Fd33935939

Kartik N. Shah, Srinivasulu Kothuru, S. Vairamuthu / International Journal of Engineering
Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.935-939
935 | P a g e
Clustering students’ based on previous academic performance
Kartik N. Shah1
, Srinivasulu Kothuru2
and S. Vairamuthu3
School of Computing Science and Engineering, VIT University
Abstract
Educational data mining is very popular
research area for studying the behavior of
students based upon their past performance. As
Education is very basic need, which must be
given to all, the study of student behavior plays a
vital role. Grouping students on the basis of their
performance, we can make a good team for any
competitions to represent from institute or
university. Also we can make some more focus on
the students who are having bad performance by
giving some extra lectures and motivating them
for better study. In this paper, we will cluster the
similar behavior students based upon their past
academic performance. We are using Similarity
measure as Canberra Distance for clustering
same type of students. We will use VIT university
data for analyzing performance.
Keywords: Educational Data Mining, Clustering
Students, Grouping Method, Students behavior.
1. Introduction
Educational Data Mining (EDM) is the
study of raw data of educational institutes and
universities to get the needed information that can
be used by educational software developers,
students, teachers, other educational researchers and
parents. Moreover, we can say that machine-
learning, statistical and data-mining (DM)
algorithms are part of EDM to analyze the various
types of educational data [3]. EDM is concerned
with understanding the student behavior and then
create the methods for understanding the other
students behavior for creating techniques or
algorithms which can help for modifying existing
system by which students can understand well [2].
Web Education or E-Learning is the era in which we
can use Internet to give education to students. Web
Education has created new era of learning and
understanding learning pattern of students [4]. This
information is gold mine for educational mining [7].
Prediction of student‟s performance is very useful
part of Educational Data Mining. We can use this
predicted data in many different contexts in
universities. We can identify exceptional students
for scholarships or stipend. It is essential part for
admission of Post Graduate students. Also we can
find the students which can behave poorly in the
exams. These predictions will make the
management of universities to take remedial
measures at early stage which enables institute or
university to produce excellent students. Now a day,
many universities follow web based education. In
this case, it is useful to classify a student‟s
performance. We can use Data Mining (DM)
techniques to achieve these objectives [6].
The supervised classification algorithm
needs a training sample for each class, which
contains the information of known collection of data
points of class of interest. So, classification process
is based on closer the data point is compared to the
training sample. The training samples are
representative of the known classes of interest to the
analyst. The classification methods which relay on
use of training samples are known as supervised
classification methods. These methods are basically
used for image classification.
The 3 steps involved in supervised classification are:
1. Data training stage: The analyst find the
interested area which is already known to
him and this known information is used to
classify the other unknown data based upon
this training data. Best match with training
sample is consider in the case of multiple
match.
2. The Classification stage: This is the main
stage in which, classification is carried out
based upon available training data. All the
unknown classes are labeled based upon
the training data available for classification.
3. Final Output Stage: The final stage is
output stage which is concerned with using
of data available from classification stage
in various applications.
2. Related Work
From the past, we had measure the
students‟ performance by the number of courses
they have registered and Grand Point Averages
(GPA) or Credit Grand Point Averages (CGPA)
they have secured. But, that traditional method that
we had used in past, was not guaranteeing that the
students have achieved the qualifications required
for the next era. Now a day, a student‟s performance
has been evaluated on the basis of the marks given
by faculty during written examination. Most of the
universities follow the mark based approach in
which relative grading is given to all students
(marks between 1 to 100) and then categorized in
the grades (A, B, C, D, E, F or N) and then nominal
scores (1, 2, 3. . .10). And finally linguistic terms
like „Fail‟ or „Pass‟, etc. In this study, a weighted
sum of assessment style (i.e each exam have
different weight) is used to compute the numerical

