Clustering Students of Computer in Terms of Level of Programming

International Journal of Computer Applications Technology and Research
Volume 6–Issue 3, 168-171, 2017, ISSN:-2319–8656
www.ijcat.com 168
Clustering Students of Computer in Terms of Level of
Programming
Shirin Esmailbegi Kermani
Department of Computer,Kerman Branch,Islamic
Azad University, Kerman,Iran
Abbas Rezaie
Department of Computer,Kerman Branch,Islamic
Azad University, Kerman,Iran
Abstract: Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key
domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student
in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier
approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the
second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review
feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of
each faculty.
Keywords: clustering algorithm, Data mining, Educational data mining, classification
1. INTRODUCTION
1. Data Mining is the process of analyzing data from
different perspectives and summarizing the result as
useful information. Educational Data mining is an
emerging research field in data mining and continuously
growing with a boom. Incalculable data mining techniques
have been applied to the variety of educational data [15].
2. For the better decision making in the learning
environment, EDM consists of four phases: Data
Preprocessing Data Validation Data Prediction Decision
making process.
3. This study investigates the education domain for relative
evaluation of faculty performance on the basis of two
parameters student feedback and result in a course taught
by faculty. Classification technique of data mining is
applied to fulfill the objective. The goal of classification
technique is to place an object into a category based on the
characteristics of the object.
4. The whole paper is organized as: In section 1 Introduction
about DM, EDM and some idea of relative problem is
mentioned. In section 2 overview of related work in which
educational data mining on student domain and faculty
domain is discussed from previous research. In section 3
proposed brings up where educational data mining is
presented in faculty domain. The proposed framework is
explained in section 4.
For the better decision making in the learning environment,
EDM consists of four phases:
Data Pre-processing
Data Validation
Data Prediction
Decision making process
2. LITERATURE REVIEW
2.2 A Comprehensive Study of Educational Data Mining:
Author Jasvinder Kumar suggests that, educational data mining
is a new discipline in research community that applies various
tools and techniques of data mining (DM) to explore data in the
field of education [2]. This discipline helps to learn and develop
models for the growth of education environment. It provides
decision makers a better understanding of student learning and
the environment setting in as of EDM. It also highlights the
opportunities for future research. Educational data Mining
(EDM) has been evolved as multidisciplinary scientific
learning area, rich in data, methods, tools and techniques used
to provide better learning environment for educational users in
educational context. This paper integrates all the modules of
EDM required to facilitate the objectives of educational
research. Lastly it shows that, there are many more research
topics that exist in this domain.[2] Utilization of data mining
techniques within education environment requires a joint effort
by the ICT specialists, educationists and the learners.
2.3 Educational Data Mining: a Case Study-
Author Kalina suggest in this paper, Author show how using
data mining algorithms can help discovering pedagogically
relevant knowledge contained in databases obtained from Web-
based educational systems. These findings can be used both to
help teachers with managing their class, understand their
students learning and reflect on their teaching and to support
learner reflection and provide proactive feedback to learners. In
this paper, Author has shown how the discovery of different
patterns through different data mining algorithms and
visualization techniques suggests to us a simple pedagogical
policy. Data exploration focused on the number of attempted
exercises combined with classification led us to identify

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students at risk, those who have not trained enough. Clustering
and cluster visualization led us to identify a particular behavior
among failing students, when students try out the logic rules of
the pop-up menu of the tool. As in [5], a timely and appropriate
warning to students at risk could help preventing failing in the
final exam. Therefore it seems to us that data mining has a lot
of potential for education, and can bring a lot of benefits in the
form of sensible, easy to implement pedagogical policies as
above. The way Author have performed clustering may seem
rough, as only few variables, namely the number and type of
mistakes, the number of exercises have been used to cluster
students in homogeneous groups. This is due to our particular
data. All exercises are about formal proofs. Even if they differ
in their difficulty, they do not fundamentally differ in the
concepts students have to grasp. Author have discovered a
behavior rather than particular abilities. In a different context,
clustering students to find homogeneous groups regarding
skills should take into account answers to a particular set of
exercises. Currently, Author are doing research work along
these lines.
2.4 Extraction of rules based on students questionnaires:
Author Manolis Chalaris; suggest there are many
students in the Greek Higher Education that are still “lingering”
in their Departments beyond the six years. The length of studies
beyond 6 years has not been justified, and this study focuses on
this problem. Author also studies another problem: The
percentage of graduates scoring about 8.5/10 or more is
extremely low [8]. Association rules mining is a well known
data analysis method for extracting associations between data
in a wide range of different fields. In this paper, Author focus
on the generation of the appropriate association rules based on
students’ questionnaires in Higher Education. A sample of
50.000 questionnaires was filled by 10.000 students in the TEI
of Athens. Various interesting rules could be extracted related
to learning goals, practices, years required for graduation, etc.
