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

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Outline

• Introduction• Data Description & Defining Risk• Data Processing• Experiments• Conclusion

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IntroductionMotivation:

• 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.

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IntroductionMotivation:

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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.

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Outline

• Introduction• Data Description & Defining Risk• Data Processing• Experiments• Conclusion

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Data Description

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

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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)

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Outline

• Introduction• Data Description & Defining Risk• Data Processing• Experiments• Conclusion

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Data Processing and Feature

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Data warehouse

19 millionSPSS

Modeler

Consider CRCT,ITBS,CogAt

CRCTs for grade7

Mike 750

Jasmine  

Thomas  

Alice 821

Peter  

Jenny 812

Longitudinal Feature Data

  

Grade CRCTs for grade8

CRCTs for grade7

CRCTs for grade6

CRCTs for grade5

ITBS for grade8

ITBS for grade5

ITBS for grade3

Mike 7   750 680 693   42 43Jasmine 6     823 805   62 58Thomas 5       725   45 42Alice 8 832 821 815 811 68 62 59Peter 4             64Jenny 7   812 795 822   60 63

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Grade

Mike 7

Jasmine 6

Thomas 5

Alice 8

Peter 4

Jenny 7

CRCTs for grade8

Mike

Jasmine

Thomas

Alice 832

Peter

Jenny

Student Profile

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gender ethnicity

Free meal

Gifted Special education

Absent day

Sus-pensions

Discipline

Mike M B Y N Y 0 X 85Jasmine F W N Y N 10 X 87Thomas M W N  N N 5 X 85Alice F W Y Y N 0 X 92Peter M W N N N 20 O 65Jenny F B N Y N 0 X 90

gender

Mike M

Jasmine F

Thomas M

Alice F

Peter M

Jenny F

ethnicity

Mike B

Jasmine W

Thomas W

Alice W

Peter W

Jenny B

Discipline

Mike 85

Jasmine 87

Thomas 85

Alice 92

Peter 65

Jenny 90

Merged Data Set

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gender ethnicity

Free meal

Gifted Special education

Absent day

Sus-pensions

Discipline

Mike M B Y N Y 0 X 85Jasmine F W N Y N 10 X 87Thomas M W N  N N 5 X 85Alice F W Y Y N 0 X 92Bill M W N N N 20 O 65Jenny F B N Y N 0 X 90

  

Grade CRCTs for grade8

CRCTs for grade7

CRCTs for grade6

CRCTs for grade5

ITBS for grade8

ITBS for grade5

ITBS for grade3

Mike 7   750 680 693   42 43Jasmine 6     823 805   62 58Thomas 5       725   45 42Alice 8 832 821 815 811 68 62 59Peter 4             64Jenny 7   812 795 822   60 63

• Target variable: CRCT grade 8

Creation of Target Variable Dependent Data

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Grade CRCTs for grade8

CRCTs for grade7

CRCTs for grade6

CRCTs for grade5

ITBS for grade8

ITBS for grade5

ITBS for grade3

Mike 7   750 680 693   42 43Jasmine 6     823 805   62 58Thomas 5       725   45 42Alice 8 832 821 815 811 68 62 59Peter 4             64Jenny 7   812 795 822   60 63

Imputation of Missing Features

  

Grade CRCTs for grade8

CRCTs for grade7

CRCTs for grade6

CRCTs for grade5

ITBS for grade8

ITBS for grade5

ITBS for grade3

Mike 7   750 680 693   62 43Jasmine 6     823 805   58Thomas 5       725   65 42Alice 8 832 821 815 811 78 71 59Peter 4             64Jenny 7   812 795 822   72 63Jason 7 790 785 801 58 63Mao 7 635 697 45Marry 8 846 777 51 58Cube 8 545 657 732 39 47Bill 7 753 745 44 49Gary 8 897 902 87 91 96Han 8 801 786 759 70 54

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Mean: (832+545+897+801)/4 = 769

Mean: (750+821+…+753+902)/8 = 788

Experiments

• Introduction• Data Description & Defining Risk• Data Processing• Experiments• Conclusion

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Risk Prediction

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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

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Peformance

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Performance

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Early Prediction of the Risk

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Early Prediction of the Risk

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Outline

• Introduction• Data Description & Defining Risk• Data Processing• Experiments• Conclusion

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Conclusion

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

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