A Survey on Educational Data Mining Techniques

Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume: 05 Issue: 02 December 2016, Page No.167-171
ISSN: 2278-2419
167
A Survey on Educational Data Mining Techniques
A.S. Arunachalam1
, T.Velmurugan2
1
Research scholar, Department of Computer Science, Vels University, Chennai, India.
2
Associate Professor, PG and Research Department of Computer Science, D.G.Vaishanav College, Chennai, India.
Mail:arunachalam1976@gmail.com, velmurugan_dgvc@yahoo.co.in
Abstract - Educational data mining (EDM) creates high impact
in the field of academic domain. The methods used in this topic
are playing a major advanced key role in increasing knowledge
among students. EDM explores and gives ideas in
understanding behavioral patterns of students to choose a
correct path for choosing their carrier. This survey focuses on
such category and it discusses on various techniques involved
in making educational data mining for their knowledge
improvement. Also, it discusses about different types of EDM
tools and techniques in this article. Among the different tools
and techniques, best categories are suggested for real world
usage.
Key words: Educational Data Mining, Web Mining, E-
Learning, Data Mining Techniques
I. INTRODUCTION
Collecting relevant student record and analyzing the same from
huge record set always remain difficult task for researchers.
Data mining process of extracting hidden information from
large database provides a meaningful solution for educational
data mining. The researcher also faces many problems in
implementing the developed system for educational data
mining in different platform. Huge number of developments in
educational courses always remains difficult task for students
in choosing best course. Current web based course applications
doesn’t provide static learning materials by understanding
students mentality. User friendly environment for web based
educational system always remain a good solution to richer
learning environment. In traditional education system, students
share their learning experiences one to one interaction and
continual evaluation process [1]. Classroom Evaluations
processes by observing student’s attitude, analyzing record,
and student appraisal in teaching strategies. The supervision is
not possible when the students working in IT field; pedagogue
chooses for other techniques to get class room data.
Institutions, which run websites for distance learning, collect
huge data, collecting server access log and web server by
automatically. Web based learning analysing tools available
online increses the interaction data between acdemicion and
students [2]. Most effective learninig environment can be
carried by following data mining techniques. The data mining
techniques stages starts from pre processing to post processing
techniques by following KDD process of identifying necessary
educational data. Web based domain area E-commerce uses
data mining techniques in advancing educational mining. E-
Learning process gives optimal solution for improving the
educational data mining process. Some differents in E-learning
and E-Commerce systems are disscussed below [3].
E-commerce technology is used for communicating client with
server for commercial purpose. Web based applications are
used for carrying out this technology. Web access log files
stores E Commerce transaction data for money transfer. The
commercial web sites transaction data passes the control to
bank website application and purchases happen. E Learning
technology is used for learning from web sites. The users can
gather necessary knowledge from web based learning process.
The web applications used in this type of websites are very
useful in providing knowledge for learners. Web based
applications stores the information through web access log
files, which eventually stores information about users working
on the websites.
In educational system the knowledge assessment techniques
applied to improve students’ learning process. The formative
assessment process evaluating continues improvement of
students learning capacity. The formative system helps the
educator to improve instructional materials. The data mining
techniques helped the educator to make academic decision
when designing or editing the teaching methodology. The
educational data mining follow the common data mining
methods. Extracted information should enter the circle of the
system and guide, fine tuning and refinement of learning [4].
This data not only becoming the knowledge, it improves the
mined knowledge for decision making. The rest of the survey
paper is planned as follows. Section 2 discusses about the basic
concepts of educational data mining. In section 3, it is
discussed about tools used for educational data mining. The
applications or techniques of educational data mining are
illustrated in section 4. Finally, section 5 concludes the survey
work.
II. EDUCATIONAL DATA MINING
Educational data mining play a major role in society and
educational area. The data mining sequence applying in
educational system can be clearly represented with a diagram
shown in Figure1.
Figure 1: Data mining sequence applying in Educational
System

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168
a. Academician
Academician plays a major role in designing educational
system. The educational system should be constructed for
better benefits to students. This effort may cause dramatic
changes in educational environment and society. The
academicians and teachers should analyze students’ records
and construct the better educational system. Data mining
techniques provides an additional benefit to academicians for
analyzing student’s behavior based on historical data.
b. Educational System
Proper educational system provides a healthy environment and
society. The students are one of the major factors in
educational system that eventually makes the society healthy.
