A Systematic Review On Educational Data Mining

IJSRSET184432 | Received : 01 March | Accepted : 12 March 2018 | March-April-2018 [(4) 4 : 164-170 ]
© 2018 IJSRSET | Volume 4 | Issue 4 | Print ISSN: 2395-1990 | Online ISSN : 2394-4099
Themed Section : Engineering and Technology
164
A Systematic Review on Educational Data Mining
M. Manjula
P. G. Student, Department of Computer Science and Engineering, Adiyamaan College of Engineering, Hosur,
Tamil Nadu, India
ABSTRACT
In this paper, implementing K-Means clustering algorithm for analyzing the particular dataset and data mining.
The main purpose is WEKA process. In Weka process we can get perfect graph, accuracy and random process.
The Pre-processing was important concept it may clear a null values, removes a unwanted data and unwanted
memory space. In Data mining analyzing data set. In Data mining implementing two methods classification,
clustering process. By using classification, clustering we get flexible result and large amount of database. Here,
weka process and K-means algorithm going to compare whether both graphs are accurate manner.
Keyword : K-means clustering, Weka process, Classification, Cluster process.
I. INTRODUCTION
Educational Data Mining (EDM) applies machine-
learning, statistics, Data Mining (DM), psycho-
pedagogy, information retrieval, cognitive
psychology, and recommender systems methods and
techniques to various educational data sets so as
to resolve educational issues. The International
Educational Data Mining Society defines EDM as
“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” EDM is
concerned with analysing data generated in an
educational setup using disparate systems. Its aim
is to develop models to improve learning experience
and institutional effectiveness. While DM, also
referred to as Knowledge Discovery in Databases
(KDDs), is a known field of study in life sciences
and commerce, yet, the application of DM to
educational context is limited One of the pre-
processing algorithms of EDM is known as
Clustering. It is an unsupervised approach for
analysing data in statistics, machine learning,
pattern recognition, DM, and bioinformatics. It
refers to collecting similar objects together to
form a group or cluster. Each cluster contains
objects that are similar to each other but dissimilar to
the objects of other groups.
This approach when applied to analyse the
dataset derived from educational system is termed
as Educational Data Clustering (EDC). An
educational institution environment broadly
involves three types of actors namely teacher,
student and the environment. Interaction between
these three actors generates voluminous data that
can systematically be clustered to mine invaluable
information. Data clustering enables academicians
to predict student performance, associate learning
styles of different learner types and their
behaviours and collectively improve upon
institutional performance. Researchers, in the past
have conducted studies on educational datasets
and have been able to cluster students based on
academic performance in examinations various
methods have been proposed, applied and tested
in the field of EDM. It is argued that these
generic methods or algorithms are not suitable to

International Journal of Scientific Research in Science, Engineering and Technology (ijsrset.com)
M. Manjula et al. Int J S Res Sci. Engg. Tech. 2018 Mar-Apr;4(4) : 44-51
165
be applied to this emerging discipline. It is
proposed that EDM methods must be different from
the standard DM methods due to the hierarchical
and non-independent nature of educational data .
Educational institutions are increasingly being
held accountable for the academic success of their
students Notable research in student retention
and attrition rates has been conducted by Luan.
For instance, Lin applied predictive modelling
technique to enhance student retention efforts.
There exist various software’s like Weak, Rapid Miner,
etc. that apply a combination of DM algorithms
to help researchers and stakeholders find answers
to specific problems.
II. LITERATURE REVIEW
1. J. Luan, "Data mining applications in higher
education," SPSS Executive ( 2004): In this paper,
most challenge is higher learning institute, it is
improve quality Managerial decision. By using this
Managerial decision making they increased
Educational entities and more complex. In
Educational entities more efficient technology,
Managerial decision making support new strategies
current process. By addressing the new knowledge it
can improve the educational process and managerial.
In Data Mining technique used analysis tool so they
extract the knowledge of large dataset. 2. C. Romero
and S. Ventura “Educational data mining: A survey
from 1995 to 2005” (2007): Today most important role
is higher education for human begin. Educational data
mining have certain development for unique types of
data education setting, whether the student have
proper education. Educational data mining methods
are Prediction, Clustering, Relationship Mining, and
Discovery with model, Distillation of data for human
judgement. Even though they have some Drawbacks:
Course outline formation, teacher student
understanding and high output. 3. M.F.M. Mohsen,
N.M. Norwawi, C.F. Hibadullah, and M.H.A.
Wahhabi “Mining the student programming
performance using roughset” (2010): In this they
analysis the programming data set by using Roughset,
they investigate student programming data based on
previous student performance. The results were
compare to data using statistic, clustering and
association rules. Rough set concept is defined by
topologies operation, interior and closure. 4. J. E. Beck
and B. P. Woolf “High-level student modelling with
machine learning” (2000): In this paper they
constructed the student of behaviour high-level of
mathematic tutor and going to analysis whether
students have particular knowledge, learning about
student can answer a problem correctly. To construct
this model they used machine learning agent. This
agent is used for gather about student information
and current topics. It is very good for offline learning.
The main large drawback is online learning.
III. EXISTING SYSTEM
In an Existing system using many algorithm concept
to perform the mining concept. It was easy to perform
the mining operations. The Accessing speed is high in
Educational Data Mining applies machine-learning,
statistics, Data Mining(DM), psycho-pedagogy,
information retrieval, cognitive psychology, and
recommender systems methods and techniques to
various educational data sets so as to resolve
educational issues. The International Educational
Data Mining Society defines EDM as “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”. EDM is concerned with
analyzing data generated in an educational setup
using disparate systems. Its aim is to develop models
to improve learning experience and institutional
effectiveness. While DM, also referred to as
Knowledge Discovery in Databases (KDDs), is a
known field of study in life sciences and commerce,
yet, the application of DM to educational context is
limited.

