Student Performance Drives Career: A Case Study
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This research uses the marks of the students and their personal, family information and gives a way for the students to predict their future career and make a choice without confusion. The ability of following a performance of a student is very important in education. In present trend everyone need a good career in his/her educational.To overcome some of the problem in choosing the path in which stream to select,we use the classification techniques/data mining prediction algorithm which help for this study and analyze the attributes which is given by the student and produce a result for the future career growth.
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Tapan Kumar Das el at. International Journal of Recent Research Aspects ISSN: 2349-7688, Vol. 3, Issue 4, December 2016,
pp. 19-22
Student Performance Drives Career: A
Case Study
Tapan Kumar Das and Asis Kumar Tripathy
SITE, VIT University, Vellore-632014, India
Abstract-This research uses the marks of the students and their personal, family information and gives a way for the
students to predict their future career and make a choice without confusion. The ability of following a performance of
a student is very important in education. In present trend everyone need a good career in his/her educational.To
overcome some of the problem in choosing the path in which stream to select,we use the classification techniques/data
mining prediction algorithm which help for this study and analyze the attributes which is given by the student and
produce a result for the future career growth.
Keywords—Data Mining, Decision Tree, Classification, Prediction
I. INTRODUCTION
Data mining concepts have a tremendous methodology and
techniques to evaluate and classify with the given data and
show the result using clustering/ classification technique.
This project uses the marks of the students and their
personal, family information and gives a way for the
students to predict their future career and make a choice
without confusion. The ability of following a performance of
a student is very important in education. In present trend
everyone need a good career in his/her educational. To
overcome some of the problem in choosing the path in which
stream to select, we use the classification techniques/data
mining prediction algorithm which help for this project and
analyse the attributes which is given by the student and
produce an result for the future career growth. The
application is applicable when the student after completing
the class 12 and are in confusion which course to opt for
them. It will have great existence in student life by making
the students’ task easier by showing them which course to
opt for, by taking the personal information, family
information and educational information up to 12th class and
show the result.
II. LITERATURE SURVEY
TECHNIQUE
TITLE
AUTHOR
IDEA
Performance improvement
in education sector using
classification and
clustering algorithms
M.Sukanya, s.Birutha,
Pr.S.Karthik, T.
Kalaikumar, 2012
Academics performance is a based
upon diverse factor like personal,
social, etc. It provide valid
information from existing student
to manage relationship with
upcoming students
Clustering/
Classification(Bayesian)
A study on student data
analysis using data mining
technique
Uma maheswari. K,
S.Niraimathi, 2013
Student into grade order in all their
education studies and it helps in
interview situation. Examine for
helping in rank order for the
recruitment process
Clustering,
Classification,
Association Rule,
Outlier detection
Prediction of student
academic performance by
an application of data
mining techniques
Survey on Decision Tree
Classification algorithms
for the evaluation of
Student Performance
SajadinSembiring, M.
Zarlis, DedyHartama,
Ramliana S, Elvi
Wani,2011
AnjuRathee, Robin
Prakash Mathur,2013
This determines whether a student
will be an academic genius, a drop
out, or an average performer
Smooth support Vector
machine classification,
Kernel K-means
This paper tells about different
algorthims and evaluated with
certain datasets, and made
difference between them
Classification
Algorthim, Decision
Tress, ID3, C4.5, CART
Application of k-Means
clustering algorithm for
prediction of students'
Academic performance
Oyelade O.J,
Oladipupo O.O,
Obagbuwa I.C
To monitor the progression of
academic performance of students
in higher Insitution for the purpose
of making an effective decision by
the academic planners.
