novel approach to evaluate student performance using data mining

8/10/2019 Novel Approach to Evaluate Student Performance Using Data Mining

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Synopsis Report On

Novel Approach to evaluate student

performance using Data MiningSubmitted in partial fulfillment for the award of the degree

Of

BACHELOR OF ENGINEERING

In

INFORMATION TECHNOLOGY

By

Rahul Raghavan

Manas Saxena

Sagar Wahal

Under the guidance of

Mr. Anil Vasoya

Designation

Assistant Professor (IT)

Academic Year 2013-2014


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Synopsis Report On

Novel Approach to evaluate studentperformance using Data Mining

Submitted in partial fulfillment for the award of the degree

Of

BACHELOR OF ENGINEERING

In

INFORMATION TECHNOLOGY

By

Rahul Raghavan

Manas Saxena

Sagar Wahal

Under the guidance of

Mr. Anil Vasoya

Designation

Assistant Professor (IT)

Academic Year 2013-2014


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ACKNOWLEGEMENT

We are foremost thankful to the Principal of our college Dr. B.K. Mishrawho has taken

strenuous efforts in providing us with excellent lab facilities.

We are greatly indebted to our internal project guide Prof. Anil Vasoya for his guidanceand enlightened comments, which has helped us in better understanding our project work.

We would like to thank him for his helpful suggestions and numerous discussions which he

has guided us.

We are also thankful to our Head of Department Dr. Kamal Shah and Project Co-

coordinator

Dr.Vinayak Bharadi who always gave us constant motivation guidance and

encouragement for the project.

We are also grateful to our classmates and friends who have given us feedback and

encouragement

Finally we would wish to thank our college Thakur College of Engineering and

Technology for providing us with a platform and the necessary facilities to make this

project

Name of Students:

Rahul Raghavan

Manas Saxena

Sagar Wahal


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ABSTRACT

Data mining is a process of extracting hidden information from huge volumes of data. The

various data mining techniques used are Classification, Clustering and Association mining.

All these techniques can be applied to educational data to predict a students academic

performance and also to determine the areas he is currently lacking in.

The student can evaluate his performance and find out area to improve. In order to increase

his percentage. While calculating a students performance we take into consideration a

studentsmarks in previous semesters and his term test marks, attendance, viva marks and

other factors.

Here we use One R algorithm and Frequency table to predict the score which determines

how important a particular area is. The accuracy of this algorithm can be measured by

comparing the predicted score with the actual score.

Teachers can forward the result of students report. They can also determine whichstudents are currently lacking based on their marks and other factors. Using this data

teacher can motivate a student to improve his performance in a particular area. Also

students can view the report themselves and can make improvements based on area which

they are lacking in.


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CERTIFICATE

This is to certify that Rahul Raghavan, Manas Saxena and Sagar Wahal are the bonafide

students of Thakur College of Engineering and Technology, Mumbai. They have

satisfactorily completed the requirements of the PROJECT-I as prescribed by University ofMumbai while working on Novel approach to evaluate student performance using Data

Mining.

(Signature) (Signature) (Signature)

Name: Name: Name:(Internal Guide) (Internal Examiner) (External Examiner)

Thakur College of Engineering and Technology

Kandivali (E), Mumbai400101

Place:

Date:

(College Round Seal)

(Signature)

Name:

(Head of department)

(Signature)

Name:

(Principal)


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C O N T E N T S

Chapter No. Topic Page No.

Chapter 1 Overview

1.1 Importance of Project

1.2 Literature Survey1.3

Motivation

1.4 Scope of the Project

1

1

24

5

Chapter 2 Proposed Work

2.1 Problem Definition2.2Methodology

2.3 Data Flow Diagram2.4 As per guides instructions

6

68

1317

Chapter 3 Analysis & Planning

3.1 Feasibility Study

3.2 Project Planning3.3 Gantt Chart

18

18

2122

Chapter 4 Results & Discussion 23

Chapter 5 Conclusion 24

References 25


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1

Chapter 1: Overview

1.1 Importance of the project

Evaluation is a systematic process of collecting, analyzing and interpreting evidences of

students progress and achievement both in cognitive and non-cognitive areas of learning for the

purpose of taking a variety of decisions. Evaluation, thus, involves gathering and processing ofinformation and decision-making.