166
One of the pre-processing algorithms of EDM is
known as Clustering. It is an unsupervised approach
for analyzing data in statistics, machine learning,
pattern recognition, DM, and bioinformatics. It refers
to collecting similar objects together to form a group
or cluster. Each cluster contains objects that are
similar to each other but dissimilar to the objects of
other groups. This approach when applied to analyze
the dataset derived from educational system is termed
as Educational Data Clustering (EDC). An educational
institution environment broadly involves three types
of actors namely Teacher, student and the
environment. Interaction between these three
actors generates voluminous data that can
systematically be clustered to mine invaluable
information. Data clustering enables academicians to
predict student performance; associate learning styles
of different learner types and their behaviors and
collectively improves upon Institutional performance.
Researchers, in the past have conducted studies on
educational datasets and have been able to cluster
students based on academic performance in
examinations.
Disadvantages
 In a Data mining concept particularly any
algorithms not using at educational purpose. So,
it was one of the drawback of to accessing
educational data’s.
 To accessing educational data’s the mining
process is slow.
 The basic concepts and basic algorithms only
using the educational mining process.
 Traditional data mining algorithms cannot be
directly applied to educational problems.
IV. PROPOSED SYSTEM
This process is implement that a preprocessing
algorithm has to be enforced first and only then some
specific data mining methods can be applied to the
problems. One such preprocessing algorithm in EDM
is Clustering. Many studies on EDM have focused on
the application of various data mining algorithms to
educational attributes. Therefore, this paper provides
over three decades long (1983-2016) systematic
literature review on clustering algorithm and its
applicability and usability in the context of EDM.
Future insights are outlined based on the literature
reviewed, and avenues for further research are
identified. The EDM process converts raw data
coming from educational systems into useful
information that could potentially have a greater
impact on educational research and practice”
Traditionally, researchers applied DM methods like
clustering, classification, association rule mining, and
text mining to educational context. A survey
conducted in 2007, provided a comprehensive
resource of papers published between 1995 and 2005
on EDM by Romero & Ventura. This survey covers
the application of DM from traditional educational
institutions to web-based learning management
system and intelligently adaptive educational
hypermedia systems. In another prominent EDM
survey by Pena-Ayala, about sample works published
between 2010 and 2013 were analyzed. One of the
key findings of this survey was that most of the EDM
research works focused on three kinds of educational
systems, namely, educational tasks, methods, and
algorithms.
Application of DM techniques to study on-line
courses was suggested by the proposed a non-
parametric clustering technique to mine offline web
activity data of learners. Application of association
rules and clustering to support collaborative filtering
for the development of more sensitive and effective e-
learning systems was studied by the researchers
Baker, Corbett & Wagner conducted a case study and
used prediction methods in scientific study to game
the interactive learning environment by exploiting
the properties of the system rather than learning the
system. Similarly, Brusilovsky Pylon provided tools
that can be used to support EDM. In their study Beck
& Woolf showed how EDM prediction methods can
be used to develop student models. It must be noted

