001Good evening. Welcome to our November 20th, 2025 board of education meeting. Would somebody like to make a motion to return to public session? Motion by Bill, second by Barbara. All those in favor? 6. Thank you. Um, if everybody would join me in the pledge of allegiance. I >> pledge algiance to the flag of the United States of America and to the republic for which it stands, one nation under God, indivisible, with liberty and justice for all. Thank you. Does anyone have any board announcements tonight? I have a few. You indulge me a moment. Um, first of all, the most exciting thing I think that's happened, we have state champions in the house again. Not only did our women's cross country team bring home the state championship, Sloan Wasserman brought home the individual state championship. Um
002our class ran first and she set a course record. Um so congratulations to Sloan. Uh she is going on to run next year for Boston. Um, and uh, we look forward to seeing what she's going to do in the winter and the spring. And Coach Day has just been a complete gamecher for our program. So, um, you know, ton of gratitude for him and hopefully he's going to bring back the Noilly days where the girls were just on fire with our cross country team. Uh, coach Nohilly, uh, for those who don't know, we just went into the Hall of Fame this year. So, it's, um, quite appropriate that we bring home a state championship again. Um, I don't know if anybody had a chance to attend the John J. Middle School drama this past weekend,
003but they did an amazing job. Um, you know, a ton of thanks to Mr. Frri and everyone else that is behind the scenes making the magic happen. Um, the future is definitely bright and I can't wait to see them in a couple years what they're going to do um at the high school, which reminds me that uh tonight starts opening night of the John J. high school production of a Midsummer's >> Night Dream. Wanted to make sure that I had that right. Um I plan on getting there at some point this weekend. Uh it's always just a great intimate um production where you're up really close and personal to the stage and so I'm looking forward to that. Um and then we had a fantastic walkthrough yesterday. Um and got to see our increased Miller
004staff um in action and all the students and um one of I think to me one of the most heartwarming things was seeing some former students of Dr. Drake running up in the halls to give her a hug. Um but there was just a lot of a ton of good work following our pillars going on in the school. So, we're extremely fortunate um to get the opportunity to see the work up um close and personal and to see what the budget dollars that the community um approves for us at work. It's um there's really some great things going on in the schools. Um and then, uh yeah, so that's all I have for announcements. Anybody else has anything to say? We can move on. Next. Hut. Did you have anything you wanted to add? >>
005No. >> Okay. >> Just forget. I would ask. I'm hope thinking you're probably along with the rest of your um classmates are looking forward to the Thanksgiving break coming up. >> It will be good. Um okay. With that, we will move on to 7.01 collaborative inquiry. and we welcome you to our board table. >> Well, thank you for coming tonight everybody. I appreciate it. And um just as the we're hand it over to the team here a little bit. So for us tonight, we're going to be walk through a process tonight regarding our data set. And the main thing we want to really think about here is the process, not necessarily kind of what we're the data we're particularly looking at, but it is the process. And then with that, um, this is like you're
006you're like you're in the kitchen with your teachers because we're right at the same spot. We're working on this together. We're figuring out what our next dancers and really how to do. So it's not, hey, we've been doing this for a couple years. We're right with everybody. We all of us have our sleeves and that's what we're just going to hopefully do with you tonight. roll those sleeves up a little bit, but please know that we're right along with our teacher colleagues as we start along this journey about how do we really use data in a meaningful and powerful way to impact our students. So that's again just that uh kind of backdrop. We'll go ahead and start it from there. If you Dr. Drake, you want anything before we hand it off? >> No,
007I'm just really excited. These are excellent colleagues of ours. Um, and they've done a really awesome job of of using all of their strengths to make something that could be a little confusing and intimidating digestible and understandable. So, I'm thankful to have you guys as our colleagues and looking forward to this evening. >> Excellent. Um, so today we're going to talk about data literacy and we're going to lean into this key protocol where we are unlocking data literacy. Um, I'm Jessica Kingsbury, the new director of assessment research technology, and I'm so fortunate to tonight to be joined by three of our assistant principles. And over here we have David from K5 representing elementary, Mike over representing middle school, and then Mallerie at the high school. So, it was really nice to be able to work