The present system of evaluation at school stage suffers from a number of imperfections. The

first and foremost shortcoming of the evaluation system is that it focuses only on cognitivelearning outcomes and completely ignores the non-cognitive aspects which are a vital component

of human personality.

Another shortcoming of the present examination system is that the results are declared in termsof raw marks which also depend on the subjectivity of the examiner.

In our project we try to extract useful knowledge from graduate students data collected fromThakur College of Engineering & Technology- Mumbai. Here, we use various data mining

algorithms to evaluate students performance. By using these algorithms we extract knowledge

that describes students performance at the end of the semester examination. It also helps earlierin identifying the dropouts and students who need special attention and allow the professor to

provide appropriate advising and counseling.

This project attempts to correct the fallacies of the current system of student evaluation. Theproject intends to extract knowledge from the raw data present. This information can help the

college management get an insight into the strengths and weaknesses of a student. Armed with

this information the college management can help students work on their personal weaknesses.The project also contains a tool that can predict the performance of the students in future

examinations.

This project provides an innovative approach towards the evaluation of student performance. It

enhances the reach of the current system which helps the students grow as individuals.


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1.2 Literature Survey

1.2.1 Use of Data Mining Techniques for the Evaluation of Student

Performance: A Case Study

ABSTRACT:

In this paper the author introduces the concept of extracting information from large volume of

database of Sri Sai University- Palampur. The author uses marks obtained by students in their

post graduate exam and other factors. Also the authorsintroduces various techniques to improve

post graduate students performance and identify students with low grades. The data include one

and half year period of data. Authors use Clustering, Decision Tree and Neural Networks are

used evaluate students performance. Italso helps in identifying dropouts and students who need

special counseling.

The drawback of this system is:

The system only takes into consideration the marks of the students. It completely ignores

the non-cognitive factors. We believe that those factors have a lasting impact on the

performance of the student.

The system does not provided any suggestions for future options for the student

The system does not evaluate the strengths and weaknesses of the student[1]

Author: Er. Rimmy Chuchra

1.2.2 Predicting students performance using ID3 and c4.5 Classificationalgorithms

ABSTRACT:

This paper introduces the concept of predicting a students marks based on previous

performances

The authors takes into consideration number factors like scores in board examinations of

classes X and XII

The system uses a number of data mining algorithms like ID3 and C.5 to predict the

marks accurately

However the drawbacks of this system are:


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The system does not take into consideration a students family background ,socio

economic factors and friend circle

Also the system does not suggest ways a person can improve his marks

The system also does not give proper results in case of missing data[2]

Author(s): Kalpesh Adhatrao, Aditya Gaykar, Amiraj Dhawan, Rohit Jha and Vipul Honrao


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1.3 Motivation

One set of the existing system of student evaluation process involves analysis of newly generated

data from separate examination conducted solely for the system.

In this project we perform data mining on the data which is already available. This provides easyintegration of our system with the current system. In addition, we also include non-cognitive data

like family background, friend circle etc.

Another set of system which we studied evaluated the student based on existing system but it

did not include the analysis of strengths and weaknesses of a student. Another important aspect

we did not find in the systems we studied was the inclusion of future options.

Thus, broadly speaking the our motivation for the project is to provide a student evaluation

system that can be easily integrated with existing system and in addition to that provide

mathematically calculated recommendations about the ways to improve the performance in the

coming semester.