The rules and regulations are made possible by the use of
experienced academicians.
c. Data mining Techniques
Historical Students records are collected from various localities
and data mining techniques are applied. The unwanted data are
removed by applying preprocessing technique. There are
multiple data mining techniques have been used in EDM
process including classification, association, prediction,
clustering, sequential pattern and decision tree.
d. Analyzing for Recommendation
The goal to have constraint about how to develop educational
system efficiency and get used to it to the performance of their
students, have measures about how to enhanced categorize
institutional resources (human and material) and their
educational offer, enhance educational programs offer and
determine effectiveness of the new computer mediated distance
learning approach.
e. Students
The objective is absorb students learning experience,resources,
their activities and interest of learning based on the
responsibilities already done by the student and their successes
and on errands made by other similar learners, etc.
III. TOOLS FOR EDUCATIONAL DATA MINING
Zaiane and Luo discusses about Web Utilization Miner
(WUM) for students’ feedback system. This added value by
specific event recording on the E- learning side will give click
steams and the patterns discovered a better meaning and
interpretation [5]. Silva and Vieira uses web usage ranking
technique to identify student information web pages. MultiStar
textually presents the patterns it finds. The patterns resulting
from the classification task are expressed through certain rules
[6]. Shen implements student academic performances
visualization through statistical graphs [7]. Romero discovers
interesting prediction rules from student usage information to
improve adaptive web courses like AHA!(Adaptive
Hypermedia for All). He also uses a visual tool (EPRules)
discover prediction rules and it is oriented to be used by the
teacher [8]. Tane propose an ontology-based tool to build the
majority of the resources available on the web. He uses text
mining and text clustering techniques in order to group papers
according to their topics and similarities [9]. Merceron and
Yacef , used traditional SQL queries to mining student data
captured from a web-based tutoring tool and association rule
and symbolic data analysis. Their objective is to find mistakes
that often occurs together [10]. Becker introduced Sequential
patterns can expose which content has provoked the access to
other contents, or how tools and contents are tangled in the
learning process [11]. Avouris, N., Komis, V., Fiotakis, G.,
Margaritis, M. and Voyiatzaki, E. develop automatically
generated log files by introducing contextual information as
additional events and by associating comments and static files
[12]. Mazza and Milani introduced a tool GISMO/CourseVis
which features Information visualization techniques can be
used to graphically render multidimensional, complex student
tracking data collected by web-based educational systems these
techniques facilitate to analyze large amounts of information
by representing the data in some visual display [13]. Mostow
used Listen tool for understanding of their learners and become
aware of what is happening in distance classes [14]. Damez,
M., Dang, T.H., Marsala, C. and Bouchon-Meunier, B., used a
fuzzy decision tree for user modeling and discriminating a
learner from an experimented consumer automatically. They
use an agent to learn the cognitive characteristics of a user’s
relations and classify users as experimented or not [15]. Bari
and Benzater recover data from pdf hypermedia productions
for serving the assessment of multimedia presentations, for
statistics purpose and for extracting relevant data. They
recognize the major blocks of multimedia presentations and
recover their internal properties [16]. Qasem A. Al.Radaideh
has introduced CRISP Framework for mining student related
academic data. He have used decision tree as a classification
technique for rule mining in academic data [17]. Cristobal
Romero also introduced a tool KEEL which is a software tool
to access evolutionary algorithms to solve various data mining
problems in regression, classification and unsupervised
learning [18]. C.Marquez-Vera has introduced an SMOTE
algorithm for classifying rebalanced students success rate using
10 Fold Cross Validation, which the rules was implemented
and tested in WEKA [19]. Dragan Gašević identifies the
critical topics that require immediate research attention for
learning analytics to make a sustainable impact on the research
and practice of learning and teaching by using online learning
tool and video annotation tool [20].
IV. TECHNIQUES FOR EDUCATIONAL DATA
MINING
Cristobal Romero compares different type of data mining
classification techniques with the use of Moodle usage data of
Cordoba University. He modeled a rebalanced preprocessing
technique for classifying original numerical data [21].
Ramasami has developed a Predictive data mining model for
identifying slow learners and to study the influence of the
dominating factor of their academic performances. He also
introduced CHAID model for predicting slow learners in an
accurate manner [22]. Edin Osmanbegovie successfully
implemented a datamining technique in higher education
socio-demographic variables and analysis high school entrance
exam and attribute related to it. He also uses some of
supervised algorithms for analyzing the student data [23].
Sujeet Kumar Yadav has used three decision trees and three
machine learning algorithms (ID3, C4.5, and CART) for
obtaining students predictive model. He also classifies by
giving accuracy value in time and identifies student’s success
and failure ratio [24].

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Table 1: Educational Data Mining Tools
Authors Tool Mining task Findings
Zaiane and
Luo (2001)
WUM
Association and
Patterns
E- learning side will give click steams and the patterns discovered a
better meaning and interpretation.
