# data[].attributes.headline
ELs are struggling the most across the board. 

# data[].attributes.subject
ELs are struggling the most across the board. 

# data[].attributes.headline
A bit hard to understand but it looks like the top two tiers (green and blue) are grouped into the 58% umbrella and the bottom two tiers (yellow and red) have 42% of the students doing poorly.

# data[].attributes.subject
A bit hard to understand but it looks like the top two tiers (green and blue) are grouped into the 58% umbrella and the bottom two tiers (yellow and red) have 42% of the students doing poorly.

# data[].attributes.body
100% ELLs are below standard in life science

# data[].attributes.headline
Math is an area of need for the AA student group

# data[].attributes.subject
Math is an area of need for the AA student group

# data[].attributes.headline
Black/African American and Hispanic scores have gone up from previous year(s)... If we recall correctly. 

# data[].attributes.subject
Black/African American and Hispanic scores have gone up from previous year(s)... If we recall correctly. 

# data[].attributes.headline
Schoolwide looks like we are just barely keeping our heads above water (58% doing average or above) while Hispanic, African-American and ELLs are struggling.

# data[].attributes.subject
Schoolwide looks like we are just barely keeping our heads above water (58% doing average or above) while Hispanic, African-American and ELLs are struggling.

# data[].attributes.body
We need to revisit what the best practices are for teaching ELA, math, and science

# data[].attributes.headline
More focused instruction on  ELLs at all levels.

# data[].attributes.subject
More focused instruction on  ELLs at all levels.

# data[].attributes.body
Communication between grade levels so we can make sure all content is covered

# data[].attributes.body
Science is an area of need across all groups

# data[].attributes.body
What about SPED?

# data[].attributes.body
I remember learning about how Aeries classifies "Hispanic" in a very strange way, where if they check off more than one race, it eliminates them from the "Hispanic" group, or something like that. I don't recall all the details, but I wonder if this issue has been revisited by Aeries, and if this is reflected in the data we are seeing right now.

# data[].attributes.headline
why are there so few ELLs for each test?  And there are different amount of ELLs on each test.  Why?

# data[].attributes.subject
why are there so few ELLs for each test?  And there are different amount of ELLs on each test.  Why?

# data[].attributes.headline
ELL is a small group, so harder to compare scores to Hispanic and African-American groups

# data[].attributes.subject
ELL is a small group, so harder to compare scores to Hispanic and African-American groups

# data[].attributes.headline
We need more information to be able to see where the need is, and to see what growth has happened

# data[].attributes.subject
We need more information to be able to see where the need is, and to see what growth has happened

# data[].attributes.headline
Making sure we are taking advantage of using our IABs and other practice tests.

# data[].attributes.subject
Making sure we are taking advantage of using our IABs and other practice tests.