These rules and clustering techniques could be used for solving
the problem of the students that are still “lingering”, and the
problem of the low “scoring” of the graduates. In this section,
Author use the simple example of the section 3 in order to
present the potential impact of these results in Higher
Education, and how the described technique could be
competitive.
2.5 Predicting Students academic Performance using
Artificial Neural Network: A Case Study of an
Engineering Course:
Author V.O. Oladokun suggest that , The observed poor quality
of graduates of some Nigerian Universities in recent times has
been partly traced to inadequacies of the National University
Admission Examination System. In this study an Artificial
Neural Network (ANN) model, for predicting the likely
performance of a candidate being considered for admission into
the university was developed and tested. Various factors that
may likely influence the performance of a student were
identified. Such factors as ordinary level subjects scores and
subjects’ combination, matriculation examination scores, age
on admission, parental background, types and location of
secondary school attended and gender, among others, were then
used as input variables for the ANN model. A model based on
the Multilayer Preceptor Topology was developed and trained
using data spanning five generations of graduates from an
Engineering Department of University of Ibadan, Nigeria’s
first University. Test data evaluation shows that the ANN
model is able to correctly predict the performance of more than
70% of prospective students. This study has shown the
potential of the artificial neural network for enhancing the
effectiveness of a university admission system. The model was
developed based on some selected input variables from the pre
admission data of five different sets of university graduates. It
achieved an accuracy of over 74%, which shows the potential
efficacy of Artificial Neural Network as a prediction tool and a
selection criterion for candidates seeking admission into a
university. One limitation of this model stems from the fact that
not all the relevant performance influencing factors are
obtainable from the pre-admission record forms filled by the
students. A model incorporating the use of results from a
carefully designed oral interview administered to the students
may likely be an improvement over the present model. Also the
extension this research to non-engineering departments is
recommended. The current admissions system should be
reviewed in order to improve the standard of candidates being
admitted into the institution. A more adequate ANN may be
very useful for such an exercise.[11]
2.6 Survey on Decision Tree Classification algorithms for
the Evaluation of Student Performance:
Author Anjou Rathee, suggest that Now days, the amount of
data stored in educational database is increasing rapidly. These
databases contain hidden information for improvement of
student’s performance. Classification of data objects is a data
mining and knowledge management technique used in
grouping similar data objects together. There are many
classification algorithms available in literature but decision tree
is the most commonly used because of its ease of
implementation and easier to understand compared to other
classification algorithms. The ID3, C4.5 and CART decision
tree algorithms has been applied on the data of students to
predict their performance. In this paper, all the algorithms are
explained one by one. Performance and results are compared of
all algorithms and evaluation is done by already existing
datasets. All the algorithms have a satisfactory performance but
accuracy is more witnessed in case of C4.5 algorithm. In this
paper, three existing decision tree algorithms have been applied
on the educational data for predicting the student’s
performance in examination. All the algorithms are applied on
student’s internal assessment data to predict their performance
in the final exam. The efficiency of various decision tree
algorithms can be analyzed based on their accuracy and time
taken to derive the tree. The predictions obtained from the
system have helped the tutor to identify the weak students and
improve their performance. [14] C4.5 is the best algorithm
among all the three because it provides better accuracy and
efficiency than the other algorithms. Still effective algorithms
for decision tree should be developed.

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2.7 Survey on Decision Tree Classification algorithms for
the Evaluation of Student Performance:
Author Anjou Rathee, suggest that Now days, the amount of
data stored in educational database is increasing rapidly. These
databases contain hidden information for improvement of
student’s performance. Classification of data objects is a data
mining and knowledge management technique used in
grouping similar data objects together. There are many
classification algorithms available in literature but decision tree
is the most commonly used because of its ease of
implementation and easier to understand compared to other
classification algorithms. The ID3, C4.5 and CART decision
tree algorithms has been applied on the data of students to
predict their performance. In this paper, all the algorithms are
explained one by one. Performance and results are compared of
all algorithms and evaluation is done by already existing
datasets. All the algorithms has a satisfactory performance but
accuracy is more witnessed in case of C4.5 algorithm. In this
paper, three existing decision tree algorithms have been applied
on the educational data for predicting the student’s
performance in examination. All the algorithms are applied on
student’s internal assessment data to predict their performance
in the final exam. The efficiency of various decision tree
algorithms can be analyzed based on their accuracy and time
taken to derive the tree. The predictions obtained from the
system have helped the tutor to identify the weak students and
improve their performance.[14] C4.5 is the best algorithm
among all the three because it provides better accuracy and
efficiency than the other algorithms. Still effective algorithms
for decision tree should be developed.
