 Clustering
Clustering: the process of grouping physical or abstract
objects into classes of similar objects [14]. Clustering is a
process of partitioning a set of data (or objects) into a set of
meaningful sub-classes, called clusters. It helps users
understand the natural grouping or structure in a data set.
Clusters of objects are formed so that objects within a
cluster have high similarity in comparison to one another,
but are very dissimilar to objects in other clusters. Each
cluster formed can be viewed as a class of objects, from
which rules can be derived [7]. Application of clustering in
education can help in finding academic trends, student’s
performance analysis in class. In educational data mining,
clustering has been used to group students according to their
behavior [2]. The schools could be clustered together (to
investigate similarities & differences between schools),
students could be clustered together (to investigate
similarities and differences between students). Some of the
well known clustering methods are k-mean and expectationmaximization algorithm (EM-clustering) [9].
4.3 Relationship Mining
In relationship mining, the goal is to discover relationships
between variables, in a data set with large number of
variables. This may take the form of attempting to find out
which variables are most strongly related/ associated with a
single variable of particular interest. Broadly relationship
mining is classified into four types: association rule mining,
correlation mining, sequential pattern mining, and casual
data mining.
4.3.1 Association rule mining
Association rules are if/then statements that help uncover
relationships between unrelated data in a relational database
or other information repository. An association rule is an
implication of the form: X  Y, where X, Y  I, and X Y
=.
Association rule mining has been applied to
educational data mining for: finding students’ mistakes
often occurring together while solving exercises [15], finding
44 out relationships in learners’ behavior patterns [17],
diagnosing students’ learning problems and offers students
advice [13] etc.
4.3.2 Correlation mining
In correlation mining, the goal is to find (positive or
negative) linear correlations between variables. Correlation
analysis is used to find the most strongly correlation
attributes
5. The Various Data Mining Areas
5.1 Web Mining
Web mining is the application of data mining to discover the
patterns from the Web in the form of data collected from
online information databases, hyperlinks, and digital data.
Data mining technique used in web mining are
Classification
(supervised
learning),
Clustering
(unsupervised learning) [19, 20].
5.2 Ubiquitous data mining
Increasing computational capacity and emergence of latest
electronic devices leads to ubiquitous or pervasive
computing paradigm [21]. The Ubiquitous computing
environments give rise to Ubiquitous Data Mining (UDM).
5.3 Data mining using multimedia
The multimedia data includes images, video, audio, and
animation. Data mining techniques followed in multimedia
data are rule based decision tree classification algorithms
like Artificial Neural Networks, Instance-based learning
algorithms, Support Vector Machines, Association rule
mining, clustering methods [23].
5.4 Spatial data mining
The spatial data includes astronomical and data related to
space technology. It includes the use of spatial warehouses,
spatial data cubes, spatial OLAP, and clustering methods
[25]
.
5.5 Emergence of Data mining in other fields
Other data mining areas include visualization, medical,
pattern, wireless networks, association rule based mining.
6. Performance Improvement in Education Sector
6.1.1 Data Mining Techniques in Education
Applying data mining techniques to educational data for
knowledge discovery is significant to educational
organizations as well as students. Knowledge driven data
supports educational decision support system. Educational
data mining enhance our understanding of learning by
finding educational trends which includes improving student
performance, course selection, in-house trainings and
faculty development. Using linear regression analysis [26],
some factors are correlated to student’s academic
performance like mother’s education and student’s family
income. DM combines machine learning, statistics and
visualization techniques to discover and extract knowledge.
Questionnaires and feedback forms are often used to collect
data related to students’ approach towards educational
patterns or trends, interest towards technologies, teaching
methodologies followed and data collected is to be analyzed
using techniques like decision tree, neural networks etc.
There are different Mining models like Decision Trees,
Naïve Bayes, Support Vector Machines, Linear Regression,
Minimum Description Length, K-means, and O-Cluster. By
using these models, one can get Student Behaviors Patterns,
Course Behavior Patterns, Predict Student Retention, Predict
Course Suitability, and Personalized Intervention Strategy
[27]
.