936 | P a g e
score of each student based upon the university
rules: Quiz (Q), Common Assessment Test (CAT)
or Internal (I), Final (F), Performance Appraisals
(P). The total of all of the above measures is treated
as Final result of the student.
For Intelligent Tutoring System, we can
use constraint relaxations and sequential pattern
mining to automatically acquire the knowledge [8].
We can predict the student‟s grade based upon
similarity measures called Sum of Absolute
Difference and Sum of Squared Difference and then
we can assign grades for the system of university
which follow the Relative grading scheme [1]. To
label the students‟ behavior, we can use text
replays.It is more precise and faster [9]. There can
be many different objectives for classifying students
based on their characteristics which are 2 steps
classification process. In which, first step is to study
single classifier accuracy on data and then choose
the best and gather this information with weak
classifier for voting method [11], In Statistical
classification, individual result is taken and grouped
into similar results and then majority is considered
for classification [12]. Some of the statistical
algorithms are linear discriminant analysis, kernel
and k nearest neighbors [5]. The decision tree is a
set of conditions which is organized in a hierarchical
structure [13]. It is a predictive model in which to
classify an instance, we need to follow the path in
the tree till leaf node. If we are able to reach leaf
node then we can classify it on that class. We can
convert a decision tree to a set of classification
rules. Most well-known decision tree algorithms are
C4.5 [13]. Rule Induction - it is an area of machine
learning in which, from a set of observations, IF-
THEN production rules are extracted [14]. In rule
induction, operators correspond to generalization
and specialization operations and state corresponds
to a candidate rule that transform one candidate rule
into another. Examples of rule induction algorithms
are CN2 [15], Supervised Inductive Algorithm
(SIA) [16], a genetic algorithm using real-valued
genes (Corcoran) [17] and a Grammar-based genetic
programming algorithm (GGP) [18]. We can use
Neural Networks also for rule induction. Examples
of neural network algorithms are multilayer
perceptron (with conjugate gradient-based training)
[19], a radial basis function neural network (RBFN)
[20], incremental RBFN [24], decremental RBFN
[20], a hybrid Genetic Algorithm Neural Network
(GANN) [21] and Neural Network Evolutionary
Programming (NNEP) [22].
3. Proposed Framework
Different measures of similarity or distance
are convenient for many types of analysis. There are
many techniques available to cluster the similar type
of students. All grading techniques and
classification techniques can be extended for
clustering of students. When we classify the
students based upon any algorithm, we can conclude
that the students who are coming in same class
having some type of similar behavior or
characteristics. So, we can make a group of students
who are coming in same class and make a single
cluster of that group based upon the criteria which is
used for classification purpose. We will cluster
students‟ records based upon their academic
performance of the previous data. Some of the
distance and similarity measures for numerical data
are listed below. We will use this distance measure
to cluster the students based upon their academic
performance.
Distance measure between KNOWN(p, q, r) and
UNKNOWN(xj, yj, zj)
Euclidean
Distance  
j
jjj zrAbsyqAbsxpAbs ])()()([ 222
Manhatta
n
Distance
 
j
jjj zrAbsyqAbsxpAbs )]()()([
Squared
Euclidean
Distance
 
j
jjj zrAbsyqAbsxpAbs ])()()([ 222
Bray
Curtis
Distance
 

j jjj
jjj
zrAbsyqAbsxpAbs
zrAbsyqAbsxpAbs
)]()()([
)]()()([
Canberra
Distance
j
j
j
j
j j
j
zr
zrAbs
yq
yqAbs
xp
xpAbs









)()()(
Chessboar
d
Distance
)](),(),([ jjj zrAbsyqAbsxpAbsMAX 
Cosine
Distance
222222
1
jjj
jjj
zyxrqp
rzqypx



Table1 Similarity measures for numerical data
Table1 shows similarity and distance
measures are useful for numerical data. There are
many more measures available for finding similarity
like Normalized Squared Euclidean Distance,
Correlation Distance etc. We can use 1 of the
techniques and then apply classification process to
create the cluster of similar students. Now days,
every university follow their own evaluation
patterns based upon that we can modify our
approach to calculate the similarity. We will take
Canberra Distance as similarity measure and cluster
the students based upon their marks obtained in
CAT I, CAT II and Quiz. We will study this
analysis for VIT University‟s students‟ records. In
case, if we want to apply the same technique for