a. Rule Mining Framework for Students Performance
Evaluation:
Author P. Ajith, B. Tejaswi, M.S.S.Sai suggest that Academic
Data Mining used many techniques such as Decision Trees,
Neural Networks, Naïve Bayes, K- Nearest neighbor, and many
others. Using these techniques many kinds of knowledge can
be discovered such as association rules, classifications and
clustering. The discovered knowledge can be used for
prediction and analysis purposes of student patterns. Prior
approaches used decision tree classifications optimized with
ID3 algorithms to obtain such patterns. Among sets of items in
transaction databases, Association Rules aims at discovering
implicative tendencies that can be valuable information for the
decision-maker which is absent in tree based classifications. So
we propose a new interactive approach to prune and filter
discovered rules. First, Author propose to integrate user
knowledge in the post processing task. Second, Author propose
a Rule Schema formalism extending the specifications to obtain
association rules from knowledge base. Furthermore, an
interactive framework is designed to assist the user throughout
the analyzing task. Applying our new approach to discover the
likelihood of students deviations / requiring special attention is
organized and efficient providing more insight by considering
more information.[12]
3. PROPOSED WORK:
Similar to this, the Data Mining technique can also help the
education industry to achieve better goals. Educational
institutes can apply data mining techniques for analyzing
student performance, faculty performances and the overall
performance of institutes. Institutes can study comparative
performances with other institutes to compete with and lead
other institutes and improve their ratings.
Data Collection
Data Preprocessing
Refined Data
KNN Algorithm
4 Parameters used(Calculate result for each parameter)
Sum of all parameters
Rule based classification algorithm
Analysis of all subject and class of each faculty result
Figure 1 Flow of System
So, By using multiple classifier approach and cure clustering
algorithm the performance of the system is better than the
existing one. Here KNN is used for mapping purpose. And rule
based algorithm is used for deciding the rules on the data.
Following figure shows the flow of the proposed system that
will be implemented.
4. CONCLUSIONS
The overall performance of multiple classifier approach is
better than the single classifier approach. In the second step of
multiple classifier approach we Rule-based classification have
been used where the authors define their own rules for
classification, which make the difference from single classifier
approach. In single classifier approach we sum up the both
parameters scores which may restrict the performance of this
approach. Future work can enhance the performance of the
approach by considering some more parameters according to
the Requirement of the organization. Secondly, in the present
work, author has taken only Computer Science and engineering
department faculties. Future work can be enhanced for all
branches of the college for overall performance enhancement
of the college.
5. REFERENCES
[1] N.Aggrawal et.al,"Analysis the effect of data mmmg
techniques on Database" Elsevier, Advances in Engineering
Software 47 (2012) 164-169.
[2] N.Ahmad and S.Shamsuddin," A Comparative Analysis of
Mining Techniques for Automatic Detection of Students
Learning Styles," IEEE, 978-1-4244-8136-1
(2010).
[3] Z.Abdullah et.al," Extracting highly positive association
rules from students enrollment data", Elsevier, Procedia -
Social and Behavioral Science 28 (2011) 107-111.
[4] K.Bunkar et.al," Data Mining: Prediction for

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Performance Improvement of Graduation Student using
Classification", IEEE, 978-4673-1989-8 (2012).
[5] B.Bidgoli et.al,"Predicting Student Performance: An
Application of Data Mining Methods with an
Educational Web-Based System", 33rd ASEE/IEEE
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[6] N.Delavari et.al,"A New Model for Using Data Mining
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[7] E.Garcia and P.Mora,"Model Prediction of academic
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[8] S.Liao et.al,"Data Mining techniques and applicationsA
decade review from 2000-2011",Elsevier, Expert System with
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[9] LiPing and DuanFu,"The Disposal of Incomplete
Classification Data in Teaching Evaulation System",
IEEE, 2009 Third International Symposium on Intelligent
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7695-3859-4
[10] X. Long and y.wu,"Application of decision tree in student
achievement evaluation", IEEE, 2012
International Conference on Computer Science and
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[11] S. Mardikyan and B. Badur,"Analysis Teaching
Performance of Instructors Using Data Mining
Techniques" ,Information in Education, 2011, Vol. 1 0,
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[12] P.Qingxian et.al,"data milllllg and application of
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[13] Al.Radaishel et.al,"Mining student data using decision
tree", in the proceeding of the International Arab conference on
information technology, Jordan, 2006.
[14] C.Romero and S.Ventura", Educational data mining: A
survey from 1995 to 2005". Elsevier, Expert System with
Application 33 (2007) 135-146.
[15] C.Singh et.al,"Extraction and Analysis of Faculty
Performance of Management Discipline from Student
Feedback Using Clustering and Association rule mining
techniques", IEEE, 978-4244-8679-3 (2011).
[16] K. Shaban and M. Abdulwahed,"Research-Based
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[17] H.Wang and R.Zhong,"Application of Association Rules
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