Predicting students performance using classification techniques in data mining

INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)
ISSN-2455-099X,
Volume 2, Issue 10 October 2016
IJTC201610001 www. ijtc.org 489
Predicting Students’ Performance Using
Classification Techniques in Data Mining
Mukesh Kumar1
, Prof (Dr.) A. J. Singh
1
(PhD Scholar, CS Department, HPU-Shimla HP, mukesh.kumarphd2014@gmail.com)
2
(Professor, CS Department, HPU-Shimla HP, aj_singh@yahoo.uk.in)
Abstract: Role of education is very critical for the development of any country. So it is the responsibility of each and every person
to do something for the betterment of education. Taking this fact into consideration we start working on the education system.
Education system ranging from basic to higher education. Now a day education system generates a lots of data related to student.
If we cannot analyze that data properly then that data is useless. With the help of data mining techniques we can find the hidden
information from the data collected for the different educational setting. With the help of that information we can review our
educational process or make improvement in our education system. Here in this article we are considering a case of an engineering
college student and try to predict the final result in advance. The result of the prediction provides timely help to those students
who are on risk of failure in the final examination. There are different techniques of data mining are available and we are using
J48, RandomForest, and ADTree to predict the performance of the student in their final examination. On the basis of this
predication we can make a decision whether the student will be promoted to next year or not. We the help of the result we can
improve the performance of the student who are on risk of fail or promoted. After the declaration of the final result of the student,
result is fed into the system and hence the result will analysed for the next semester. The comparative result shows that, prediction
help in the improvement of overall result of the weaker students.
Keywords- Data Mining, EDM, Decision Tree Algorithm, J48, RandomForest, ADTree.
I. INTRODUCTION
Data mining is a one of the most important field to study.
Data mining concepts, techniques and algorithms are
applied into different fields like education, medicine,
business, retail management, hospital and hospitality
industries etc. With the help of data mining techniques we
can predict the future of any business or make improvement
in it. We are learning about different data mining techniques
in our study like association rule mining, clustering,
classification etc. There are two types of data mining
techniques are available like supervised and unsupervised
learning. In supervised learning we are making model first
and then apply algorithm on that data set [2]. While in
unsupervised learning we are applying algorithm first and
then make model for analysis.
Now we are just discussing about the concept of
educational data mining. As we already mentioned that we
are choosing educational field because education is one of
the most important facture for the development of the
nation.
Data mining is used to find hidden information for the data
set. So by analysing the educational data we want to find
some important information which is helpful for the further
improvement in education. Which data mining algorithms
are applied on dataset is depend upon the types of dataset
and what you want to find form it. We have studied
different algorithm which are applied on the different data
set. Data mining algorithm like neural network, Naïve
Bayes, K- Nearest neighbour, Decision tree, classification
and clustering are applied on the educational dataset [3].
With the help of data mining techniques we can predict,
classify or cluster student according to their performance in
their education. Examination marks play most important
role in the life of a student. If we can predict the result of
the student before examination then we can put some extra
effort to improve the performance of that student in their
final examination. You can say with the help of predication
we can provide timely help to the student who are at risk of
education failure.
II. PROBLEM RELATED TO THE HIGHER
EDUCATION SYSTEM
At present most of the institutions or organisation in India
are facing the problem of student admission. Most of the
engineering college or university are face problem of low
admission in engineering stream. There are lot of reason for
that like less placement record, less infrastructures; syllabus
not updated, less qualified staff, poor teaching
methodology. So to increase admission in the college we
need to provide these basic needs of the time. Without
providing these features no college will sustain in the near
future and face the problem of failure [1]. So to remain in
the competition with other college they need to provide
extra to the student which helps them a lot in their study.
Educational data mining is the solution of the entire
problem because with the help of educational data mining
we can analysis the all the data which are produced by the
educational setting.
With the help of analysis we can predict the result of the
student, dropout of any student, placement of the student,
behaviour of the student etc. If any student having a risk of
failure and we can predict that risk in advance then we can
provide timely help to that student. Education data mining
techniques can be applied on any types of educational data.
There are lots of data mining techniques which are applied
on educational data like classification and clustering
algorithm.
In this article we can consider the case of an engineering
college in which we want to predict the result of the student
in their final examination of next semester. For that purpose
we can collect the information of the first year student with
different attribute like branch, sex, category, father
occupation, Mother occupation etc [1]. With the help of
data mining we can predict the result of the student in
advance and then provide the student timely help who are
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on the risk of failure. The motive behind this article is to
help different educational institutional administrator by
creating a model which provide some helps to student and
hence they will improve their result in future. We are taking
different steps to achieve these motives in mind are listed
below:
1. Choose the different source by which you can
collect the information related to the student with
selected attributes
2. By collecting these data select the best attribute
which helps for the prediction of the student result,
their behaviour and academic achievement.
3. Select the best data mining algorithm for your
dataset which give the result with great accuracy.
We are applying different classification data
mining algorithm for our analysis.
4. At the end, validate the presented model for
different student of engineering institution and
university of India.