008on this together because we each have a different lens and a viewpoint. And this came out better than I ever could have imagined thanks to their collaboration. So, from here, we keep coming back to this graphic just to frame it from that very 10,000 foot view. So right now we've worked on vision 2030. We've launched it within our buildings. We have our pillars, our traits, our portrait traits. But that's really our destination. That's where we're going. We know we're not going to get there overnight. It's a multi-year plan to get us there. And the thing that's going to bring us there is our professional learning communities. So along with our teachers, with our collaborative teams, uh that's kind of the road that we have in our analogy. And then if we're going to go on
009the road, we need to start the car. So the data is really the keys that are going to help us get to that destination. And keeping in mind that we have lots of different data sets that we can look at, but our ultimate goal is to come up with a unified process so that no matter what data set you're looking at, everyone is using the same language and process to analyze that data. And alongside this, um, we are also trying to do work with Dr. Julia Drake as well as with Dr. Maggie Conrad about some unified learning experiences for our staff as part of a multi-year PD plan. So, we're thinking about those things that we want to be sure that every single staff member has a common language and understanding of, whether you've been
010here for 20 years, whether you just got hired. So, as we look at this presentation tonight and we look at data literacy, it might be the first of a multi-part series on data literacy. But the intention for us is that when we roll this out to teachers, it will be rolled out across grade levels, across buildings, and then as part of our new teacher orientation, making sure that is a capstone learning experience when you enter our district to be sure that everyone has the same understanding of our priorities. Um, and lastly with this, we do want to just reiterate that the decisions that we're doing right now are very based in research. So Mand Mandinachch and Gummer are data analytic experts and that's their passion. But when we go into teaching as our jobs, we
011don't go into it because teach data is our passion. We go into it for the kids. So as the system says teachers, you must look at data, you must look at data. Mandr realized that our teacher preparation programs for teachers on a whole doesn't include a lot of work of data. So what they did is as there was pressure coming from different policy changes that teachers must look at data, they said that they needed to establish what does that actually mean and they used this definition up on the board of using data to transform information and to transform those learning experiences for students. So with this Mandinach and Gummer uh defined what data literacy is and then what we had done here prior to my time had started to develop out using Nancy Love's collaborative
012inquiry protocol. So the process that you'll see tonight is an adaptation of that. >> All right. Thank you Jessica. When we look at why data literacy and why that's important as educators our students are always at the forefront of our why, right? and how we can continue to support their growth. So we look at data literacy. Data literacy will help us empower us to make informed decisions. Uh it will continue to help promote collective responsibility and provide educators with that common language as Jessica mentioned before uh to help us work towards unified goals. Um, in addition to this, with having strong data literacy literacy skills, excuse me, uh, students will be able to receive that same common experience in the class and looking at it through an equity lens will help us continue towards towards
013that path to empower them uh, as we set forth on our uh, 2030 vision. Uh the first handout for tonight that we I just want to reference here is our KSD uh PLC process, our collaborative teams workflow. So starting I want to say in the spring of last year uh all of our staff was introduced to the four essential questions of PLC. Uh the first one being what do we want students to know and able to do? Uh so throughout the district teachers have been working diligently together uh to to identify priority standards. Uh in addition to that they're also working on the second question right now of how we how will uh we know students have learned it right so in doing so working on developing common formative assessments. The one focus that I
014want to draw your attention to on this sheet is the highlighted section here uh labeled teach. This is where our collaborative uh inquiry protocol is going to come into play. Uh teachers will be able to use this to help answer the next two questions a part of our PLC process. So the next question of how will we respond if students haven't learned the concepts and skills and then also how will we extend learning for those students who have been able to grasp those concepts and skills. I'd also like to draw attention to to the circular arrows uh that are also on this graphic. Uh this is a continuous process. This isn't a one-time event. Uh sometimes a misconception with data use is something that we compile and look at at the end. This is going