Silva and
Vieira
(2002)
MultiStar
Association and
classification
MultiStar textually presents the patterns it finds. The patterns
resulting from the classification task are expressed through certain
rules.
Shen et al.
(2002)
Data Analysis
Center
Association and
classification
E- Learning system for solving problem like students and teacher’s
interaction problem in assignments and other problems.
Romero et
al. (2003)
EPRules Association
Grammar based genetic programming with multi-objective
optimization techniques for providing a feedback to course ware
authors was developed and identifies increasing relationship in
student’s usage data.
Tane et al.
(2004)
KAON
Text mining and
Clustering
The Course ware Watch dog addresses the different needs of
teachers and students. It integrates the Semantic Web vision by
using ontologies and a peer-to-peer network of semantically
annotated learning material.
Merceron
and Yacef
(2005)
TADA-ED
Classification and
Association
Implementing traditional SQL queries to mining student data from
a web based tutoring tool. The objectives of fining mistakes that
occur often are done with accurate rating.
Becker et al.
(2005)
O3R Sequential patterns
The proposed filtering functionality 1) They have the support of the
ontology to understand the domain, and establish interesting filters,
and 2) Direct manipulation of domain concepts and structural
operators minimize the skills required for defining filters.
Mostow et
al. (2005)
Listen tool Visualization
Log each distinct type of tutorial event in its own table. Include
student ID, computer, start time, and end time as fields of each such
table so as to identify its records as events.
Merceron
and Yacef
(2005)
TADA-ED
Classification and
Association
Implementing traditional SQL queries to mining student data from
a web based tutoring tool. The objectives of fining mistakes that
occur often are done with accurate rating.
Avouris et
al. (2005)
Synergo/ ColAT
Statistics and
visualization
Main features of two tools that facilitate analysis of complex field
data of technology mediated learning activities, the Synergo
Analysis Tool and ColAT.
Mazza and
Milani
(2005)
GISMO/
CourseVis
Visualization
GISMO has been implemented based on authors previous
experience with the CourseVis research, and proposes some
graphical representations that can be useful to gain some insights
on the students of the course.
Damez et al.
(2005)
TAFPA Classification
Four new steps were expected as four questions were asked, but
that made a notice that one user missed a question by double-
clicking accidentally on the button “Show next question”. It can
lead to some mistakes to use the LCS.
Qasem A.
Al.Radaideh
(2006)
CRISP Classifier Classification
The classification algorithms ID3, C4.5 and Naive Bayes correctly
classification parentage accuracy rating is not so high.
Cristobal
Romero
(2009)
KEEL
Régression,
classification and
unsupervised
Learning
Association rule mining has been used to provide new, important
and therefore demand-oriented impulses for the development of
new bachelor and master courses
C.Marquez-
Vera (2010)
10 Fold Cross
Validation using
WEKA Tool
Classification
Rule induction algorithms such as JRip, NNge, OneR, Prism and
Ridor; and decision tree algorithms such as J48, SimpleCart,
ADTree, RandomTree and REPTree are used for experiment
because these algorithms can be used directly for decision making
and provides detailed classification results.
Dragan
Gasevic
(2015)
Online learning
tools and video
annotation tool
Classification and
segmentation
Transition graphs are constructed from a contingency matrix in
which rows and columns were all events logged by the video
annotation tool.

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Table 2: Techniques for Educational Data Mining
Authors Techniques Mining task Findings
Cristobal
Romero
(2007)
Moodles Classification
C4.5 and CART algorithms are simple for instructors to
understand and interpret. GGP algorithms have a higher
expressive power allowing the user to determine the specific
format of the rules.
M. Ramasami
(2010)
CHIAD model Association
Features whose chi-square values were greater than 100 were
given due considerations and the highly influencing variables
with high chi-square values. These features were used for the
CHAID prediction model construction.
Edin Osman
begovie (2012)
L86 Classifier
Classification
and Association
The Results shows that the naïve Bayes algorithms performance
and accuracy level for decision tree is much more than that of
the neural network method for decision tree classification.
Sujeet Kumar
Yadav (2012)
C4.5,ID3,CART
Algorithm based tool
Classification
ID3, C4.5 and CART machine learning algorithms that produce
predictive models with the best class wise accuracy. Classifiers
accuracy True positive rate for FAIL is 0.786 for ID3 and C4.5.
The created model successfully classified.
Dorina
Kabakchieva
(2013)
CRISP-DM Classification
The J48 classifier correctly classifies about 66% of the instance
with 10-fold cross-validation testing and 66.59 % for the
percentage split testing and produces. The achieved results are
slightly better for the percentage split testing option.
