This document proposes using data warehousing and data mining techniques to predict student academic performance in schools. It describes collecting student data like scores, attendance, discipline, and assignments into a data warehouse. Data mining methods are then used to analyze the student data and identify relationships between variables to predict performance, such as whether students are progressing, being retained, or conditionally progressing. The results could help schools identify students at risk of failing and take actions to help them succeed.

1. USE DATA WAREHOUSE AND
DATA MINING TO PREDICT
STUDENT ACADEMIC
PERFORMANCE IN SCHOOLS
Presented by
Ranjith G N
1SJ10IS070
Under the guidance of
Mr. Nagesh R
Assistant Professor
Dept of ISE,SJCIT

2. CONTENTS
• ABSTRACT
• INTRODUCTION
• METHODS
• RESULT AND DISCUSSION
• CONCLUSION
• REFERENCES

3. ABSTRACT
 The real facts in the education institute is the significant growth of the
educational data.
 Basically the main goal of this paper is to propose a model that can be
applied on data warehouse and data mining techniques to predict
student performance in schools.
 Data mining techniques was used to extract the essential information
from the data warehouse and to explore the relationships between
variables stored in the data warehouse.
8th Sem,Dept of ISE 3 2013-14

4. INTRODUCTION
 The data in educational institute is growing significantly. Presently
the need to represent data in an integrated and consistent format is
also increasing.
 So we propose a model on which data mining and data warehouse
techniques can be applied to predict student academic performance in
schools.
 Data mining is a technique to find a relationship between variables or
factors in the large amount of database (usually data warehouse).
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5.  Here we implement the data mining techniques through data
warehouse to forecast for students progression status (progress,
retain, conditional progression) by analyzing student data.
 The information from a school system can be rapidly assessed to find
the performance of students in the school.
 The data and information gained from the learning system
can be used as an substantial indicator for monitoring of the
potential student failure in the school.
 Furthermore alerts can be sent to the parent and academic staff to
intimate them about the performance of the student.
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6. METHODS
 The implementation of this study consists of two types of methods:
(1) Analysis Method. (2) Methods for data warehouse design.
 The process of Analysis Method is done through several stages
including: Literature review, the study of literature discussing the
establishment of a data warehouse and data mining.
 In the design phase, design models and applications based on the
results of an analysis of the problem are used
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7. RESULT AND DISCUSSION
 The first objective of this study is to propose the data
mining techniques.
 The second objective is to propose the tools for the parents
or students to analysis their own data in the school.
 Data mining technique can give the input for the teachers
and students about the student academic results.
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8. 8th Sem,Dept of ISE 8 2013-14
Student
Performance
Student
Attendance
Student
Score
Discipline
System
Assignment
Fig. 1: The Framework for Student Performance Predictors

9. Grain/
Dimension
Time Student Subject Class Merits
Demerits
Index student
discipline
system    
Student score
  
Numbers of
student
attendance
transaction
  
Numbers of
student
completed
the assignment
   
Subject Rating   
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Table 1: Grain Per Dimension.
 The table 1 above shows the adjustment dimension to the existing grain.

10. FACT ATTRIBUTE
SCORE AVERAGE_SCORE_PER_
SUBJECT
DISCIPLINE NOTES
TOTAL_POINT
CATEGORY
ATTENDANCE QTY_ATTENDANCE
ATTENDANCE_STATUS
ASSIGNMENT ASSIGNMENT_STATUS
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Table 2: Student related variables.
 The entire predictor and response variables derived from the data warehouse
can be seen in table 4.

11. Table 3:Dimension and Attribute
DIMENSION ATTRIBUTE DESCRIPTION
TIME
TIME_CODE
DATE
DAY
WEEK
MONTH
SEMESTER
ACADEMIC_YEAR
The report can be viewed based on the
specific time periods, which can be
based on the academic year, semester,
month, week, day, and date.
CLASS
CLASS_CODE
CLASS_DESCRIPTION
CLASS_NAME
The report can be viewed
based on existing classes.
STUDENT
STUDENT_ID
STUDENT_NAME
CLASS_CODE
The report can be viewed based on
active students enrolled per semester
and academic year.
SUBJECT
SUBJECT_CODE
SUBJECT_DESCRIPTION
SUBJECT_TYPE
The report can be viewed based on
subject type (core subject atau special
subject).
MERITS
DEMERITS
MERIT_DEMERIT_CODE
POINT
DESCRIPTION
The report can be viewed based on
student merits and demerits.
COMPONENT
COMPONENT_CODE
COMPONENT_NAME
SUBJECT_CODE
The report can be viewed based on
component score per subject.
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12. 8th Sem,Dept of ISE 12 2013-14
Fig. 2: Star Schema for Scoring

13. Fig. 3: Star Schema for Discipline
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14. Fig. 4: Star Schema for Student Attendance
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15. Fig. 5: Star Schema for Student Assignment
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16. Fig. 6: Star Schema for All Facts
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17. Fig. 7:The model of Data Mining and Data Warehouse System
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18. Table 4: Student related variables.
Variable Values
Student Score 0 – 100{High, Medium, Low)
Student Grade { A+ > 95, A > 90, A- > 85, B+ >
80, B > 75, B- > 70, C+ > 65, C >
60, D > 55, F < 54.99}
Discipline Systems {Very Good, Good, Fair, Needs
Improvement}
Student Attendance {Good, Average, Poor}
Assignment {Yes, No}
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 The entire predictor and response variables derived from the data warehouse
can be seen in table 4.

19.  The domain values for some of the factors were defined for the
present investigation as follows:
− Student Score – The semester score (Final) obtained in all subjects
(esp. core subject)
− Student Grade – Student test grade obtained.
− Student Attendance – Attendance of the student.
− Discipline System – Discipline obtained.
− Assignment –Assignment performance is divided into two classes.
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20. CONCLUSION
 These predictions may not be appropriate if the data or
parameters required to perform data mining is incomplete.
 We can remind the students whose failure rates are potentially high
and help them from the failing. As a result, students do not need to
repeat the course (retain) and this helps the schools to conserve
resources.
 The result of this study indicates that data mining techniques provide
effective improving tools for predicting student performance.
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21. REFERENCES
 S. Z. Erdogan and M. Timor, “A Data Mining Application in a Student
Database,” in Journal of Aeronautics and Space Technologies, 2005, vol.2, No.
2, 2005, pp. 53-57.
 K. B. Brijesh and P. Saurabh, “Mining Educational Data to Analyze Students
Performance,” In International Journal of Advanced Computer Science and
Applications, vol. 2, No. 6, 2011, pp. 63-69.
 Y. Zhang, S. Oussena, T. Clark, and H. Kim, “Use data mining to improve
student retention in higher education - a case study,” In ICEIS(1), 2010, pp.
190–197.
 C. Romero, S. Ventura, P. G. Espejo, and C. Hervás, “Data mining
algorithms to classify students,”. In EDM, 2008, pp. 8–17.
 W. H. Inmon, “Building the Data Warehouse,” 3rd edition, Canada: John
Wiley & Sons, Inc., 2005.
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