III. DIFFERENT SOFTWRAES AVAILABLE FOR THE
DATA MINING ANALYSIS
At present scenario, data is one of the most important in
today’s world. Because by analysing that data we can find
some information which will be helpful in future. We have
different types of data mining software for analysis. Every
organisation deals with different types of data in real life
like data related to education, business, sales, marketing,
hospital, hospitality etc. Software’s has their own features
and properties and it depend on the data that which software
is suitable for their analysis [6]. Here we present ten most
important tools used for the data analysis in tabular form
below:
Table 1: List of different software available for the purpose
of data mining analysis
S.No Software Language
used
Developed State
1 RapidMiner Java Technical
University of
Dortmund
2 SAS Data
Mining
C North Carolina State
University
3 WEKA Java University of
Waikato, New
Zealand
4 R-Software C, Fortran,
R
University of
Auckland, New
Zealand
5 Orange Python University of
Ljubljana
6 KNIME Java University of
Konstanz
7 NLTK Python University of
Pennsylvania
8 DataMelt Jython,
Groovy
jWork.ORG
community
9 Pentaho Java Hitachi Data
Systems
10 Tanagra DELPHI 6 Lumière University
Lyon, France
After reading different research paper about educational
data mining we find that RapidMiner and WEKA are the
mostly used software for the analysis purpose. So form the
above discussion we are taken WEKA software tool for our
analysis purpose. WEKA is an Open source software and
easily available for the user under GNU public licence. We
can also implement our own algorithm on this software.
Most of the data mining algorithms are available in WEKA
software. WEKA is a complete package of different data
mining or machine learning algorithm. It support
classification, clustering, regression, association rule and
feature selection algorithm. It also able to shows you
various relationships between data sets, cluster,
visualization, predictive modelling and association rule
algorithms.
IV. CLASSIFICATION ALGORITHM TAKEN INTO
CONSIDERATION FOR ANALYSIS
We have different types of data mining algorithms are
available to make an analysis of our data like clustering,
classification, association rule mining. But which data
mining algorithm is suitable for your data is depend upon
what types of information your want to take and what types
of data set you have in your hand. Before selecting any
algorithm make sure that what types of information your
want to take from the dataset [7]. Every data mining model
is created with the help of a specific algorithm. We can
solve any data mining problem with best possible way by
using more than one algorithm. In this article we want to
make a prediction related to the final result of the student in
the coming semester. You will be the successful at data
mining field even if you are not very much familiar with the
inner working of the each algorithm. But it is important to
get the full understanding of the general features of the each
algorithms and their suitability with different dataset.
Data mining function may be off two types supervised and
unsupervised. Here according to our dataset fall into the
categories of supervised learning. Under supervised
learning we want to apply classification function. Because
we want to predict the result of the student according to the
predefined classes [4]. There are lots of algorithms fall into
the categories of classification like Decision tree, Naive
Bayes, Generalized Linear Models (GLM), Support Vector
Machine (SVM) etc. In this article we want to apply
Decision tree algorithms because it extracts predictive
information in human readable and easy to understandable
form. The rules generated are in the form of if-else
expressions and hence they leads to the prediction.
There are lot of Classification algorithm are available for
the analysis but we are applying only few of them for the
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analysis purpose like, J48, RandomForest REPTree,
LADTree and then compare their predictive result.
V. DATA COLLECTION AND PROCESSING PHASE
For predicting the academic progress of any student in their
early stage of higher education is very important. Because
early prediction of result always help a students to perform
well in the final examination. So for making any prediction
related to academic progress of the students we need lots of
parameters of students [1]. Prediction model takes lots of
parameter of student into consideration like personal,
family, psychological and social information for effective
prediction in their academics. Student’s educational
backgrounds are also taken into consideration while making
the prediction [8]. In student’s educational background
make contain data like grade, attendance, behaviour,
attitude toward study etc.
The dataset used for this article was taken from a reputed
engineering from under Punjab Technical University
Jalandhar. This university produced lots of engineer every
year. But the problem is that not all the registered student
gets their degree in time due to their backlogs. So in this
study we want to analysis the result of student in advance
and if the result is not in the favour of student then we can
provide timely help to them to improve their result in final
examination. For that analysis we need to collect some
information from the student and then apply data mining
algorithm on that dataset and hence predict the final result
of the student in advance.
Students have lots of attribute in their study period but we
need to collect only those attributes only which are helpful
for the prediction of the result. We are selecting only eleven
attributes which we think are one of the most important in
all the attributes. We was selecting student grade in high
school and senior secondary school, gender, family size,
family status, parents qualification, parents occupations and
previous semester result [1]. Most the information which
we collected is from the previous record of the students
which are most probably available with the concerned
institution. Most of the information was collect from the
database of the institution. All the selected attributes with
their response variables are listed in the table given below:
Table 2: Selected attributes of students considered for the
analysis purpose
Attributes Description
of the
attributes
Possible Values of the
attribute
Branch Students
Branch
{CS, ECE, ME, CE}
Gender Student
Gender
{Male, Female}
Grade_HS High School
Grade
{E – Above 90%, A – 81-
90%, B – 71-80%, C – 61-
70%, D – 51-60%, E – 40-
50%, F - < 40%}
Grade_SS Senior { E – Above 90%, A – 81-
Secondary
Grade
90%, B – 71-80%, C – 61-
70%, D – 51-60%, E – 40-
50%, F - < 40%}
Family_Size student’s
family size
{1, 2, 3, >3}
Family_Status Students
family status
{Joint, Individual}
Father_Qual Fathers
qualification
{no-education, elementary,
secondary, UG, PG, Ph.D.