k-Mean, Clustering
© 2016 IJRRA All Rights Reserved
page-19-
Tapan Kumar Das el at. International Journal of Recent Research Aspects ISSN: 2349-7688, Vol. 3, Issue 4, December 2016,
pp. 19-22
Mining Educational Data
to Analyze Students
Performance
Brijesh Kumar
Baradwaj, Saurabh
Pal,2010
To extract knowlodge that
describes students performance in
end semester examination and
helps to identify the dropouts and
student who need special attention
and allow the teacher to provide
approriate advising
Decision
Tree(Classification),
ID3 Algorithm
Data Mining approach for
predicting student
performance
EdinOsmanbegovic,
MirzaSuljic, 2012
The impact of students sociodemographic varibale, achieved
results from high school and from
the entrance exam, and attitude
towards studying which can have
an affect on success were all
investigated
Decision
Tree(Classification)
Evaluation of student
performance with Data
Mining : An Application
of ID3 and Cart
Algorithms
An Analysis of students
Performance using
Genetic algorithm
ManawinSongkroh,
Andrea Ko,2010
The process is complies with
CRISP- DATA Mining to ensure
its completeness and accuracy
CART Algorithm, ID3
Algorith, RapidMiner
T.Miranda Lakshmi, A.
Martin,
V.PrasannaVenkatesan,
2013
Genetic algorithm,
RGSPAT model
Prediction of student
performance by using data
mining methods for
classification
Dorina
Kabakchieva,2013
Mining Educational Data
to Improve Students
Performance
Mohammed
M.AbuTair,
AlaaM.El-Halees
The genetic process on the natural
evolution principles of populations
have been fairly successful at
solving problems and produce
optimized solution from generation
to generation.
It is implemented at a bulgarian
university, aimed at revealing the
high potential of data mining
applications for university
management
Collecting the data and
preprocessing the data with data
mining techinques to extract
knowledge and describe its
importance in education domain
Predicting students
performance using ID3
and C4.5 Classification
algorithms
KalpeshAdhtrao,
Aditya Gaykar,
AmirajDhawan,
RohitJha,
VipulHonrao, 2013
Collecting the data of class X and
XII marks and rank in entrance
examinations and results in the first
year of the previous batch of
student. They predicted the general
and individual perfromance of
freshly admitted students in future
examination
ID3, C4.5, HTML, CSS,
PHP, Code Igniter
Framework, MySQL,
Rapid =Miner
Identification of potential
student academic ability
using comparison
algorithm k-Means and
farthest first
AthanasiaO.P.Dewi,
WirantoH.Utomo, Sri
Yulianto J.P, 2013
How to measure the potential of
students academic skills by using
the parameter values and the area
by using clustering analysis
comparing two algorithskmeans
and farthest first algorithm
WekaDatamining
application, K-Means,
Farthest First
III. SYSTEM ARCHITECTURE
Algorithm for Student Performance drives career
Step I: Connect to data base
Step II: Take phone number as input
© 2016 IJRRA All Rights Reserved
CRISP-DM, Decision
Tree, Weka Tool, OneR
classifier
classification,
Association rules,
clustering, Outlier
Detection
Step III: Retrieve marks for Math, Phy, Chem, Biology from
Class 12 data base where ph no is same as input
Step IV: Add all the marks
Step V: Retrieve marks for Math, Phy, Chem, Biology from
Mean data base
Step VI: Now add all the marks received from Mean data
base and validate the result
page-20-
Tapan Kumar Das el at. International Journal of Recent Research Aspects ISSN: 2349-7688, Vol. 3, Issue 4, December 2016,
pp. 19-22
Step V: If the total mark of the student is less than the total
mark of mean data base
Step VI: Then display that student name as a poor performer
Step VII: End of algorithm
V. SNAPSHOTS
Naïve Bayes Algorithm
IV. DETAILED MODULES
Actual predicted calculation and pruned tree Module
Thismodulecalculatesbytaking
the
arfffileandbyusingnaveBayesitgivesoutputasactualandpredict
edcombined with the pruned tree J48.
Weka Tool Module
The Weka Tool module load the arff file and preprocessed
and by selecting the classify technique as Nave Bayes shows
the output as Mean values.