Abeer Badr El
Din Ahmed
(2014)
Decission Tree (ID3
Algorithm)
Classification
For measurements of best attribute for a particular node in the
tree Information Gain are used with attribute A, relative to a
collection of sample S.
Xing Wanli
(2015)
GP-ICRM for rule
generation and work
flow
Classification
Implemented EDM, theory and application to solve the problem
of predicting student’s performance in a CSCL learning
environment with small datasets. Model for performance
prediction is evaluated using a GP algorithm.
Ashwin
Satyanarayana
(2016)
Bootstrap Averaging
Classification
and Clustering
Single Model: Decision trees (J48) was used for single filtering
base model.
Online Bagging: Implemented online bagging using Naive
Bayes as the base model. Ensemble Filtering: The proposed
algorithm uses the following classifiers: J48, RandomForest and
Naive Bayes.
Dorina Kabakchieva implemented CRISP (Cross-Industry
Standard Process) approach for Data mining model for non-
property, freely available and application neutral standard for
data mining projects. Author also discusses about decision tree
classifier of NaiveBayes and BayesNet with J48 10 fold cross
validation and J48 percentage split and identifies weighted
average. Same J48 10 fold cross validation and J48 percentage
split comparison testing was carried for K-NN Classifier (with
k=100 and k=250) and OneR and JRip classifiers [25]. Abeer
Badr El Din Ahmed uses decision tree method for predicting
students’ performance with the help of ID3 Algorithm [26].
Xing Wanli introduces student prediction messures by using
different rules and uses gnetic operator for classification and
evaluate offspring for analysing student participations [27].
Ashwin Satyanarayana uses multiple classifiers such as J48,
NaiveBayes, and Random Forest for classifying students’
prediction. He also uses K-means clustering algorithm for
calculating similar cluster cancroids average in student cluster
[28].
V. CONCLUSION
Educational data mining is the most valuable research area
which makes society a better one by giving nice prediction
techniques for academician, teachers and students. The papers
discussed in this survey will give the detailed thought of
educational data mining and core paths of EDM. The
techniques and tools discussed in this survey will provide a
clear cut idea to the young educational data mining researchers
to carry out their work in this field. Also, this research work
carried out on the areas which make data mining process with
educational data mining in a batter way. Finally, it is confirmed
that most of the classification algorithms perform in a better
way of understating the current trends of EDM by the students
as well as academicians.
References
[1] Sheard. J, Ceddia. J, Hurst. J & Tuovinen. J, “Inferring
student learning behaviour from website interactions: A
usage analysis”, Journal of Education and Information
Technologies, 2003, Vol: 8(3), pp. 245-266.
[2] Sheard. J, Ceddia. J, Hurst. J & Tuovinen. J, “Determining
website usage time from interactions: Data preparation and
analysis”, Journal of Educational Technology Systems,
2003, Vol: 32(1), pp.101-121.
[3] Muehlenbrock, Martin.,“Automatic action analysis in an
interactive learning environment”, Proceedings of the 12th
International Conference on Artificial Intelligence in
Education. 2005, pp.452-455.
[4] Anuradha. C and T. Velmurugan, “A Data Mining based
Survey on Student Performance Evaluation System”, IEEE

ISSN: 2278-2419
171
Int. Conference on Computational Intelligence and
Computing Research, 2014, pp. 452-455.
[5] Zaiane. O, & Luo. J, “Web usage mining for a better web-
based learning environment”, In Proceedings of
conference on advanced technology for education, Banff,
Alberta, 2001, pp. 60–64.
[6] Silva, D., & Vieira, M. “Using data warehouse and data
mining resources for ongoing assessment in distance
learning”. In IEEE international conference on advanced
learning technologies, Kazan, Russia, 2002, pp. 40–45.
[7] Shen, Ruimin, Fan Yang, and Peng Han. “Data analysis
center based on e-learning platform”, The Internet
Challenge: Technology and Applications. Springer
Netherlands, 2002, pp.19-28.
[8] Cristobal Romero, Sebastian Ventura, Paul de Bra &
Carlos de Castro, “Discovering prediction rules in AHA!
Courses”, International Conference on User Modeling,
Springer Berlin Heidelberg, 2003, pp.25-34.
[9] Tane, Julien, Christoph Schmitz, and Gerd Stumme,
“Semantic resource management for the web: an e-
learning application”, Proceedings of the 13th
international World Wide Web conference on Alternate
track papers & posters, ACM, 2004, pp. 1-10.
