# data[].attributes.body
A lot of lingering questions about the data; no opportunity to dive into the data points we felt would be more revealing and help us craft a LCP.

# data[].attributes.headline
Being given time to collaborate on noticings in two smaller district groups for the first part of the dive, and then synthesizing the learning as a whole district team.

# data[].attributes.subject
Being given time to collaborate on noticings in two smaller district groups for the first part of the dive, and then synthesizing the learning as a whole district team.

# data[].attributes.body
The protocol was helpful. It kept us focused on what the data was sharing.

# data[].attributes.body
There didn't seem to be a connection between Data set A and B

# data[].attributes.body
It makes sense to do this work with my district team.

# data[].attributes.body
Working in small groups and following a protocol.

# data[].attributes.headline
It is necessary to get a clearer, more wholistic look at root causes.

# data[].attributes.subject
It is necessary to get a clearer, more wholistic look at root causes.

# data[].attributes.headline
great to start this conversation together

# data[].attributes.subject
great to start this conversation together

# data[].attributes.body
having more information regarding the data for example being able to look at the original questions would have helped with some of our wonderings around the resposes

# data[].attributes.body
How can we implement this with our schools

# data[].attributes.body
IReady is very visual of their strengths and weaknesses

# data[].attributes.body
Collaborative discussions in smaller groups

# data[].attributes.body
The importance of clear roles and time-bound assignments for each step in the protocol.

# data[].attributes.headline
The want to triangulate the data with another data set with parent/guardian responses.

# data[].attributes.subject
The want to triangulate the data with another data set with parent/guardian responses.

# data[].attributes.body
Working on it as a mini group.

# data[].attributes.body
It was difficult to marry the data sets together.

# data[].attributes.body
Hard to have a (more) complete picture without looking at both sets of data.

# data[].attributes.body
Lost time due to technical issues getting to the data sets and breakouts. Limited alignment between the data sets for group A and B when we came together. Unclear how to apply an equity lens to looking at the data would have helped to explore that further.

# data[].attributes.body
Interesting to explicitly connect the SEL parent survey data and iReady performance data when considering assets and problem of practice.

# data[].attributes.body
We fell into the equity trap of not being able to talk about race because the data wasn't available in the second data set

# data[].attributes.body
For the panoramic data, it wasn't clear how parents interpreted the questions, which made it challenging then to understand the data.

# data[].attributes.body
Hearing others share ideas and insight.

# data[].attributes.body
Some of us aren't as familiar with IReady as we thought we were.

# data[].attributes.body
I believe that first you have to build a level of trust and set clear expectations about the process and how to use discoveries to implement change

# data[].attributes.headline
We needed the data to be disaggregated.  We felt we were missing too much information to hypothesize an learner centered problem or problem of practice.

# data[].attributes.subject
We needed the data to be disaggregated.  We felt we were missing too much information to hypothesize an learner centered problem or problem of practice.

# data[].attributes.body
It was challenging to talk about what we did in our breakout groups because the data sets were different. We didn't know if the data sets were even from the same school. Also, the data sets were not clear and easy to read (parts of it were cut off and continued on the next page, we couldn't see the entire question, etc)

# data[].attributes.headline
Everyone needs to come to the table understanding what we are doing, how we are doing it and why/where it is leading

# data[].attributes.subject
Everyone needs to come to the table understanding what we are doing, how we are doing it and why/where it is leading

# data[].attributes.body
Allowing staff to look at the data a face value and then work through the implications

# data[].attributes.body
It was great to discuss the trends with the District team as a whole

# data[].attributes.body
The data felt like a starting point, but there was a need to get a wider set of data and disaggregate data.

# data[].attributes.headline
the amount of questions it generated - next steps were clear and abundant = sparking learning!

# data[].attributes.subject
the amount of questions it generated - next steps were clear and abundant = sparking learning!

