1. Predicting Student Risks Through Longitudinal Analysis Date : 2015/04/23 Resource : KDD’14 Author: A.Tamhan,S.Ikbal,B.Sengupta,M.Duggirala…. Advisor : Dr. Jia-Ling Koh Speaker : Sheng-Chih Chu 1

2. Outline Introduction Data Description & Defining Risk Data Processing Experiments Conclusion 2

3. Introduction Motivation: K-12 reflects the most critical phase of an personal lifelong learning, during which the opportunities for a successful future need to be created and nurtured. Poor academic in K-12 is often precursor to unsatisfactory eduational outcomes,which are associated with social costs and significant personal. 3

4. Introduction Motivation: 4

5. Introduction Goal: Building predictive module to predict students at risk of poor performance is first goal. In addition, early prediction can allow teachers take remedial actions in a students’s learning path. 5

6. Outline Introduction Data Description & Defining Risk Data Processing Experiments Conclusion 6

7. Data Description GCPS is one of the largest school systems in the US,consisting of 132 schools and serving more than168000 students at present. 7

8. Defining Risk CRCTs : (Score rang from 650~900) 850↑(excedding standards) 800 (standards) 800↓(at risk) (mathematics,science) ITBS : (provse PR) 25% as a thresholds on grade 8 (at risk (reading,written expression,mathematics,science,…) CogAt (reasonable ability) 8

9. Outline Introduction Data Description & Defining Risk Data Processing Experiments Conclusion 9

10. Data Processing and Feature 10 Data warehouse 19 million SPSS Modeler Consider CRCT,ITBS,CogAt

11. CRCTs for grade7 Mike750 Jasmine Thomas Alice821 Peter Jenny812 Longitudinal Feature Data GradeCRCTs for grade8 CRCTs for grade7 CRCTs for grade6 CRCTs for grade5 ITBS for grade8 ITBS for grade5 ITBS for grade3 Mike7 750680693 4243 Jasmine6 823805 6258 Thomas5 725 4542 Alice8832821815811686259 Peter4 64 Jenny7 812795822 6063 11 Grade Mike7 Jasmine6 Thomas5 Alice8 Peter4 Jenny7 CRCTs for grade8 Mike Jasmine Thomas Alice832 Peter Jenny

12. Student Profile 12 genderethnici ty Free meal GiftedSpecial education Absent day Sus- pensions Discipline MikeMBYNY0X85 JasmineFWNYN10X87 ThomasMWN NN5X85 AliceFWYYN0X92 PeterMWNNN20O65 JennyFBNYN0X90 gender MikeM JasmineF ThomasM AliceF PeterM JennyF ethnici ty MikeB JasmineW ThomasW AliceW PeterW JennyB Discipline Mike85 Jasmine87 Thomas85 Alice92 Peter65 Jenny90

13. Merged Data Set 13 genderethnici ty Free meal GiftedSpecial education Absent day Sus- pensions Discipline MikeMBYNY0X85 JasmineFWNYN10X87 ThomasMWN NN5X85 AliceFWYYN0X92 BillMWNNN20O65 JennyFBNYN0X90 GradeCRCTs for grade8 CRCTs for grade7 CRCTs for grade6 CRCTs for grade5 ITBS for grade8 ITBS for grade5 ITBS for grade3 Mike7 750680693 4243 Jasmine6 823805 6258 Thomas5 725 4542 Alice8832821815811686259 Peter4 64 Jenny7 812795822 6063

14. Target variable: CRCT grade 8 Creation of Target Variable Dependent Data 14 GradeCRCTs for grade8 CRCTs for grade7 CRCTs for grade6 CRCTs for grade5 ITBS for grade8 ITBS for grade5 ITBS for grade3 Mike7 750680693 4243 Jasmine6 823805 6258 Thomas5 725 4542 Alice8832821815811686259 Peter4 64 Jenny7 812795822 6063

15. Imputation of Missing Features GradeCRCTs for grade8 CRCTs for grade7 CRCTs for grade6 CRCTs for grade5 ITBS for grade8 ITBS for grade5 ITBS for grade3 Mike7 750680693 6243 Jasmine6 823805 58 Thomas5 725 6542 Alice8832821815811787159 Peter4 64 Jenny7 812795822 7263 Jason77907858015863 Mao763569745 Marry88467775158 Cube85456577323947 Bill77537454449 Gary8897902879196 Han88017867597054 15 Mean: (832+545+897+801)/4 = 769 Mean: (750+821+…+753+902)/8 = 788

16. Experiments Introduction Data Description & Defining Risk Data Processing Experiments Conclusion 16

17. Risk Prediction 17

18. Dataset ITBS data set contains 58361 samples containing 15.3% positive(at-risk) and 84.7% negative(non-risk) CRCT data set contains 43036 students containing 10.7% and 89.3% samples. Used 5-fold cross validation Used SPSS or Weka 18

19. Peformance 19

20. Performance 20

21. Early Prediction of the Risk 21

22. Early Prediction of the Risk 22

23. Outline Introduction Data Description & Defining Risk Data Processing Experiments Conclusion 23

24. Conclusion The result showed that a student’s risk of poor performance can be predicted with reasonable accuracy. 24