937 | P a g e
different university then we can add details in
implementation of this technique. We can extent it
for analysis of students of final year based upon the
previous years. Consider the following details of the
students.
CAT1 CAT2 QUIZ REG_NO
34 36 8 1
14.3 29 9 2
44.5 47 13 3
40 45 14 4
41 46 13 5
45.5 46 15 6
37.5 43 12 7
28.5 43 10 8
38.5 45 13 9
28.5 40 6 10
40 47 14 11
35 40 9 12
19 30 8 13
43.5 46.5 13 14
42.5 44.5 13 15
36 43 12 16
40 40 14 17
41.5 45 14 18
31.5 40 8 19
42 47 13 20
36 38 13 21
42.5 46 14 22
42 33.5 9 23
30 35 13 24
29 30.5 9 25
20 25 7 26
35 41 14 27
38 40 13 28
41 47 15 29
40.5 46 14 30
47.5 43 15 31
25 40 6 32
0 0 0 33
42 41 12 34
28 29 8 35
Table2 Students‟ Records
After applying above Canberra Distance
algorithm, we can cluster the Table2 data. After
clustering, we will able to know that which students
come in which category. As every universities
follow different evaluation patterns, for VIT
University, CAT I and CAT II is having 15% weight
for each. They conduct 3 quiz and each having 5%
of weight. In above records, we have added all 3
quiz marks for simplicity. Then we will calculate the
marks based upon the weight of the exam and then
all details are together applied to algorithm. We will
give reference detail to cluster the data based upon
which students will be grouped. We have used
following groups to classify students.
Group „A‟ CAT I- 45, CAT II- 45
and Quiz – 15
Group „B‟ CAT I- 40, CAT II- 40
and Quiz – 13
Group „C‟ CAT I- 34, CAT II- 34
and Quiz - 9
Group „D‟ CAT I- 28, CAT II- 28
and Quiz - 8
Unusual Behavior or
Failure
Remaining
Table3 Reference marks for clustering
After applying the algorithm, we can get following
clusters. Table4 shows the details about clusters
after applying algorithms.
Group
„A‟
3,4,6,11,14,15,18,22,29,30,31
Group
„B‟
4,5,7,9,11,14,15,17,18,20,21,27,28,30,3
4
Group
„C‟
1,12,23
Group
„D‟
25,35
Unusual
Behavio
r or
Failure
2,8,10,13,16,19,24,26,28,32,33
Table4 Output Clusters
4. Implementation
We can implement this algorithm using
.Net technology and C# language. It will take all
students‟ records and Reference marks (criteria for
clustering) as input and will provide the Reg. no of
students who are coming in that cluster. Following
figure1 shows the implementation details.
Figure1 Implementation
5. Analysis
After applying algorithm, we can cluster
students based upon criteria shown above. We can
map in line chart as follows.

938 | P a g e
For more precision, we can go for normalization to
get similar type of points. The line graph can be
shown as below. It shows that while we consider the
individual weight of the exams then also it can be
able to cluster proper way. From Line chart it is
clear that all the students are coming in same
clusters. So, Canberra Distance algorithm can group
the students based upon similar academic
performance in previous exams.
6. Future Work
In this paper, we have discussed marks as a
measure for clustering the students. There are many
other behaviors by which we can cluster the
students. Some of the measures are based upon
practical knowledge, class behavior, talent in
particular field, family background. We have not
considered any of the measures as it is very difficult
to understand each student. We can enhance the
clustering techniques based upon the above
mentioned criteria.
7. Conclusion
From the above analysis, we can say that
we can make cluster of students based upon
Canberra Distance Similarity and Distance measure.
In this study, we have use VIT University data as a
reference to check validity of results. We can extend
this algorithm for any university which follow
numerical evaluation students (i.e based upon
marks). Most of the universities follow the marks
pattern and then marks are converted into grades.
So, this analysis can be applied on most of the
university‟s students‟ records.
8. Acknowledgement
The authors would like to thank the School of
Computer Science and Engineering, VIT University,
for giving them the opportunity to carry out this
project and also for providing them with the
requisite resources and infrastructure for carrying
out the research.
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