# data[].attributes.body
The collaboration with my team was helpful in interpreting the data.

# data[].attributes.headline
We wanted historical data for comparison. 

# data[].attributes.subject
We wanted historical data for comparison. 

# data[].attributes.body
Using the breakout rooms to discuss the data

# data[].attributes.headline
It would help to have more time to review both sets of data, possibly prior to session.   

# data[].attributes.subject
It would help to have more time to review both sets of data, possibly prior to session.   

# data[].attributes.body
Not familiar with "learner centered problem" and could use more work around this.

# data[].attributes.headline
Hearing how others viewed the data worked well.

# data[].attributes.subject
Hearing how others viewed the data worked well.

# data[].attributes.body
Selection of data and how schools collect data is important especially questions asked in surveys.

# data[].attributes.body
Questioning data source, get more information before think about the implications.

# data[].attributes.body
Ensuring that areas of strength are discussed and used as leverage for supporting the more challenging areas.

# data[].attributes.body
The data we received was not labeled, so we lost time deciphering it first and then barely had time to engage in looking at the data

# data[].attributes.headline
Not enough time to really dig in and clarify what I was looking at. I wish I had more time.

# data[].attributes.subject
Not enough time to really dig in and clarify what I was looking at. I wish I had more time.

# data[].attributes.body
The first thing that comes to mind, given my own experience, is need when leading these conversations with schools to ensure that the conversations remain asset-based, affirm students, and focused on what educators need to do to better serve students.

# data[].attributes.headline
We never got to a root cause analysis- just the same surface level data analysis.

# data[].attributes.subject
We never got to a root cause analysis- just the same surface level data analysis.

# data[].attributes.body
That it is an important process for schools to engage in a focused conversation about data so that they can better address issues at the school.

# data[].attributes.body
Not enough direction about what we were supposed to do.  We were given 5 artifacts but when we came back in a  group, we were asked to only look at two!

# data[].attributes.body
Something that came up in our small group was the need to do this work at the ILT/Admin level, not just departments. In relation to that, we discussed the importance of including the DCI's in the discussion.

# data[].attributes.headline
Don't rush this process. Look deeper at a few high leverage sources of data. Make sure to look at something broad AND something really granular (like student interviews)

# data[].attributes.subject
Don't rush this process. Look deeper at a few high leverage sources of data. Make sure to look at something broad AND something really granular (like student interviews)

# data[].attributes.body
When in schools, will we look at all data or select pieces of data like we did today? If so what pieces of data should be chosen?

# data[].attributes.headline
I would like to have a more experience with this process before doing this with schools

# data[].attributes.subject
I would like to have a more experience with this process before doing this with schools

# data[].attributes.body
time to have mutual collaboration with of the members of the team.

# data[].attributes.body
It always helps to have a partner to dive into data with as there may be times where you miss something be represented in the data.

# data[].attributes.headline
Hard to see connection between my groups data and the other groups. One was Iready Reading Data and the other Group had a parent survey.

# data[].attributes.subject
Hard to see connection between my groups data and the other groups. One was Iready Reading Data and the other Group had a parent survey.

# data[].attributes.headline
Opportunity to unpack common data sources in use in our district 

# data[].attributes.subject
Opportunity to unpack common data sources in use in our district 

# data[].attributes.body
Appreciate the opportunity to discuss the data with colleagues.

# data[].attributes.body
I am still confused about what we did!

# data[].attributes.body
I think we should be clear on what data we are looking at and why.

# data[].attributes.body
It was challenging to understand the data without knowing why the school focused on the data they chose and which groups of students they elected to zoom in on

# data[].attributes.body
Will we gain access to this data for all of our schools so we can plan and strategize together as a team?

# data[].attributes.headline
Parents conerned on social emotional and not on academics based off survey

# data[].attributes.subject
Parents conerned on social emotional and not on academics based off survey

# data[].attributes.body
It would have been helpful to have a 1-pager or session notecatcher with all the links hyperlinked in one place. We lost a lot of time trying to get to rooms and locate materials.

# data[].attributes.headline
talking out the data with team members to get a deeper understanding and catch things we may not have seen

# data[].attributes.subject
talking out the data with team members to get a deeper understanding and catch things we may not have seen

# data[].attributes.headline
Talking about the data. Seeing the different perspectives. 

# data[].attributes.subject
Talking about the data. Seeing the different perspectives. 

# data[].attributes.body
It was great working with a team to brainstorm together.

# data[].attributes.body
I need more training on this to be able to turn key it!  I still dont understand how this relates to a coaching cycle.  Or is it that our first cycle with be a data dive with all of our schools?

