This document describes a study that used multiple regression to build a model for predicting the academic performance of elementary schools. The researchers analyzed performance data from 400 California schools. They identified 5 key attributes that impact performance: English language learners (ELL), student mobility, average class size, parental education, and percentage of fully credentialed teachers. A regression equation was developed relating these attributes to academic performance. The model was verified to have good accuracy and predictive power. Attribute impacts were then interpreted, and recommendations were provided for how schools could improve performance by addressing these factors. An automated dashboard was created to allow calculating predicted performance based on inputting attribute values.

1. Predicting Academic
Performance of an
Elementary School
Submitted By
Ankush Roy
Ashitha VS
Koushik Rakshit
Krishna B
Roma Agrawal1
(Multiple Regression using SAS)

2. Data Description
2
• Dataset contains performance data of 400
elementary schools from the California
Department of Education.
• Attributes like average class size, parent’s
education, students opting for free meal etc.
• Many attributes have missing values, outliers
which needs to be fixed before using this in
analysis.

3. 3
Objective
• To find important attributes that majorly affect
the academic performance
• Using this attribute, build a model that can predict
the overall academic performance of school
• Verify the model before accepting that.
• Create an automated excel sheet using above
regression model, which will give the performance
taking attributes as an input.

4. 4
Finding Important Attributes,
Building Model and its
Verification

5. 5
Attributes and their Impact
After statistical analysis, we found below 5 attributes
to be most important and on which performance
depends.
api00=459.713-2.899(ell)-3.108(mobility)+3.688(acs_46)+3.377(grad_sch)+2.332(full)
Regression Equation

6. 6
Model Verification
 MAPE (Mean Absolute Percentage Error)
MAPE=Mean(ABS((Actual value – Predicted
value)/actual value) )*100
(It should be <10)
For our model, MAPE=8.9140897
 Adjusted R-Square
For our model, Adj R-Sqr=76.5%

7. 7
 Residuals are normality distributed (as per graph
and also with Anderson-Darling test)

8. 8
Attribute-wise Interpretation and
Recommendation

9. 9
English Language Learner
• ELLs have negative impact on overall performance of
school means decreasing this will improve
performance.
• ELLs more means students are coming from different
geographies where English speaking is less and they
are poor as they haven’t learnt English speaking.
• There will be a challenge in communicating to these
students.
• Schools can work on this, to improve its performance
by providing special coaching to ELLs to ensure
students master in English.
• Recruit teachers who can better understand the need
of these ELLs.

10. 10
Percentage first year in school(Mobility)
• %age of students who are not completing their
studies in school.
• It is negatively affecting the performance, as
students are studying for some time and then they
move. Decreasing mobility will improve the
performance.
• It affects students also, as they will not be able to
concentrate on studies.
• One of the reason for mobility can be residence
change of family.
• School should consult with parents and make them
understand to keep their child in same school.

11. 11
Average Class Size 4-6
• It is positively associated with performance of
school means increase in this will increase the
overall performance also.
• It can be improved by
– decreasing mobility
– Increasing promotion of students from one grade
to another grade
– Increase in students taking admission each year.
• This is the direct indicator of overall performance
of a school. But without controlling other
variables, only increase in this will not help
school in increasing performance.

12. 12
Parent’s Grad School
• It has positive impact on increasing performance of a
school.
• If parents will be educated they can take care of
children’s studies at home as well. They will take
interest in their homework, they will ask for marks
etc. which will help child a lot.
• Children can learn many things from their parents
apart from school.
• School can start some initiative for parent’s
involvement like monthly parent’s-teacher’s meeting
in which teachers can discuss with parents about
their child’s studies.

13. 13
Percentage Full Credential
• This is percentage of full time teachers in school.
• It has positive impact on performance of school as
they are the source of knowledge for the students.
In case of absence of temporary teachers, students
would be sitting idle.
• School should recruit more full time teachers and
provide them good working environment so that
they can give their 100% in sharing their knowledge
with students and help them in learning better.
• Shortage of teacher can also be a reason for
student’s mobility and low performance.

14. 14
Dashboard Creation

15. 15
api00=459.713-2.899(ell)-3.108(mobility)+3.688(acs_46)+3.377(grad_sch)+2.332(full)
Regression Equation
**By entering values for 5 attributes, api00 (performance) can be
calculated

16. 16
Increasing ELL, decreases the performance value(API00)
Impact of English Language Learner

17. 17
Increasing Mobility, decreases the performance value
(API00)
Impact of Percentage first year in
school(Mobility)

18. 18
Increasing acs_46, increases the performance value
(API00)
**But this should be increased by controlling other factors. Simply increasing
class size will not help in improving performance.
Impact of Average Class Size 4-6

19. 19
Increasing grad_sch, increases the performance value
(API00)
Impact of Parent’s Grad School

20. 20
Increasing full, increases the performance value(API00)
**This is the attribute which is totally in control of school. So increasing this
will help a school in increasing performance.
Impact of Percentage Full Credential

21. 21
Thank you…!!!