Educational Data Mining in relation to Educational Statistics of Nepal

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This is the final presentation done at the Institute of Engineering Pulchowk Engineering Campus. We have applied data mining techniques like Regression Analysis, Clustering, to find the problems in education of Nepal. We had collaborated with Depart of Education of Nepal for Data. We came up with a suggestive term called "Educational Development Index" to find the relative development status of a district. To read the complete report of our research please check here:- http://flipkarma.com/project/educational-data-mining-in-relation-to-educational/

Software

EDUCATIONAL DATA MINING
In Relation To Educational Statistics Of Nepal
Final Presentation
Presenters:
Roshan Bhandari (16226)
Sijan Bhandari (16236)
Subit Raj Pokharel (16237)
Sujit Maharjan (16239)
Supervisor:
Bibha Sthapit
(Lecturer, Pulchowk Campus)
Co-Supervisor:
Anjesh Tuladhar
(COO, YIPL)

35 000 Schools
50 billion + budget
20 parameters
2 times a year
400 + NGOs and INGOs
Statistics

Department of
Education
Office of
Controller of
Examination
“Education for all”

Problem ???
Reports Not Accessible
Unmanaged and Unformatted

2007
Schools - 29448
Stds - 6533392
2008
Schools - 31156
Stds - 6964553
2009
Schools - 32130
Stds - 7295433
2010
Schools - 33160
Stds - 7463793
2011
Schools - 34361
Stds - 7444134

Educational Data Mining
• New methodology of manipulating
educational data.
• Modern methodology started since 2008.
• In this regard, we are the first people to
introduce Educational Data Mining to Nepal

API For Developers
District wise number of
school in the year 2007
http://edm.hamroschool.org/numschool/2007

API For Developers
Parameter values of
Kaski for 2011
http://edm.hamroschool.org/kaski/2011

Educational Development
Index for Individual District

Comparison of Districts
Year wise Pupil
Teacher Ratio
Comparison

Cluster Analysis
Year2066 Year2067 Year2068
1 24.08481 26.19629 25.74609
2 53.66839 50.65694 52.70283

Government
2785
private
87
Cluster Analysis
Government
2872

Prediction & Parameter Relationships
● Correlation Coefficient
● Least Square Regression
● Standard Error of Estimate
● Multiple Regression

Multiple Regression
Our proposed models:
Gross Enrollment Model
Girls Enrollment Model
Dropout Rate Model:
○ Dropout rate = 53.65 + 0.4644 * Enrolled Passed - 5.455*GPI -
0.084*Unqualified Teacher
○ Standard Error = 2.87
○ R2 = 80.73 %

Schools Classification
• Tried to classify schools based on the
performance of the schools
• Six subjects, average marks of school and pass
out data are used to classify schools
• ID3 algorithm has been used to construct
Decision Tree.

Enhancement
• More parameter can be included to get more
accurate results
• More data can be provided by collaborating
with DoE
• A real time data update and feeding
mechanism can be built to make system more
realistic.
• More data mining techniques can be used to
extract core information.

EDI
Clustering
Regression
Classification
Conclusion

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