6.1.2 Statistics and visualization
According to Tsantis & Castellani [31], Student’s log history
and usage statistics are helpful in evaluation of an e-learning
system. Information visualization techniques [28] can be used
to graphically represent student data like his maximum
interest towards which technologies or interest which he has
shown in solving questionnaires etc are collected by webbased educational systems. Visualization techniques
involves conversations among online groups, social
networking websites etc. These techniques are also helpful
for instructors which can manipulate the graphical
representations generated and get the understanding and
interest of their learners.
6.2 Web mining
Srivastava et al., [32] proposed, “Web mining is used to
extract knowledge from web data”. In web mining useful
information is extracted from the contents of web
documents and web usage mining is another technique to
discover meaningful patterns from data generated by clientserver Transactions on one or more web localities.
6.2.1 Clustering, classification and outlier detection
Clustering and classification are both classification methods.
Clustering is unsupervised and classification is supervised.
Classification and prediction are also related techniques.
Classification predicts class labels, whereas prediction
predicts continuous-valued functions and outlier is an
observation that is unusually large or small relative to the
other values in a dataset. According to Chen, Liu [35]
decision tree i.e. C5.0 algorithm and data cube technology
are used for managing classroom processes. Induction
analysis helps in identifying potential student groups having
similar characteristics. Talavera and Gaudioso [36] propose
mining student data using clustering to discover patterns
reflecting user behaviors.
6.2.2 Adaptive and intelligent web-based educational
systems
Tang et al. [37] gives concept of data clustering for web
based learning and to help in solving learner based
problems. They find clusters of students with similar
learning characteristics based on the sequence and the
contents of the pages they visited.
6.3 Association rule mining
Association rule mining is popular mining method used
between set of items in large databases.
Here one or more attributes of a dataset are associated with
each other using IF-THEN statements.
6.3.1 Particular web-based courses
Ha et al. [38] perform web page navigational structure
analysis from web-based virtual classrooms, e-learning
portals and web pages navigated by learners.
45 6.3.2 Adaptive and intelligent web-based educational
systems
Lu uses association fuzzy rules in a personalized e-learning
material recommender system. Fuzzy matching rules are
used to discover associations between student’s
requirements and a list of learning materials [38]. Romero et
al. [39] propose to use grammar-based genetic programming
with optimization techniques for providing a feedback to
authors who designed courses and derived relationships
from student’s usage information.
6.4 Text mining
In text mining, mining is done on text data and is related to
web content mining. It is an interdisciplinary area involving
machine learning and data mining, statistics, information
retrieval and natural language processing[28,40].Text
mining can work with unstructured or semi-structured
datasets such as full-text documents, HTML files, emails,
etc.
6.5 Web-based educational systems
Data mining and text mining technologies are used in Webbased educational systems for shared learning. Text mining
is used for discussion board for expanded correspondence
analysis. Learners select the relevant category which
represents his/her comment and the system provides
evaluations for learner’s comments between peers.
6.5.1 Well-known learning content management systems
Dringus and Ellis [41, 42] propose to use text mining as a
strategy for assessing conversations among irregular
discussion forums. Text mining techniques also helps in
evaluating the progress of a thread or user group
discussions. Data can be retrieved from pdf interactive
multimedia productions for helping the evaluation of
multimedia presentations for statistics purpose and for
extracting relevant data [38, 39]. Web-based educational
systems collect large amount of student data from web log
history which can be further analyzed for deriving
meaningful patterns [43].
6.5.2 Adaptive and intelligent web-based educational
systems
Tang et al. [37] propose to construct a personalized web
based application by which mining can be done on both
framework and structure of the courseware. Keyword-driven
text mining algorithms are used to select articles for distance
learning students.