NA}
Mother_Qual Mother’s
Qualification
{no-education, elementary,
secondary, UG, PG, Ph.D.
NA}
Father_Occ Father’s
Occupation
{Service, Business,
Agriculture, Retired, NA}
Mother_Occ Mother’s
Occupation
{House-wife (HW),
Service, Retired, NA}
Result Result in B.
Tech Ist
Year
{Pass, Promoted, Fail}
All the attributes selected above are taken into consideration
for the purpose of prediction with data mining techniques.
At the starting phase we start with twenty attributes but find
some attribute irrelevant to predict the result. Due to this
reason we just ignore those attributes for the final selection
of the dataset for the analysis.
VI. IMPLEMENTATION OF DATA MINING MODEL
FOR PREDICTION
As we already discuss that we will use WEKA tools for our
implementation. Because it is open source and maximum
classification algorithm are implemented on it. After
collecting all the information above put it in
SUDENTDATA.csv files. Before loading this file into the
WEKA explorer make sure that all the information is
correct according to the format of data collection. After
loading STUDENTDATA.csv file into explorer, apply
different classification algorithms on that data. There is
more the sixteen Decision tree algorithm are available for
the analysis [2]. In WEKA we are applying J48,
RandomForest, REPTree and LADTree for over analysis
here. After selecting these algorithms, next step is to select
10-fold cross-validation under “Test options” conditions.
There is no separate data set for the testing of the algorithm,
so it is necessary to get reasonable idea of accuracy for the
generated algorithm. The predictive result provide use
information that student will perform or not in the
examination.
VII. RESULTS AND DISCUSSION
We are working on four decision trees for the prediction of
final result from the student dataset by four machine
learning algorithms: the J48, RandomForest, REPTree and
LADTree respectively. These all are the important
algorithm for the prediction purpose.
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Fig 1: Tree generated by LADTree decision tree algorithm by WEKA tool.
Fig 2: Tree generated by REPTree decision tree algorithm by WEKA tool.
The table III shows the accuracy of J48, RandomForest,
REPTree and LADTree algorithms for classification applied
on the given educational data sets with 10-fold cross
validation under test options in Weka tool is given below:
Table III: Classifiers accuracy with Weka tool
Algorithm
Correctly
Classified Instance
Incorrectly
Classified
Instance
J48 62.6068% 37.3932%
RandomForest 51.4957% 48.5043%
REPTree 58.3333% 41.6667%
LADTree 57.906% 42.094%
Table III shows that a J48 technique used for classification
has highest accuracy of 62.6068% compared to other
decision tree techniques. Other algorithms are also having
great level of accuracy. After J48 algorithm RandomForest
algorithm also showed accuracy up to 58.3333%.
Table IV also shows the four decision tree algorithms for
classification that produce predictive models. We also put
all the information of the classification accuracy with their
class.
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Table IV: Detailed J48, RandomForest, REPTree and
LADTree algorithms accuracy by class
Algorithm Class TP Rate FP Rate
J48
Pass 1.000 1.000
Promoted 0.000 0.000
RandomForest
Pass 0.666 0.737
REPTree
Pass 0.901 0.949
LADTree
Pass 0.894 0.949
In table V we put the time complexity of various
classification algorithm techniques like J48, RandomForest
and REPTree and LADTree algorithms in seconds.
Table V: Execution time to build the J48, RandomForest
and REPTree and LADTree model
Algorithm Execution Time(Sec)
J48 0.00 Seconds
RandomForest 0.09 Seconds
REPTree 0.00 Seconds
LADTree 0.04 Seconds
VIII. CONCLUSION
Classification is one of the most interesting and important
topic of data mining techniques. Most of the researchers in
this field are using classification algorithm of data mining
for knowledge discovery from the dataset. There are lots of
classifications techniques are there in data mining like
Decision tree, Bayes, etc. We are here using decision tree
algorithm for prediction of the result. We are using one of
the best classification algorithms for the prediction of the
student result of the engineering student of first year
students. Form the above analysis we can find the TP ration
of the J48 and REPTree is 1.00 and 0.901 respectively. It
means that these to algorithm are almost identifies those
student who have possibility to pass the final examination.
The rest of the student who are not able to pass the
examination in our prediction may need some counselling
to improve their result. In future study we can add more
algorithms on the dataset and hence get some more
accuracy in the result. I think this is one of the best ways to
improve the performance of the student in their final
examination.
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