015to be a continuous process to help inform our instruction to provide the best level of support for all of our students. And I want just want to say this was super timely because we saw a lot of this yesterday. We saw the individual lessons. We saw the students doing peer work together. We saw, you know, the teachers standing back and watching and being able to see because they're all standing at the board who is struggling and be able to so it's working already. It's uh it's really great to see it in person. >> That's fantastic. Um and the last piece I just wanted to mention here is through looking at data with this new lens and this is this is a first experience for a lot of us as Dr. Blanch had mentioned before. Uh
016this is just going just the start of unlocking new opportunities throughout the district for our staff for our students and in the community. Uh which we're very excited about. Uh our collaborative inquiry protocol is broken up into three sections. our key uh as as we tagged it here. Uh the first section is really helping our staff understand the data that they are looking at. When you look at data, sometimes it it could be a daunting task and this process really helps break down the information for staff. Uh it helps build that shared understanding uh across the district to help interpret what they're looking at. Uh this also this process encourages uh thoughtful questions uh and engaging in this conver this type of collaboration will help us determine next steps for our students and yield uh
017and be able to take action developing smart goals uh as we proceed moving forward. Uh for tonight's purposes, we're going to be looking at uh New York State data for 2023 2024 state regent exams for ELA, algebra, and physics uh that David kindly uh put together for us tonight. Uh again, this is uh New York State data throughout the state. Uh we're going to be utilizing our collaborative inquiry protocol uh and walk you through this process uh just to get a little glimpse of what our collaborative teams and our educators throughout the district uh are going to be partaking in throughout the course of the year. Uh the next sheet as part of our handouts here we have our collaborative inquiry key >> as well as our collaborative inquiry mini for us to help us
018walk through this process. Um this graphic uh reflects not only the collaborative inquiry process but also the collaboration behind it. So, I'd just like to take a moment to acknowledge uh Jessica and Mallerie uh for their collaboration and and hard work and creativity in developing this tool together uh because it's going to benefit our staff and in turn, you know, help our students uh continue to grow. So, I just want to take a moment and thank them because it's definitely helped uh our data team as as we work through this uh process as well. Uh the first step that David as I'm going to turn it over to him in a moment is to predict and to go visual. Uh these first two is steps are really essential steps to help our educators within their
019collaborative teams calibrate their teams right level set see where everyone is at before coming to a consensus and moving forward within the process. Uh, so at this time I'm going to turn it over to David and >> wait. So I'm gonna kind of lead you through the first few steps of the process. And Mike mentioned this sheet. This is we're I'm call this the mini. And then this will be your packet which has the graph on the front cuz we're going to go back and forth between talking a little bit looking at the some slides and then doing some individual work and talking with partners. So just so you have those two sheets ready to go. Um, the first step of the process is our predict step. Um, and to orient yourself, the slides will
020have these colored icons like the pink predict one, and that'll always match up on your mini with whatever step we're on right now. Um, so for predict, we're thinking about what we expect to see in the data. The reason that's an important step to start with is because it gets you immediately cognitively engaged with the data. Um it's when we've gone through this I found that really engaging to start out right away you have have it revealed the data but you have to make your own predictions. Um and then it also helps question our mental models. Um when we've run through this a few times I think we've all been very surprised on different data sets making a prediction and then seeing sometimes being very close sometimes being very far away from it. Um >>
021it's like being a scientist. You make a hypothesis and you then >> I think you'll see a lot of parallels in the process. Yeah, it's it's also not like terribly familiar to to most people. You're used to just diving into data. All right, what do we what do we what do we know about what we see here? And so it is >> and there's a couple steps. You'll say like we won't see the data for a couple steps, which I think definitely slows you down and gets your brain going right away. >> Um so we're going to do that step now. Um >> in the pink section, there's a few kind of like sentence starters to get you thinking. So I predict, I assume, I wonder, I'm expecting to see. So, there's not a ton
