Department of Information Technology
FYPQA_BE Project Poster_2019-20
ITB14:STUDENT PERFORMANCE
PREDICTION USING DATA SCIENCE
Snehal Bodekar,Neha Chaudhari,Advait Shetye, Prof Yash Shah
1.Abstract
Prediction of student’s performance is desired by most of
educational entities and institutes. That is essential in order
to help at-risk students and assure their retention,
providing the excellent learning resources and experience,
and improving the institution’s ranking and reputation.
However, that might be difficult to be achieved for schools
to mid-sized universities, especially those which have
small students’ records for analysis. So, the main aim of
this project is to prove the possibility of training and
modeling a small dataset size and the feasibility of creating
a prediction model with credible accuracy rate. This
research explores as well the possibility of identifying the
key indicators in the small dataset, which will be utilized
in creating the prediction model, using visualization and
Machine Learning algorithms. Best indicators were fed
into multiple machine learning algorithms to evaluate them
for the most accurate model. The proposed model relies on
attributes such as previous year grade,past exam grade and
various other attributes to predict the end exam grade of
the student.
4. Proposed System & Flow Chart
To achieve the project’s aims, quantitative simulation
research methods were conducted as suggested in the
framework phases shown in Fig. 1. In these phases the
dataset will be prepared
to be passed through visualization to extract the top
correlated indicators.Then, the indicators will be used
in different classification algorithms and the most
accurate model will be the chosen for predicting
student performance in dissertation projects and all
courses grades. In between, and before the
classification models’ evaluation phase, the datasets
will pass through a pre-processing (cleansing, missing
data imputation) stage to make it ready for the
analysis phase.
2. Introduction/Theory
In present educational systems, student performance
prediction is getting worsen day by day. Predicting student
performance in advance can help students and their teacher
to keep track of progress of a student. Accurate predictions
of student’s academic performance at early stages helps in
identification of the weak students and enable management
to take the corrective actions to prevent them from failure.
One way to achieve highest level of quality in higher
education system is by discovering knowledge for
prediction regarding enrolment of students in a particular
course, alienation of traditional classroom teaching model,
detection of unfair means used in online examination,
detection of abnormal values in the result sheets of the
students, prediction about student’s performance and so
on. Many institutes have adopted continuous evaluation
system today. Such systems are beneficial to the students
in improving performance of a student. The purpose of
continuous evaluation system is to help regular students.
3.Problem Statement
One of the most challenging tasks in the education sector
is to predict student’s academic performance due to a huge
volume of student data. In the context, we don’t have any
existing system by which analyzing and monitoring can be
done to check the progress and performance of the student
mostly in Higher education system. Every institution has
their own criteria for analyzing the performance of the
students. The reason for this happening is due to the lack
of study on existing prediction techniques and hence to
find the best prediction methodology for predicting the
student academics progress and performance. Another
important reason is the lack in investigating the suitable
factors which affect the academic performance and
achievement of the student in particular course.
5. Result and discussion
Figure.2. Correlation Heatmapt
Figure.3. Webpage Interface
Figure.1. Flow chart
Fig.4. Output Sceenshot
Educational Data Mining (EDM) and Learning Analytics
(LA) research have emerged as interesting areas of
research, which are unfolding useful knowledge from
educational databases for many purposes such as
predicting students’ success. The ability to predict a
student’s performance can be beneficial for actions in
modern educational systems. Existing methods have used
features which are mostly related to academic
performance, family income and family assets; while
features belonging to family expenditures and students’
personal information are usually ignored. In this paper, an
effort is made to investigate aforementioned feature sets
by collecting the scholarship holding students’ data from
different schools. Learning analytics, discriminative and
generative classification models are applied to predict
whether a student will be able to complete his degree or
not. Experimental results show that proposed method
significantly outperforms existing methods due to
exploitation of family expenditures and students’ personal
information feature sets. Outcomes of this EDM/LA
research can serve as policy improvement method in
higher education.
6. Conclusion
The major advantage of Student score
prediction system is its robustness and ease of
installation.Use
of
machine
learning
techniques will prove to be more accurate
when the given data set of the students is more
accurately formed and stored which will help
in predicting particular student’s score based
on his previous record. This system would also
help in reduction risk of the failure of the
student as the depending on the predicted
score one can improve the preparations for the
studies.It thus demonstrates that with least
resources and maintenance, it is capable of
stabilizing performace thus helping the
students in their future building.
7. References
[1]Adejo, O., & Connolly, T. (2017). An
integrated system for predicting students’
performance in higher education(IJCSIT),
[2]Abu Zohair, L.M., Prediction of Student’s
performance. Int J Educ Technol High Educ
[3]John Jacob, Kavya Jha, Paarth Kotak and
Shubha Puthran ‘Educational Data Mining
Techniques and their Applications’