# data[].attributes.headline
Interested to see if other teachers on the team would have scored students the same because maybe the teacher that scored the student had an implicit biased against that student.  

# data[].attributes.subject
Interested to see if other teachers on the team would have scored students the same because maybe the teacher that scored the student had an implicit biased against that student.  

# data[].attributes.headline
We are seeing a discrepancy between referrals we have written and what is reflected on the spreadsheet. Are only fighting referrals listed?

# data[].attributes.subject
We are seeing a discrepancy between referrals we have written and what is reflected on the spreadsheet. Are only fighting referrals listed?

# data[].attributes.body
There are 17 teachers who have no idea what this data means, what the parameters are, and how to utilize the numerical representations to improve student behavior.

I'll give you 3 guesses as to which 17 teachers...

# data[].attributes.body
It would be helpful to have a color that is not read. A student who is an "at risk 5" will be different from an "at risk 18".

# data[].attributes.headline
There are some students who are rated a "0" but we do still have concerns about them.  These could include students who have poor attendance, students who are very quiet and not disruptive but who we may have concerns about their social interactions, lack of friends, lack of participation, etc.  

# data[].attributes.subject
There are some students who are rated a "0" but we do still have concerns about them.  These could include students who have poor attendance, students who are very quiet and not disruptive but who we may have concerns about their social interactions, lack of friends, lack of participation, etc.  

# data[].attributes.body
There are a high number of ECE students that qualify as high-risk for internalizing and/or externalizing.

# data[].attributes.body
Our students are externalizing a lot of behaviors due to the trauma they have experienced in the past. Additionally, their externalizing behaviors seem to be connected with how they relate to each other. The issues they are having in the classroom have to do with their peer relationships and how to handle those frustrations.

# data[].attributes.headline
Teachers do so much in the classroom to avoid having infractions. I believe it would be much higher if we were strick.

# data[].attributes.subject
Teachers do so much in the classroom to avoid having infractions. I believe it would be much higher if we were strick.

# data[].attributes.body
With the students that came to mind, I am not surprised by their data. I wonder how behavior is handled after a student receives a referral. Are they automatically sent to ISAP or suspended after so many referrals?

# data[].attributes.body
Some of the numbers are reflective of only one particular time period for a student, specific to one behavior incident or something they were experiencing at the time.

# data[].attributes.body
We are noticing that a lot of the students that have referrals written for them, but nothing is listed in the spreadsheet for them. It is not clear if the referrals were either not processed or not included in the spreadsheet.

# data[].attributes.headline
We are concerned about the number of students with internalizing behaviors and the lack of mental health support/ students waiting to talk to someone. We wonder if there is something we can be doing to help in the meantime.

# data[].attributes.subject
We are concerned about the number of students with internalizing behaviors and the lack of mental health support/ students waiting to talk to someone. We wonder if there is something we can be doing to help in the meantime.

# data[].attributes.body
I am not surprised at who scored high. These students are withdrawn in class and have little support from home.

# data[].attributes.body
When looking at our team's data, it is not accurate

# data[].attributes.body
Because we all completed it individually and not as a team, some students show different behaviors in different classes/environments, so I think it would be more beneficial to have done it as a team to gather more accurate data

# data[].attributes.body
Students flagged are not surprising. We wondering about some others that didn't score high but are very quiet. It is hard to figure out if there is an issue or if they are just a quiet person.

# data[].attributes.body
how do we plan to make unique plans for students who are red across the sheet versus students who are extremely high in just one category?

# data[].attributes.headline
We are seeing many students who we have written referrals for, but nothing is in the spreadsheet. Now we aren't sure if the referrals were either not processed or just not in this spreadsheet.

# data[].attributes.subject
We are seeing many students who we have written referrals for, but nothing is in the spreadsheet. Now we aren't sure if the referrals were either not processed or just not in this spreadsheet.

# data[].attributes.body
I think the data needs to be taken with a grain a salt- it was completed in December during a pandemic when teachers were drained. YES our students have a lot of needs and we need as MUCH SUPPORT as possible. But if this is our only data point then it's skewed.

# data[].attributes.body
It’s interesting to see that that external and internal SRSS scores do not always positively correlate.

# data[].attributes.body
Surprised by data! This data does not reflect what is going on in our classrooms. We have extensive interventions throughout the day

# data[].attributes.body
We are excited about this additional layer of support for students. What an incredible source of data. It's interesting to compare students internalizing and externalizing different  behaviors.

# data[].attributes.headline
Interesting seeing data backing some of the students that are at risk.

# data[].attributes.subject
Interesting seeing data backing some of the students that are at risk.

# data[].attributes.body
The data is good to have, but would be even better if you knew the ways the students were at risk. Some may have different risk factors than others that need to be addressed differently.

# data[].attributes.body
Differentiating between these two locales might help identify students with issues on the bus but not in the classroom due to the structures which exist in the classroom but not on the bus. This can result in attendance issues because so many of our students have no way to school except for the bus. If they are removed from the bus, even temporarily, it has a severe effect on learning and the ability for students to keep up in class.

# data[].attributes.body
I'm not surprised by the data that I'm seeing. The first couple of students that came to mind are showing red in the exact areas that I see them struggling in as well.

# data[].attributes.headline
It is amazing the amount of students who have referral data but no ISAP data. 

# data[].attributes.subject
It is amazing the amount of students who have referral data but no ISAP data. 

# data[].attributes.body
Not surprisingly, many of the at risk students from the Pelicans are ECE students who receive extensive interventions throughout the day.

# data[].attributes.headline
Lots of information to look over here.  Hope this additional layer of data helps us propel students forward.

# data[].attributes.subject
Lots of information to look over here.  Hope this additional layer of data helps us propel students forward.

# data[].attributes.body
Click on the plus sign add a post. Breifly record your thoughts on the data.