022of space, but just in that little pink area, jotting down things you're expecting to see when we look at the proficiency scores from the ELA, algebra 1, and physics regions. And just a reminder, this is not KSD data. This is for the average for all of New York State. So, when you're making those predictions, just keep that in mind. So, we'll take one minute to just write down. >> And David, we're looking at one year of results. Yes, it's the 20 the snapshot was from the 23 24 school year. >> Okay. >> And it's a comparison. It's just what we think the state scores are going to be or what we think >> Kona Louis Faro is going to >> No, just predictions about what you think the state data is going to look like
023when we review it. Okay. So, as you're writing those down, let's just take a second to turn to someone next to you, share out those predictions. We've found that this whole process is a very like social process, and a lot of the thinking happens with other people and partners, small groups. So, take a minute, share out the predictions, and then we'll move on to our next step. >> I predict scores are generally going to be higher in physics is that there's going to be a higher proportion of students who algebra math was just sneak one that's the younger It's hard they didn't give you the years. Physics is just always a hard one. So I definitely physics and I think my numbers might be even for New York State overall. I think it might be
024my number too high because we have past years >> numbers are always I'm always shocked at how low the numbers are. I would think so in 2022. No, I'm sorry. Was it? >> Yeah. >> Okay. Forget what year it was. >> Let's come back together. I hear a lot of interesting conversation. Just to clarify, I just want to make sure everyone understood because these can get confusing in terms of >> when predictions when we were thinking about this, we meant percent of students that would end up having like passed the exam or gotten a proficient score, not like what the score that would be considered proficient. I don't know if that was a confusion for anybody, but we were just thinking about what that bar in our mind, how many% of students statewide passed the
025physics exam, how many passed algebra and so forth. Um so moving to the next slide we write hand inhand with making predictions we have the next step is called go visual um and this is where we can define our expectations visually with color ranges and I saw Kim brought her matching lights here to >> turn it on. We can show how it works. >> Yeah, you're done. I'll move fast. Um, so we have three different colors. We have green, yellow, and red. Um, so think about it like this, and the chart might help a little bit for green. If the this percentage of students was proficient, then that would meet our expectations. Like we are going to set our own individual where we think is a green zone, what we think is the yellow range,
026what we think is the red range. For example, you might say as a state if above 80% of students were passing, then that would be in the green zone for us. It's in terms of prioritizing different things to focus on as a state. Obviously, we would do this as a school or grade team, but we're way zoomed out, then that would be fine. Then the yellow zone would be if that percentage fell in that range, then it would be something that would make us pause and we might need to look into it further, might need to address it. If the percent of students proficient fell into a red range, that would be something immediately concerning. Stop. This has to be addressed immediately. So the tool, the colors again, it's each person might have their own
027slightly different range, but it helps a group also think about if you're looking at many data sets, what are we going to prioritize? Because you might be looking at 10 different things, whether it's a group of standards and a state test or something when if it was more focused, but it helps you prioritize among those. Um, and it's kind of like a stoplight analogy, thinking about how urgent your different priorities are. Um, so on your paper, your packet paper is right here. Um, we are going to do this right now. That's why we had the highlighters. Um, we're going to pretend the pink is red and then you have a green and a yellow. Um, so on that graph, you're going to shade in what you would consider your green zone, your yellow zone, and
028your red zone for percent of students that are proficient on a regent exam. And again, I gave 80% as an example of where green could be, but that doesn't have to be the number. Just think about what yours would be, and then we'll share them out, and we'll come up with kind of like an average of the group. >> Does that Does that make sense? Oh, no one. >> You have to share your markers. >> He's got the two in the fist. >> Wrestling mentality. >> Next time bring more market. >> Thank you. I'm very traditionalist with my >> You guys went for like the most interactive fun so far this year. >> Well, we didn't tell you guys. You're the guinea pigs for us doing this group. >> Also glad that you think data
029is fun. >> We're a very competitive group. We are >> team one won bingo yesterday. >> I disagree. >> Unofficial result. Looks like almost there. So notice we still haven't seen the actually revealed the data, right? We were talking about how there's a few stages of getting you to think um before you've even looked at the data. So if we could just share a couple people, we'll go color by color and then Jess is going to >> wave a magic wand, >> help with some tech wizardry and we're going to then map that onto the slide over there. So anybody want to share what their green zone was? What threshold? Let's show >> 80 80% and higher. >> 80. Okay. >> Oh, I went much lower. >> It's okay. It's okay. >> No wrong answers