167
that student modeling is an emerging research
discipline in the field of EDM While another
group of researchers, Garcia at al devised a toolkit
that operates within the course management systems
and is able to provide extracted mined information
to non-expert users. DM techniques have been used
to create dynamic learning.
Advantages
 Mining process speed is high.
 Easily access the educational and educational
types of all data’s.
 This process may fully concentrate the
education purpose.
 Using (EDM) process.
 So the pre-processing speed is high.
V. SYSTEM ARCHITECTURE
Figure 1. System Architecture
VI. MODULES
Figure 2. Flow Diagram
6.1 User Login
6.2 Load Data
6.3 Pre-processing
6.4 Mining Process
6.5 Searching Process
6.6 Weka Process
6.1 USER LOGIN-PROCESS
This module was important for user to access the
database. This process was most secure process the
authentication person only access the data’s. In this
process the user give the correct username and
password. If the name and password was wrong the
user does not access his/he own data’s. In large
database the security purpose was necessary and more
important.
6.2 LOAD DATA
In this process load the education relevant data’s. The
data’s may upload the large amount of database.

168
6.3 PRE-PROCESSING
Pre-processing is one of main modules for data
mining system. Here we are removing unwanted data
or null values and unstructured data. So when we
remove unstructured data’s then only we get accurate
results for given dataset. It is particularly applicable to
data mining and machine learning projects. Data-
gathering methods are often loosely controlled,
resulting in out-of-range values. Impossible data
combinations, missing values, etc. Analysing data that
has not been carefully screened for such problems can
produce misleading results.
6.4 MINING PROCESS
Data mining is the computing process of discovering
pattern in large data set involving methods at the
intersection of machine learning, statistic and
database system. The goal of data mining process is
extract information from dataset.
Figure 3. Data Distribution
Data mining have two methods
1. Classification
2. Clustering
6.1.1 CLASSIFICATION
Classification is the data mining (machine learning)
technique used for group of membership.
SVM (SUPPORT VECTOR MACHINE)
Support Vector machine (SVM) is a machine learning
algorithm and supervised learning algorithm that
analyze data used for classification and regression
analysis. SVM is powerful, strong theoretical and
strong regularization properties.
6.1.2 CLUSTERING PROCESS
Clustering can be considered the most important
unsupervised learning problem so, as every other
problem of this kind; it deals with finding a structure
in a collection of unlabeled data. In this mining
process may using some clustering algorithm’s to may
using this process. Example for clustering algorithms
(wards method).
WARDS METHOD
Wards method is a criterion applied in hierarchical
cluster analysis.
6.5 SEARCHING PROCESS
In this module the user may searching the important
data’s. This module may gives the correct formatted
data’s and cleaned data’s information’s. The user
viewing process may include in this process.
6.6 WEKA (Waikato Environment for Knowledge
Analysis) PROCESS
Weka is the collection of machine language algorithm
for data mining. The algorithm can either be applied
directly to a dataset or own java code. Weka contains
tools for Pre-processing, Classification, Clustering,
Association rules and Visualization.
Advantage
 Weka fully implemented in java programming
language, protable and platform independent.
 It is freely available under GUI General Public
License.
 Weka software contain very graphical user
interface, so system can easily access.
VII. CONCLUSION
This paper has presented over three decade’s
systematic review on clustering algorithm and its
applicability and usability in the context of EDM.