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1.4 Scope of the Project

The system will take into account a number of factors by gathering data about a students

Semester marks, Term Test Marks, Attendance, Students background and various other factors

from Thakur College of Engineering and Technology (Mumbai), IT Department and predictingstudent marks. All these factors will be taken into consideration while designing the final project

which could be used by a student for decision making process. This information can be used by a

student to monitor his progress. Also this can be used by a student to determine his academic

strength.

This system will analyze the performance of the student and highlight those parameters on which

the student needs to work on in order to improve their performance in the near future.


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CHAPTER 2: PROPOSED WORK

2.1 Problem Definition

The primary purpose of our project is to provide a novel approach to evaluate studentperformance using data mining. Data mining is a type of sorting technique which is actually used

to extract hidden patterns from databases. The major advantages of using data mining are the fast

retrieval of data or information, Knowledge Discovery from databases, detection of hidden

patterns, and reduction in the level of complexity, time saving etc.

The main objective of educational institutions is to provide high quality education to its students

and to improve the quality of managerial decisions. One way to achieve high quality education is

by discovering knowledge from educational database and using it to create an environment that

helps students grow better.

The application has the following objectives:

To predict student performance on the basis of both congestive and non-congestive

parameters. The parameters are as follows:-

Aggregate Marks

Term Work

Term Test Marks

Viva Marks

Practical Marks

10

thMarks

12thmarks

Attendance

Family background

Hostelite or days scholar

Friend Circle

Educational background of father and mother

MH-CET marks

Any Live KTs or not

Educational background of siblings

Current posting of siblings

Current posting of father

Income of family

Mothers job profile

After prediction of the performance of the student, analysis based on different parameters

used to make the prediction.

Identifying the strengths and weaknesses of the student on the basis of this prediction.


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Communicating to the student the parameters on which they need to work in order to

improve their overall performance.

Flexible design strategy which allows future updating and improvements.

Round the clock availability.

Easy and understandable graphical user interface.

Formidable security measures to ward off any attacks on database of students.

We expect this application to be used by college professors and administrators for evaluating

student performance and taking important managerial decisions. The primary objective of

this application is to provide a detailed evaluation of a student to the professors so that they

can condition their teaching style to suit the needs of the student. The information provided

by the application can also be used by visiting companies to filter out those candidates that

suit their requirements.


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2.2Methodology

1.2.1: One R Algorithm:

Description:One R, short for "One Rule", is a simple, yet accurate, classification algorithm that

generates one rule for each predictor in the data, and then selects the rule with the smallest totalerror as its "one rule.The basic idea behind this algorithm is to test every single attribute and

branch for every value of that attribute. In our case we are predicting how a particular student

can improve is performance using other attributes like Term Test marks, Viva Marks and other

factors.

Algorithm:

Use of One R algorithm to calculate the weight age to be given to each parameter.

Classifying each parameter in ranges namely high , medium and low

Classifying the target attribute into high ,medium and low

Calculating success percentage of each parameter

Now we calculate total error for each frequency table and find the frequency table with minimum

or low total error. A low total error means higher contribution to improve the accuracy of the

model

One Rule Algorithm on a chosen data set:

To illustrate we have collected the sample data of students performance. We already know themarks student have obtained in semester 4. Here we use this information to calculate the impact

each input parameter has on the final result.

Figure 1.2.1.1 Sample data of students

H-High, L-Low, M- Medium


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We have classified our input parameters into the following categories:

Aggregate marks in previous semester:

Above 70High

Between 60 and 70 inclusiveMedium

Below 60- Low

Attendance in this semester:

Above 80-High

Between 60-80 inclusive-Medium

Below 60Low

Term work in this semester:

Above 85-high

Between 75-85Medium

Below 75Low

Viva marks in this semester:

Above 85- High

Between 80 to 85Medium

Below 80Low

Our class level attribute here is our semester 4 marks. We take each parameter and attempt to

match it with our class level attribute. Example:-

If input parameter Attendance is High and our class level attribute Semester 4 marks are

also High then we have a match. Similarly we calculate the total count of all such matches

from our sample data. We finally get the following result:

Frequency Table:

For input parameter: 3rd

semester marks:

Class level attribute: Semester 4

High Medium Low

Semester 3

High 2(Match) 1 0

Medium 1 1(Match) 0

Low 0 0 2(Match)

Table 1.2.1.1 Frequency Table generated for the parameter 3rd

semester marks


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Now we calculate the success rate of the total count of matches with the following formula

Success Rate= (Number of successful match / Total number of samples)*100

Example Success Rate for input parameter 3rd

semester marks= ((2+1+2)/7)*100=71.42%

Success rate is= 71.42%

For input parameter: Attendance:


High Medium Low

Attendance

High 3 1 0

Medium 0 1 0

Low 0 0 2

Table 1.2.1.2 Frequency Table generated for the parameter Attendance

Success rate= 85.71%

For Term work:


High Medium Low

Term

work

High 2 1 0

Medium 1 1 0

Low 0 0 2

Table 1.2.1.3 Frequency Table generated for the parameter Term Work

Success rate=71.42%


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For Viva:


High Medium Low

Viva

High 3 0 0Medium 0 1 0

Low 0 1 2

Table 1.2.1.4 Frequency Table generated for the parameter Viva

Success rate=85.71%

We have now calculated the percent success rates for each parameter individually. Now, we

calculate the impact each parameter has on the final aggregate score of the student.

We do this by calculating the overall impact of each of these factors in predicting the final result

Parameter Impact Rate= Success Rate / Success rate

Example:-If I want to calculate the Parameter Impact Rate for Input Parameter Attendance it will

be done as follows

Parameter Impact Rate for attendance= 85.71/ (71.42+85.71+71.42+85.71) = 0.27

Input Parameters Success rate (%) Parameter Impact Rate(PIR)

Semester 3 71.42 0.22

Attendance 85.71 0.27

Term Work 71.42 0.22

Viva 85.71 0.27

Table 1.2.1.5 Table for Parameter Impact Rate

Once we have obtained the PIR for all the parameters we will use this information to predict the

marks of the student in the current semester. We have taken the example of student Shivam

Thakur and predicted his marks for 5th

semester on the basis of the PIR we obtained.

To predict his marks for the current semester we will have to calculate his Parameter Impact

Score (PIS) for the previous semester. After that we will calculate the PIS for the current

semester and then by unitary method predict the marks for the current semester. PIS can be

obtained by the formula given below:

Parameter Impact Score (PIS) = Parameter Impact Rate*(Parameter Value)


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Ex:-So in case of input parameter 3rd

Semester marks the PIS will be:

PIS= 0.22*73=16.06

Now, here we calculate the PIS of Shivam Thakur for his score in Semester 4

Name PIS for 3r Semester Marks

PIS for Attendance(4

thsemester)

PIS for Term Work(4

th semester)

PIS for Viva(4

thsemester)

Shivam Thakur 16.06 24.84 19.8 24.84

Table 1.2.1.6 Table for Parameter Impact Score

Mean PIS for Semester 4 is = (16.06+24.84+24.84+19.8)/4

Mean PIS for Semester 4 = 21.39

We now know that when Shivam Thakur obtained mean PIS of 21.39 his aggregate score was

78%

Similarly we will calculate the PIS of individual input parameters of Shivam Thakurs for

Semester 5

Name 4t

semester

marks

Attendance

(5th

semester)

Term Work

(5th

semester)

Viva

(5th

semester)

Shivam Thakur 78 80 85 78

PIS 17.16 21.6 18.7 21.06Table 1.2.1.7 Table for Parameter Impact Score

Mean PIS for 5th

semester = (17.16+21.6+18.7+21.06)/4 = 19.63

So, when Mean PIS of Shivam Thakur was 21.39 he obtained 78 % marks. Therefore when mean

PIS value is 19.335 the marks he obtains will be:-

Predicted Percentage for Semester 5 is = (78/21.39)*19.335=71.58

However his Actual 5th

semester marks =73.2%

Hence we were able to make an approximation of the marks he will obtain in semester 5.