030here. >> Higher. >> 55. 55. >> Maybe 55. >> I for ELA. I actually went 90. I think that if we have 90% of our kids are at expectation, that's where they should be. >> Oh, but we're not saying what they should be. Are we saying what they should be or are we saying what the data shows? >> This is information. Like I say 90% of our kids should be proficient in ELA. >> I think 100% should be but >> yes it so this this color exercise this part of it is more is about the should. >> So it's not a predictive exercise in thinking like oh we think 50% statewide or 70% statewide. It's more like personally and but it would be more like as a team, right? If you're in a school group,
031if you're in a grade team or something like that, where do we think our students should be proficiency wise? So, if you want to take >> we can go back to it for a second. If you want to adjust it and think about like green, what would be your cut off as a state that you think, okay, if we're in this green zone as a state, we're doing well, not not a priority to address this. And it's also good to keep in mind that 55 or whatever number you thought each of these levels might show up as because we'll once we look at the data you'll be able to compare. >> Yeah, it was that was what I was marking. >> That's great. >> So with that adjustment we had a 90 80. Anybody have
032a different number for green? >> Yeah. Um, for English I was at 95. Algebra 185. >> So, can we say an average of 90? >> Yeah. Like we're all lumped around 85 or 90. >> And physics lower just cuz. Yeah. Physics 70. >> I had 85. Physics. >> Okay. >> I had 70 as well. >> Physics is hard. >> Yeah. So, if that So, let's set our green though around maybe 85. Sounds like And then again, this is not an exact science. We're just getting the sense of the group. Then for the next cut score, where did people have their yellow zone between what range? >> I had small. So I like a 10 point below. >> And what was what was it like? >> So for English, I had between 7 and 80 yellow.
033For algebra, I had um between 75 and 65. And then for physics, I had 70 to 60 yellow >> a little bit bigger sort of >> just to kind of encompass a larger universe of students. >> Yeah. >> Um so I I would consider yellow between 55 and above. >> Okay. >> Any other yellow scores before we map it? >> Let's go with go 55. So then and then by definition anything below that cut score would be the red zone. >> So again this it might depend on the person the group but it gives you a tool then as we now reveal the data in the next step to think about okay which scores are falling in our red zone yellow zone green zone what how we going to prioritize. But just before you do
034this, it >> Yeah. >> Do we get a prize if we're bingo? Get a wolf sticker. >> Okay, there we go. >> The very different CL like scores together though. What when because >> it's it's just the way we decided to approach the data this time. Uh you might just be looking at one uh one exam at a time. So, for example, if I'm in the math department, I'm probably going to only be looking at the algebra scores and then I would develop the red, yellow, and green for that. But for this exercise, we just lump them all together. >> Okay. >> Yeah. I think great question. >> That's what I was saying too about like that we're very zoomed out. So, like I'm thinking elementary like at a grade team level, you might more
035be even looking at one specific test and then the standards on that test would be more let's say 10 different standards, where do they fall? Green, yellow, red based on student performance and then seeing like, okay, which standards do we need to address? That would be like a more like granular example. >> Okay. >> All right. Uh I also wanted to to to touch on too with this last process how it translates to other aspects of what teachers uh could potentially use it for in in the classroom. Uh for example, in the development of rubrics, right? If a teacher is working on uh a common assessment for uh an an ELA writing sample, uh this is a procedure that they could use. All the ELA teachers within a uh grade level or department can come
036together and utilize the same process to determine what the rubric is going to be and hash out those expectations as well. And again, it's just a means of of calibrating on what everyone's expectations uh are and be able to I keep hitting this mic. I don't know how you guys do this. Um and and how they can move forward with that, right? So, >> I can see that super helpful. One conversation we've been having is how do we make sure that students getting the same experience regardless of the teacher that they have? And so, I can see where that's really helpful to make sure that that's actually happening. and it's some really good objective way to determine that. That's a really great >> use of that tool. >> So, I've been tasked with the Morgan
