Predicting academic performance of an elementary school using attributes like class size, enrollment, poverty, parent education, student performance, teachers credentials from 400 elementary schools from the California Department of Education's API 2000 dataset

1. SOMDEEP SEN; Business Analyst: Trimax Analytics
(e) somdeepenggmba@gmail.com; (p): 09748229123
LinkedIn: http://linkd.in/1ifqs3x

2.  The Data set contains:
 Performance of 400 elementary schools from the California Department of Education
 Factors like class-size, parent education, student performance, etc.
 Objectives:
 To find the factors having major influence on the academic performance
 To predict academic performance of an school using those factors

3. Factors
Impact
English language learners(ELL)
Negative
Percentage first year in school (Mobility)
Negative
Parent grad school (grad_sch)
Positive
Percentage full credential (Full)
Positive
Average Class size 4-6 (ACS_46)
Positive
Note: Factors have been chosen based on statistical significance

4. Variable
Label
Parameter
Intercept
Intercept
459.71
ell
english language learners
-2.90
mobility
pct 1st year in school
-3.11
acs_46
avg class size 4-6
3.69
grad_sch
parent grad school
3.38
full
pct full credential
2.33
Regression Equation
API00= 459.71+ (-2.90)* ell+ (-3.11)*(mobility) + 3.69* acs_46+ grad_sch*(3.38) + full* (2.33)
To view the detailed SAS Code please visit the following link:
http://bit.ly/1c08pGE

6. •
ELLs are one of the fastest growing populations in the public schools
•
Number of ELLs in CA is healthy due to the geographic location & economic significance
•
ELL students come from different backgrounds &face multiple challenges
•
But, the main challenge continues to be the problem in communication
Recommendations:
•
Provide special coaching to ELLs to ensure that they master English
•
Special coaching should be done before they get tested in English in core content areas
•
Ensure that all ELL students receive the full range of services
•
Improve teacher training opportunities so teachers can understand the needs of ELLs

7. •
Students making non-promotional school changes is known as mobility
•
California students, like students in the rest of the U.S., are highly mobile
•
Mobility happens due to following reasons:
– Families changing their residences
– School changes initiated by students especially in California
– School changes initiated by schools especially in California

8. Recommendations:
Families should:
•
Attempt to resolve problems at school before initiating transfer
•
Make changes between semesters or at the end of the school year
Schools should:
•
Counsel students to remain in the school if at all possible
•
Prepare in advance for incoming transfers
•
Assess the past enrollment history of incoming students
•
Assess the number of previous school changes
•
Facilitate the transition of new students as soon as they arrive

9. •
Research shows US students spend less than 15% of their time in school
•
Therefore parent involvement is as important as the time spend in school
•
Checking homework, attending school meetings, influences student performance
•
Educated parents finds it easier to get involved than the others
Recommendations:
•
Look to ensure that parents with school graduation lie between 65-70%
•
Conduct parent interview during the admission of the students
•
Also take initiatives to increase parent engagements
•
But schools shouldn’t limit a parent’s involvement based on socio-economic status

10. •
Experienced teachers are more effective at raising student
•
Experienced teachers are also more likely to be fully credentialed also
•
Hence teacher retention could be instrumental in performance improvement
•
Shortage of fully credential teachers is a prime reason for low performance & mobility
•
Many assume that financial incentive is the silver bullet; but that is only partially true
Recommendations:
•
Financial incentives can make schools more attractive to more qualified teachers
•
Money is Necessary, But Clearly Not Sufficient
•
Teachers often leave due to poor working conditions, and lack of administrative support
•
Schools should recruit & develop administrators who can draw on the expertise of teachers

11. •
Improvement in avg. class size in 4-6 grade((ACS_46) tends to improve performance
•
ACS_46 can be improved when:
– Mobility is low
– Promotion of student’s from one grade to another is high
•
So, it can be said that ACS_46 is an indicator of the overall academic performance
Recommendation:
•
Focus should be on all the recommendations mentioned previously to improve ACS_46

14. Before the treatment
•
After the treatment
Outliers were found using the proc univariate option & treated accordingly

15. •
This is done to check the overall significance of the model
•
H0: independent variables collectively or individually can’t influence the dependent variable
•
H1: the independent variables collectively or individually can influence the dependent variable
•
If P value>α: H0 can’t be rejected & hence the model is useless
•
If P value<α: H0 is rejected & hence some independent can influence the dependent variable
•
In this case the P value<α & hence some independent can influence the dependent variable

16. •
This happens when the independent variables are highly interdependent
•
Hence the individual impact on the dependent variables can’t be correctly estimated
•
The extent of multicolineraity is captured by the variance inflation factor(VIF)
•
The final model must have only those variables having VIF ranging from 1.5 to 2

17. •
To control multicolineraity certain variables gets removed based on high VIF
values
•
For the rest the significance of the corresponding population parameter
•
The P values of the variables are checked for the significance
•
Variables having P value>α are not important for the model
•
The final model must have variables having P value>α & VIF ranging from 1.5 to 2

18. •
This occurs when the variance of the random error component is not constant
•
The White’s test used for the check For Heteroscedasticity
•
Null Hypothesis: Model is Homoscedastic
•
If P value>α: H0 can’t be rejected & hence the model is Homoscedastic & viceversa
•
The VIF SPEC option is used to check for the Heteroscedasticity

19. •
Once the model has only the significant variables the o/p file created
• The o/p file contains the predicted & the residual variables
•
The residual variables saved in the o/p file for normality
•
This is done using the proc univariate with normal option

20. •
Mean absolute percentage error or MAPE captures the overall % error of the model
•
Ideally MAPE should be with in 10%

21. •
This captures the proportion variation that can be explained by the linear regression
•
Higher the value of R-square, better the explanatory power
•
This acts as a measure of goodness of fit of the model
•
R- square value should be at least 65% or .65

22. API00(E3)= C3+C4*D4+C5*D5+C6*D6+C7*D7+C8*D8
OR
API00= 459.71+ (-2.90)* ell+ (-3.11)*(mobility) + 3.69* acs_46+ grad_sch*(3.38) + full* (2.33)