Mean Value Storing Module
The Mean Value Storing module stores the Weka tool result
and separates the mean values and store in mean excel file
and that mean excel file values is stored in database.
Signup Module
This module signing up for an account to be created so the
information will be stored inside the database. The inputs are
like name, email-id, phone number, etc.
Login Module
This module takes the roll number as user name and
password, so when the user gives input it will be validated
and send to main page.
Edit Information (Personal, Family, Educational)
Module
This module take input from the user and validates with
normal with the html, php code and save inside the database.
View Information (Personal, Family, Educational)
Module
This module takes phone number or roll number to retrieve
the information from the database and show in html page.
Career after 12th class Module
This module takes input as phone number and checks
theuserinformationmarkswiththemeanvaluewhichiscalculate
dfromserversideand retrieve the branches from the database
and show in html page.
© 2016 IJRRA All Rights Reserved
Weka Tool & MySQL Database
User Interface
page-21-
Tapan Kumar Das el at. International Journal of Recent Research Aspects ISSN: 2349-7688, Vol. 3, Issue 4, December 2016,
pp. 19-22
[4].
[5].
[6].
[7].
VI. CONCLUSION
The application takes personal, family, educational
information from student which is logging in the existing
system and will be a great strength for the system. The user
now really finds the easiest way to find which branches to be
chosen with the help of this application, viewing information
and the predicted branches which the student is need to take
is present. In this way the application could succeed in
reaching the goals which leads to an efficient and proper
output.
[8].
[9].
REFERENCES
[1]. Robin Prakash MathurAnjuRathee. Survey on
decision tree classification algorithms for the
evaluation of student performance. International
Journal of Computers and Technology, 4:244–247,
Mar-13.
[2]. Sri
Yulianto
J.P
AthanasiaO.P.Dewi,
WirantoH.Utomo. Identification of potential student
academic ability using comparison algorithm k-means
and farthest first. International Journal of Computer
Applications, 63:18–26, Feb-13.
[3]. Saurabh Pal Brijesh Kumar Baradwaj. Mining
educational data to analyze students performance.
© 2016 IJRRA All Rights Reserved
[10].
[11].
International Journal of Advanced Computer Science
and Applications, 2:63–69, 2011.
MirzaSuljicEdinOsmanbegovic.
Data
mining
approach for predicting student performance. Journal
of Economics and Business, X:3–12, May-12.
DorinaKabakchieva.
Prediction
of
student
performance by using data mining methods for
classification.
CYBERNETICS
AND
INFORMATION TECHNOLOGIES, 13:61–72,
2013.
Amiraj Dhawan Rohit Jha Vipul Honrao Kalpesh
Adhtrao,
Aditya
Gaykar.
Predictingstudentsperformanceusingid3andc4.5classifi
cationalgorithms. International Journal of Data
Mining and Knowledge Management Process, 3:39–
52, Sep-13.
Andrea KoManawinSongkroh. Evaluation of student
performance with data mining : An application of id3
and cart algorithms. The Fourth International
Conference on Software, Knowledge,Information
Management and Applications, pages 276–282, Aug10.
AlaaM.El-Halees Mohammed M.AbuTair. Mining
educational data to improve students performance.
International
Journal
of
Information
and
Communication Technology Research, 2:140–146,
Feb-12.
Pr.S.Karthik T. KalaikumarM.Sukanya, s.Birutha.
Performance improvement in education sector using
classification and clustering algorithms. International
Conference on Computing and Control Engineering,
2012.
Obagbuwa I.C Oyelade O.J, Oladipupo O.O.
Application of k-means clustering algorithm for
prediction of students’ academic performance.
International Journal of Computer Science and
Information Security, 7:292–295, 2010.
DedyHartamaRamliana S ElviWaniSajadinSembiring,
M. Zarlis. Prediction of student academic
performance by an application of data mining
techniques. International Conference on Management
and Artificial Intelligence, 6:110–114, 2011.
page-22-
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