169
This paper has also outlined several future
insights on educational data clustering based on
the existing literatures reviewed, and further
avenues for further research are identified. In
summary, the key advantage of the application of
clustering algorithm to data analysis is that it
provides relatively an unambiguous schema of
learning style of students given a number of
variables like time spent on completing learning
tasks, learning in groups, learner behaviour in class,
classroom decoration and student motivation
towards learning. Clustering can provide pertinent
insights to variables that are relevant in separating the
clusters. Educational data is typically multi-level
hierarchical and non-independent in nature, as
suggested by Baker &Yosef [6] therefore a
researcher must carefully choose the clustering
algorithm that justifies the research question to
obtain valid and reliable results.
VIII. REFERENCES
[1]. C.Romero and S.Ventura, "Educational data
mining: a review of the state of the art,"
Systems,Man,and Cybernetics,Part C:
Applications and Reviews, IEEE Transactions
on, vol.40, pp.601-618, 2010.
[2]. (2011,01 July). International Educational Data
Mining Society. Available:
http://www.educationaldatamining.org/
[3]. J. Ranjan and K. Malik,"Effective educational
process: a data-mining approach," Vine,
vol.37, pp.502-515, 2007.
[4]. V. P. Bresfelean,M. Bresfelean,N. Ghisoiu,and
C. A. Comes,"Determining students’ academic
failure profile founded on data mining
methods," presented at the ITI 2008 - 30th
International Conference on Information
Technology Interfaces, 2008.
[5]. J. Vandamme,-P.,Meskens,N.,Superby,F.-
,J,"Predicting Academic Performance by Data
Mining Methods," Education Economics,
vol.15,pp.405-419, 2007.
[6]. R. S. Baker and K. Yacef,"The state of
educational data mining in 2009: A review
and future visions," JEDM-Journal of
Educational Data Mining, 2009.
[7]. J. P. Campbell,P. B. DeBlois,and D. G.
Oblinger,"Academic analytics: A new tool for a
new era," Educause Review, vol.42, p.40,2007.
[8]. J. Luan,"Data mining applications in higher
education," SPSS Executive, vol.7, 2004.
[9]. S.H.Lin, "Data mining for student retention
management," Journal of Computing Sciences
in Colleges, vol.27, pp.92-99, 2012.
[10]. T. Denley,"Austin Peay State University:
Degree Compass," EDUCAUSE Review
Online., 2012.
[11]. M. F. M. Mohsin,N. M. Norwawi,C. F.
Hibadullah,and M. H. A. Wahab,"Mining the
student programming performance using
rough set," presented at the Intelligent
Systems and Knowledge Engineering (ISKE),
2010 International Conference on, 2010.
[12]. C. Romero and S. Ventura,"Educational data
mining: A survey from 1995 to 2005," Expert
Systems with Applications, vol.33, pp.135-
146,7/ 2007.
[13]. A.Pena-Ayala, "Educational data mining: A
survey and a data mining-based analysis of
recent works," Expert Systems with
Applications, vol. 41, pp.1432-1462, 3/ 2014.
[14]. O.R.Zaıane and J.Luo, "Web usage mining for a
better web-based learning environment," in
Proceedings of conference on advanced
technology for education, 2001, pp.60-64.
[15]. O.R.Zaıane, "Building a recommender agent for
e-learning systems," in Computers in
Education,2002. Proceedings. International
Conference on, 2002, pp.55-59.
[16]. R.S.Baker, A.T.Corbett, and A.Z.Wagner, "Off-
task behavior in the cognitive tutor classroom:
when students game the system," in
Proceedings of the SIGCHI conference on
Human factors in computing systems, 2004,
pp.383-390.

170
[17]. P. Brusilovsky and C. Peylo,"Adaptive and
intelligent web-based educational systems,"
International Journal of Artificial Intelligence
in Education, vol.13, pp.159-172, 2003.
[18]. J.E.Beck and B.P.Woolf, "High-level student
modeling with machine learning," Intelligent
tutoring systems, pp.584-593, 2000.
[19]. E. Garcia,C. Romero,S. Ventura,and C. de
Castro,"A collaborative educational
association rule mining tool," The Internet
and Higher Education, vol.14, pp.77-88, 2011.
[20]. Y. H. Wang and H. C. Liao,"Data mining for
adaptive learning in a TESL-based e-learning
system," Expert Systems with Applications, vol.
38, pp.6480-6485, 2011.
[21]. M. E. Zorrilla,E. Menasalvas,D. Marin,E.
Mora,and J. Segovia,"Web usage mining project
for improving web-based learning sites," in
Computer Aided Systems Theory–
EUROCAST 2005,ed: Springer,2005, pp.205-
210.
[22]. T.S.Madhulatha, "An overview on clustering
methods," arXiv preprint arXiv:1205.1117,
2012.
[23]. A.K.Jain and R.C.Dubes, Algorithms for
clustering data.Englewood Cliffs,NJ, USA:
Prentice-Hall, Inc., 1988.
[24]. S.Sagiroglu and D.Sinanc, "Big Data: A Review,"
Proceedings of the 2013
[25]. B. Kitchenham,O. Pearl Brereton,D. Budgen,M.
Turner,J. Bailey,and S. Linkman,"Systematic
literature reviews in software engineering–a
systematic literature review," Information and
software technology, vol.51, pp.7-15, 2009.
[26]. H. M. Chen and M. D. Cooper,"Using
clustering techniques to detect usage patterns
in a Web‐based information system," Journal
of the American Society for Information
Science and Technology, vol.52, pp. 888-904,
2001.
[27]. N.A.Rashid, M.N.Taib, S.Lias, N.Sulaiman,
Z.H.Murat, and R.S. S.A.Kadir, "Learners’
Learning Style Classification related to IQ and
Stress based on EEG," Procedia - Social and
Behavioral Sciences, vol. 29, pp.1061-1070, /
2011.

A Systematic Review On Educational Data Mining

More Related Content

Similar to A Systematic Review On Educational Data Mining (20)

More from Katie Robinson (20)

Recently uploaded (20)

A Systematic Review On Educational Data Mining