037Freeman narrator role setting the stage uh for the next couple slides here. So, uh the next section here is to observe. Okay. So, we want you once we populate uh the the data set here uh to think about, you know, what stuck out to you. Was your predictions in line with the current data uh that we're presenting? Uh what are you noticing? What do you see? Okay, turn it over to Dave. >> Okay, so the big reveal. Um here is the data. Um and just a couple reminders, this is statewide data across New York State. Um and these the yet yellow, red, and green that you're seeing are the ones we just kind of settled on as a group. Um, so you can see the different columns. The numbers might be hard to read, but
038so it's 79% for ELA, 62 for algebra 1, and 74 for physics. So in this observation phase, um, we're going to take a couple minutes in the yellow section of your mini sheet. Um, there's kind of two steps within it. The first is just your low inference observations. What are you seeing right in front of you? What do you notice? And then you'll see phase two. What are you surprised by? So kind of thinking back to your predictions. How did things play out compared to that? Take a couple minutes and write down your observations. And you also see like predictions. >> It is. It did. Is this 79? Is that the students who >> which is all below what we said they should be at? That's actually higher. >> Yeah, that's shocking. But kids think
039that >> that's right. It's a student, too. This is important. This require >> Okay. Um, does anybody want to share out any reactions, any thoughts, observations? I heard the word shocking thrown around. >> He's an expert observer of years. Excellent. >> I'm shocked that algebra was so much lower than physics. >> Oh man, >> but Bill and I pointed out again, this is why I asked why are we lumping these together? I know that it was for the exercise, but think about it. The people who take physics are taking it's self- selective. Whereas algebra, we are requiring our students to take algebra. ELA, it is important that our kids take. So, you're going to see high scores in that because I wanted >> and I was only one point off on ELA. I had I
040had 80. >> You won. >> And you're you're almost jumping ahead to the next. >> Perfect. What you just said, it's coming. So if we move to the next slide, um kind of part two of the observation step is defining a student problem. And again, a different grade team, a different guiding coalition, a different building team looking at data could define different problems with the same data. Um but this is kind of just even a sentence frame to help us think about having a data point, having a certain group of students, how did they perform, where was the data source? So if we fill that in on the slide, one potential problem, student problem you could define here is 62% of high school students in New York State are scoring proficiently on the algebra 1
041regions test as evidenced by the New York State report card. >> All right. Uh, next up here, so in the green highlighted section here on your mini and also on the key is making inferences and questions. Uh, now as we ran through this process several times as a district data team, one popular and common question that that always arose when we were doing this was, uh, are we missing any data? Right? Is there additional data sources that we need to be able to make the most best informed decision to move forward? So, with that frame in mind, uh, think about that as we go on to the next section here. >> We're going to take one more moment to jot stuff down again. We're going to have the green step infer question. Um, so now
042that we have our data, we have our green, yellow, and red zones, we've talked about our observations. In this step, we're going a little bit deeper in our thinking. What questions are now arising? One of them, Mike mentioned, could be other data that you'd want to see. Um, it could just be wonderings about why the data looks like this. Um, and then any inferences you're starting to make based on the data. So, take a minute and then we'll share those out and have them typed up. Okay. So, let's just share out a few. Jess is going to type them up as we go so we can track them. Um because this is you're thinking about the key and we'll talk about this in a second, but this is a pivotal step because this is going
043to kind of decide as a group look at the data if you're going to continue kind of going through this cycle a few more times or kind of launch off into the next part of the process. Um depends on kind of what inferences and questions you generate as a group. >> You're taking my line, David. Um, I was wondering if it includes just general ed students or all students. >> I went right into what lessons or how what's our curriculum around algebra in New York State because that's the one that stands out to me as a big problem. Mhm. >> I wonder like does this is this reflective of most students? What percentage of students take the regions? >> Um and then what are the demographics of folks who didn't pass? >> How has it
044changed over time? Is there a big difference between last year's scores and this year's scores? >> And has the and has the test remained the same >> or has it changed over time? You might also wonder like how did males versus females perform? >> That was something that came up in one of our reports. >> I wonder the age of the students. >> Females will score much better. >> That goes without saying. So this and this data set is not nearly as rich as the ones we'd be looking at like a teacher would be looking at because there would just be so much more context and data points. Again, this is just a very zoomed out one to just kind of go through this process. But we already have so many different avenues that we