[10]Merceron, Agathe, and Kalina Yacef, “Tada-ed for
educational data mining”, Interactive multimedia
electronic journal of computer-enhanced learning, 2005,
Vol:7(1), pp: 267-287.
[11]Vanzin, Mariangela, Karin Becker, and Duncan Dubugras
Alcoba Ruiz , “Ontology-based filtering mechanisms for
web usage patterns retrieval”, International Conference on
Electronic Commerce and Web Technologies, Springer
Berlin Heidelberg, 2005, pp. 267-277.
[12]Avouris, N., Komis, V., Fiotakis, G., Margaritis, M. and
Voyiatzaki, E., “Logging of fingertip actions is not enough
for analysis of learning activities”, In 12th International
Conference on Artificial Intelligence in Education, AIED
05 Workshop1: Usage analysis in learning systems, 2005,
pp.1-8.
[13]Mazza. R , and Milani. C , “Exploring usage analysis in
learning systems: Gaining insights from visualizations”,
Workshop on usage analysis in learning systems at 12th
international conference on artificial intelligence in
education, 2005, pp. 65-72.
[14]Mostow. J, Beck. J, Cen. H, Cuneo. A, Gouvea. E, &
Heiner. C, “An educational data mining tool to browse
tutor-student interactions: Time will tell”, Proceedings of
the Workshop on Educational Data Mining, National
Conference on Artificial Intelligence. AAAI Press, 2005,
pp. 15-22.
[15]Damez. M, Marsala. C, Dang. T, & Bouchon-Meunier. B,
“Fuzzy decision tree for user modeling from human–
computer interactions”, In International conference on
human system learning: Who is in control?,2005, pp.287–
302.
[16]Bari. M, & Benzater. B, “Retrieving data from pdf
interactive multimedia productions”. In International
conference on human system learning: Who is in control?
,2005, pp.321–330.
[17]Qasem A. Al-Radaideh, Emad M. Al-Shawakfa, Mustafa
I. Al-Najjar, “Mining Student Data Using Decision Trees”,
The 2006 International Arab Conference on Information
Technology, Jordan,2006, pp.1-5.
[18]Romero. C, Alcala-Fdez. J, Sanchez. L, Garcia. S, del
Jesus. M. J, Ventura. S, Garrell. J. M,& Fernandez. J,
“KEEL: a software tool to assess evolutionary algorithms
for data mining problems.” Soft Computing, 2009,
Vol:13(3), pp. 307-318.
[19]Marquez-Vera. C, ROMERO. C, "Predicting School
Failure Using Data Mining", Educational Data
Mining, 2010,2011.
[20]Dragan Gasevic, “Let’s not forget: Learning analytics
areabout learning", Association for Educational
Communications and Technology, 2015, Vol: 59(1), pp.
64-71.
[21]Romero. C, Ventura. S, Espejo. P. G, & Hervas. C, “Data
mining algorithms to classify students”, Educational Data
Mining 2007, 2007, pp:1-10.
[22]Ramaswami. M, and R. Bhaskaran, “A CHAID based
performance prediction model in educational data
mining”, 2010, arXiv preprint arXiv:1002.1144.
[23]Edin Osmanbegovic & Mirza Suljic , “DATA MINING
APPROACH FOR PREDICTING STUDENT
PERFORMANCE”, Journal of Economics and Business,
2012, Vol: 10(1), pp. 3-12.
[24]Surjeet Kumar Yadav, Saurabh Pal, “Data Mining: A
Prediction for Performance Improvement of Engineering
Students using Classification”, World of Computer
Science and Information Technology Journal (WCSIT),
2012, Vol: 2(2), pp. 51-56.
[25]Dorina Kabakchieva, “Predicting Student Performance by
Using Data Mining Methods for Classification”,
Cybernetics and Information Technologies, 2013, Vol:
13(1), pp.61-72.
[26]Abeer Badr El Din Ahmed and Ibrahim Sayed Elaraby,
“Data Mining: A prediction for Student's Performance
Using Classification Method”, World Journal of Computer
Application and Technology, 2014, Vol: 2(2), pp. 43-47.
[27]Xing Wanli , Guo Rui, Petakovic Eva & Goggins Sean,
“Participation-based student final performance prediction
model through interpretable Genetic Programming:
Integrating learning analytics, Educational data mining
and theory”, Computers in Human Behavior, 2015, Vol:
47, pp. 168–181.
[28]Ashwin Satyanarayana, Gayathri Ravichandran, "Mining
Student data by Ensemble Classification and Clustering
for Profiling and Prediction of Student Academic
Performance", 2016 ASEE Mid-Atlantic Section
Conference, 2016.

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