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2.3 DATA FLOW DIAGRAM

LEVEL 0 DFD

Fig 2.3.1: Data flow diagram Level 0

Login

Faculty

AdministratorDatabase


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LEVEL 1 DFD

Fig 2.3.2: Data flow diagram Level 1

Administrator

Verify Student

Student

Add/delete

records

Update Data

Faculty Database

Verify Faculty

User Database

Login

User

Register

Student Database

Faculty


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LEVEL 2 DFD Students

Fig 2.3.3: Data flow diagram detailing Students flow

Faculty

Login

Edit Personal

Details

Generate Reports

Faculty Database

User

User database

Register

Student SearchReport errors

Analysis


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Level 2 DFD Faculty

Fig 2.3.4: Data flow diagram detailing Facultys flow

Faculty

View Student

Data

Mail Report

Generate

Report

Login

Placement

Generate

Reports

Feedback

Student Database


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2.4 As per guides instructions

The following modifications were suggested by our guide during the design phase of the project:

Prediction:

Our guide suggested that we increase the number of parameters which we would use to predict

the performance of the students. After further research and evaluation we came up with some

additional parameters that will be used to predict the performance of the students:

Hostelite or days scholar

Educational background of father and mother

MH-CET marks

Any Live KTs or not

Educational background of siblings

Current posting of siblings Current posting of father

Income of family

Mothers job profile


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CHAPTER 3: Analysis and Planning

3.1 Feasibility Study:

Asper our project Novel Approachto evaluate student performance the total requirement forsetting up the project is given below:

Time Feasibility:Our project requires only software so the time required for the project is

68months

WE ARE DESIGNING OUR SOFTWARE ACCORDING TO SOFTWARE DEVELOPMENT

LIFE CYCLE [SDLC]

SR No. CRITERIA TIME PERIOD

1. Feasibility Study 0.5 months

2. Analysis and Data Gathering 1.5 months

4 Design of project 1.5 months

4. Implementation (Coding) 2 months

5. Testing and Finalization 1 months

6. Maintenance 1.5 months

TOTAL 8 months

Table 3.1.1: Software development lifecycle

SOFTWARE REQUIRED: The whole project is designed using JAVA technology


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Project Schedule:

VII Semester Timeline:

Name Duration Start Finish

1.Requrement Analysis 25 days 2/08/2013 27/08/2013

1.1 Software specification 4 days 2/08/2013 6/08/2013

1.2 Presentation 7 days 6/08/2013 13/08/2013

1.3 In house requirement

specification

2 days 13/08/2013 15/08/2013

1.4 SRS 8 days 15/08/2013 23/08/2013

1.5 Requirement

Gathering

4 days 23/08/2013 27/08/2013

2.Analysis 12 days 27/08/2013 8/09/2013

2.1 User Requirements 3 days 27/08/2013 30/08/2013

2.2 Functional

Requirements

5 days 30/08/2013 4/09/2013

2.2 Non functional

Requirements

4 days 4/09/2013 8/09/2013

3. Design 21 days 8/09/2013 29/09/2013

3.1 Architecture Design 6 days 8/09/2013 14/09/2013

3.2 Database Schema 7 days 14/09/2013 21/09/2013

3.3 Graphical User

Interface

8 days 18/09/2013 29/09/2013

Table 3.1.2: VII Time Line


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VIII Semester Timeline:

