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 Schools50 billion + budget

20 parameters2 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

2007Schools - 29448Stds - 6533392

2008Schools - 31156Stds - 6964553

2009Schools - 32130Stds - 7295433

2010Schools - 33160Stds - 7463793

2011Schools - 34361Stds - 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

What we have done ??

Block Diagram

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

Map

Charts

Districts with max. Dropout Rate

Educational Development Index (EDI)

Map

Educational Development Index for Individual

District

Comparison of DistrictsYear wise Pupil Teacher Ratio Comparison

Cluster Analysis

Year2066 Year2067 Year20681 24.08481 26.19629 25.746092 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 ModelGirls Enrollment ModelDropout 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.

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.

EDIClustering

RegressionClassification

Conclusion

Tools Used

Thank You