# data[].attributes.body
That sums up this entire series. Lots of different cooks in the kitchen and it is has been difficult from a learner's POV. Please give us time to make those connections that we attempted to make during the last session.

# data[].attributes.body
How will schools be informed of this structure? How many cycles will be conducted in a year? What are the expectations for schools in providing the time necessary for these cycles with their coaches and considering the various programatic issue?

# data[].attributes.body
We had trouble identifying the connection among the two data sets.

# data[].attributes.body
Often when looking at attendance data we omit or overlook other essential data.  Today's practice helped to increase awareness around how to look at data, and perhaps more importantly, what type(s) of data is missing from the conversation.

# data[].attributes.headline
time constraints and not having all data sets to look at together in the first place, but I understand the limitations

# data[].attributes.subject
time constraints and not having all data sets to look at together in the first place, but I understand the limitations

# data[].attributes.headline
The thinking from different perspectives/areas of expertise

# data[].attributes.subject
The thinking from different perspectives/areas of expertise

# data[].attributes.body
I am excited to start this work

# data[].attributes.headline
Will we have access to the same sets of data for all of our schools?

# data[].attributes.subject
Will we have access to the same sets of data for all of our schools?

# data[].attributes.body
Remembering the video at the beginning of the PL session - "How we support matters as much as what we support with."

# data[].attributes.body
The steps were unclear and the data sets were not clearly labeled or connected. It was challenging to make connections to the previous learning or between data sets.

# data[].attributes.headline
The quality of the student survey. The use of a piece of data that is extremely limited.  

# data[].attributes.subject
The quality of the student survey. The use of a piece of data that is extremely limited.  

# data[].attributes.headline
Listening to others share their interpretation of the data.  

# data[].attributes.subject
Listening to others share their interpretation of the data.  

# data[].attributes.headline
To what extent did students with low YTD attendance participate in the survey?   

# data[].attributes.subject
To what extent did students with low YTD attendance participate in the survey?   

# data[].attributes.body
What data should all coaches look at? for instance, a science coach should start the year by looking at ...

# data[].attributes.headline
Ensure clear protocols/structures for data analysis with schools

# data[].attributes.subject
Ensure clear protocols/structures for data analysis with schools

# data[].attributes.body
Schools are constantly inundated with data sets and may feel overwhelmed and unclear of which problem to tackle or which high leverage area to address. They need opportunities to engage in data dive discussions in trusting, safe, structured sessions with partners.

# data[].attributes.body
During today's session, there was a lot of edu jargon used and facilitators rattled them off as though we are all normed on this and asked "any questions" at the end of each section. These are not effective practices, so please be mindful when facilitating.

# data[].attributes.body
Why doesn't the BCO engage in these same protocols with directors in functional or district teams so we can build our capacity?

# data[].attributes.body
Looking at data without protocols is a recipe for disaster. It is so important to engage in Root Cause analyses and employ an equity lens to name disproportionality and prioritize next steps. Need more practice with leading these kinds of conversations with schools.

# data[].attributes.body
There is a ton of data available for our schools but no common language/protocols in use across the BCO to support schools in prioritizing nexts or engaging in root cause analysis. Will we be norming which data-sets, which data-analysis protocols and how to "triangulate data sets" to identify a high leverage root cause and next steps?

# data[].attributes.body
Many are new to Dr. Fergus' work, myself included. So far it seems the work aims to name and address disproportionality, identify patterns in data, and center marginalized groups. Yet the data provided wasn't disaggregated or racialized to permit the kind of discussion and analysis his work suggests is needed. Missed out on richer conversations and analysis due to limitations in the data sets.

# data[].attributes.body
Being able to examine several data points to determine what students are capable of but then to determine what are some some specific instructional next steps for cohorts of students.  There are a progression of skills that students need to learn and we have to support schools with this.

# data[].attributes.body
Not sure how to apply today's learning?

# data[].attributes.body
Today's session started with rhetoric that once again started with deficit thinking of the BCO members on the call. There are members on the call that have experience in this work.