Name Durati

on

Start Finish

4. Coding /Implementation 60

days

25/01/2014 25/03/2014

4.1 Database Creation 3 days 25/01/2014 28/01/2014

4.2 Software Development 12 days 28/01/2014 10/02/2014

4.3 Database Integration 4 days 10/02/2014 14/02/2014

4.5 Coding and Implementation 20 days 14/02/2014 6/03/2014

4.4 Integration 6 days 6/03/2014 12/03/2014

4.5 Implementation of

Application

20 days 12/03/2014 25/03/2014

5. Verification and Testing 30

days

25/03/2014 25/04/2014

5.1 Unit Testing 5 days 25/03/2014 30/03/2014

5.2 Stress Testing 5 days 30/03/2014 05/04/2014

5.3 Alpha/Beta Testing 6 days 5/04/2014 11/04/2014

5.4 Acceptance testing 5 days 11/04/2014 16/04/2014

5.5 Performance Testing 5 days 16/04/2014 21/04/2014

5.6 Modification 4 days 21/04/2014 25/04/2014Table 3.1.3: VIII Time Line


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3.2 Project Planning

The goal of our system is to evaluate ,predict and improve students performance.A student can

also monitor his progress.The system will also help students as well as companies to improve

their placement process.

The key stakeholders of this system are:

1. Students

2. Faculty

3. Administration

4. Development Team

Student:

The reports can be mailed to the student so that he/she can analyze his/her performance

And improve his /her performance

Faculty:

Faculty will visit the system the most.Faculty will be able to view a students weakness and

strengths ,help to improve them accordingly.

Administrator:

The administrator will be responsible to maintain the system.Administrator will update a

Students records.Administrator will be responsible to report errors made by the Facuty andforward it to college.Administrator will be responsible to add/delete/edit student information.

Development Team:

The development team will be repsonsible for checking any bugs in the system. Also the

development team will report System critical error and provide patches for the system.

Development team will be reponsible for adding new functionalities to the system

Project Deliverables:

Platform to interact between between students and faculty Customized report generation for faculty of students

Administrator can edit/delete/add student information


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3.3 Scheduling(TimeLine chart):

Fig 3.3.1: Gantt Scheduling chart created using Microsoft Visio


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CHAPTER 4: RESULTS & DISCUSSION

Our application will use One R algorithm to evaluate a students performance. Our system uses a

number of parameter to calculate a parameter index score for the student and evaluate them on

the basis of this score. The student will be able to visualize which topics or areas they arecurrently lacking in. Faculty will mail the analysis report to the student. The student can improve

upon the areas he is currently lacking in to improve his overall score. For example if a student

notices that his attendance parameter is not up to the mark then he can work upon improving his

attendance. This would ultimately help him understand his weak area and allow him to focus

more on that area.


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Chapter 5: Conclusion

The educational system is the backbone of progress and development of any society. Greater the

ability of the education system to improve the performances of its students better the chance of

the society to produce successful citizens. Keeping this fact in mind it is necessary to constantlywork towards a more sophisticated education system.

Data mining is an incredible concept which provides us hidden information from voluminous

and exhaustive databases. Data mining can provide many solutions towards making a stronger

education system. Our project is a stepping stone towards the integration of technology and the

education system.


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References:

[1] IJCSMR paper-Er. Rimmy Chuchra Use of Data Mining Techniques for the Evaluation

of Student Performance: A Case Study

[2] IJDKP paper-Kalpesh Adhatrao, Aditya Gaykar, Amiraj Dhawan, Rohit Jha and Vipul

Honrao PREDICTING STUDENTS PERFORMANCE USING ID3 AND C4.5

CLASSIFICATION ALGORITHMS

Books: Data Mining Concepts and TechniquesJiawei Han and Micheline Kamber

Websites

Java-ww.oracle.com

Wikipedia-www.wikipedia.com

One Rule Algorithm-www.soc.napier,ac.uk/~peter/vldb/dm/node8.html

http://www.saedsayad.com/oner.htm
http://www.wikipedia.com/http://www.wikipedia.com/http://www.wikipedia.com/http://www.saedsayad.com/oner.htmhttp://www.saedsayad.com/oner.htmhttp://www.saedsayad.com/oner.htmhttp://www.wikipedia.com/

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