7. Conclusion
Educational Data Mining is an upcoming field related to
several well-established areas of research including elearning, web mining, text mining etc. Data Mining
Techniques are used to analyze Educational data and extract
useful information from large amount of data. This paper
presents review of the KDD and basic data-mining
techniques so as to integrate research in this area. The KDD
field is related to development of methods and techniques
which make the data relevant. In Educational Sector
software’s and visualization techniques can be developed
using Data Mining Techniques which not only predict
student’s performance in examinations as well as helps us to
cluster those students who need special attention in their
studies. Knowledge Discovery in Databases results in better
decision-making related to latest technologies useful in
classroom teaching as well as faculty enhancement
programs and in-house trainings etc. Using data mining
techniques we can achieve refined data from distributed
databases. Data Mining is an efficient tool for improving
institutional effectiveness and student learning. Knowledge
ac-quired by Educational Data Mining not only help
teachers to manage their classes, improves their teaching
skills, students learning processes but also provide feedback
to institutions to im-prove their infrastructures and quality.
For making this approach successful and to increase its
scope, more data can be collected from Educational
Institutions and queries can be performed on it. Using
Techniques like Decision Tree we can predict the Class
Result of students based on the attributes taken. Decision
tree classifiers are used on student's data to predict the
student's performance in class result. These techniques will
help in identifying those students who are below attendance
and shown poor performance in Sessional. The main finding
of using these techniques is the gathering of knowledge
from student’s academic performance. Another helpful
technique is K-Means Clustering through which we can
cluster the students based on some attributes like their Class
Performance, sessional and Attendance in class. Centroid
are calculated from the educational data set taking Kclusters. It enhances the decision making approach to
monitor the performance of students. On increasing the
value of K clusters, the accuracy becomes better with huge
dataset and K means can find the better grouping of the data.
It also helps us to clusters those students who need special
attention. This review on the Knowledge Discovery
perspective in Data Mining would be helpful to find useful
patterns related to Educational Data Set.
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Mrs. Selvapriya. M has finished her
Bachelor of Computer Applications from
Hindusthan College of Arts & Science,
Coimbatore. She has completed her
M.C.A from Hindusthan College of Arts
& Science, Coimbatore. She has been
awarded her M.Phil in Data Mining from
Hindusthan College of Arts & Science, Coimbatore. She is
working as Assistant professor in Hindusthan College of
Arts & Science, Coimbatore for seven years. She is
currently a regular part - time Research Scholar in
Department of Computer Science at SNR SONS
COLLEGE, Coimbatore, Tamil Nadu, India working
towards her Ph.D.
Mrs. J. Komala Lakshmi has finished her
B.Sc Mathematics from Seetha Lakshmi
Achi College for women, Pallathur, during
1995. She has completed her M.C.A from J.
J. college of Arts and Sciences, Pudhukottai
during 1998. She has been awarded her
M.Phil Computer Science, Bharathiar
University during 2008. She has been awarded with the
Silver medal for her academic Excellence in M.C.A from
the Institution. She has ten years of teaching experience in
collegiate service. She is currently working as Assistant
Professor, Department of Computer Science, SNR SONS
COLLEGE, Coimbatore. She has presented her papers in
three international conferences. She has published papers in
six international journals. She also contributed a book
publications to the computer science students. She was a
Graduate Student Member of IEEE. She has been awarded
with the recognition from Who’s Who in the World 2011s,
28th Edition for her not able contribution to the society.
Biography
M. Selvapriya completed my Bachelor of Computer
Application from Hindusthan College of Arts & Science,
Coimbatore. I completed M.C.A. from Hindusthan College
of Arts & Science and I have been awarded with M.Phil, in
Data mining from Hindusthan College of Arts & Science,
Coimbatore. I am having a teaching experience of 8 years as
Assistant Professor, Department of Computer Technology &
Information Technology in Hindusthan College of Arts &
Science, Coimbatore. Now, I am currently pursuing my
Part-Time Ph.D as a Research Scholar in Department of
Computer Science, SNR Sons College, Coimbatore. I have
published 2 International Journals and participated and
presented papers in various workshops, seminars and
conferences. My research areas include Data Mining.
48