045could start investigating and other data to look at. And that leads us right into that next part of the key. >> All right, >> Mike, you can repeat what I said. >> Here we go. All right. So now we come to a fork in the road. All right. Uh and really bas all fantastic questions. Please join us for a data team meeting one one time because phenomenal job. Uh but really based on the questions that you shared uh we would then repeat this cycle. Okay. Which is completely okay uh to have that continuous process here. Uh now if everyone came to a consensus, we felt like we had enough information to determine and verify cause and effect, then we would head right and we would pass go and collect $200 and head on to the
046next the next step here. Um but really based on the questions that everyone formulated here tonight, we're going to cycle through this one more time and look at another data set that Mallerie is going to guide us through. All right. So we had um several questions on how to look at this data as well. We were discussing well how could we disagregate this data male female by um whether students are general education or students with disabilities and that's what we decided to go with. Um, so notice at the bottom of your mini there is a Yeah, there is a pause button um to allow you to either go through the cycle again um or to um to go back to the previous um to the next section. Uh for the purposes of this question, we're
047going to predict and go visual at the same time. Uh the only >> I don't know. Okay, I'll do that. >> All right, pretend you didn't see any of that. >> So, um, so what I'd like you to do is take two minutes. Uh, we're going to go into this packet for a quick moment. Go to the second page. >> Have a good dish, by the way. Notice that we have it as general education students and students with disabilities. So, what you're going to do is on here you're going to make two predictions for every for each of the content areas. So, what percentage of students do you think were proficient in ELA that are general education and that are special education? And you'll do the same thing for algebra and the same thing for
048physics. Okay? So, you'll have about 2 minutes to do this as well. And you do not need to create new green, yellow, and red zones because we did that already in the previous section. >> You want to put this I can move up. It's hidden. >> Thank you. Yeah. All right. One more minute. Pleasure. Pleasure. Pleasure. Okay. So, what were some of your predictions? And remember, there are no right or wrong answers here. What were some of your predictions for each of the exams? Was was 79 the combined? >> Mhm. >> It was okay. >> Yeah. >> I predicted 85 for general ed and 55 for students with disability or ELA. >> Great. Thank you. >> Other predictions. So, I know my math doesn't add up. So, that's >> This is for me Julia here.
049She So, I just had for Yeah, I had 90 and uh 40 >> and 40. Okay, just keep in mind that those numbers don't necessarily have to add up because we're talking about all students that are general education and you're comparing the data versus all those students and then all students with disabilities and you're comparing the data against them. So, they won't necessarily add up. trying to figure out the percentage, you know, the >> do you know what percentage in statewide uh student general ed students versus uh students with disabilities is like are 80% of the students in New York State? >> That would be a good question to add to our wondering list diving into the data be a good one that we could continue to dig into. Got it. >> Absolutely. Okay, so
050let's move on here. So this is what the data looks like. So again, we can start with the low inference observations. What do you notice and what do you see? And then you can move on to phase two, which is examine the data and compare your predictions. What are you struck by and what are you surprised by? And if you look at the mini document again on the back, sorry, at the bottom of that uh pause section, there is some space for you to write there. And once you're done, feel free to talk to the people around you. still has to get right now. >> Right. >> And these two are below 50%. >> I was struck by algebra. Why his why his algebra percentage wise more >> than ELA would be >> I would
051expect students to have performed >> bigger dispar I would have expected close to >> you know 79 62 74 that farap is >> I would have expected there to be a higher percentage of doing I think >> it also made and I think I'm jumping ahead but makes you wonder you know what what are the disabilities >> I was very good in math not very good my skewed because being left out >> oh sorry feel like he belongs probably a good >> struggle. Yeah, definitely. >> All right, let's bring it back >> just as as real numbers. >> What are they >> What are some things >> I'm glad you're so engaged with this. This is great. Let's bring it back for a quick moment. >> So, so what are what are some of the
052observations that you're making in the discussions that you're having with your team? What are what are some of the things that you're sharing right now? >> Kim, we need your light. >> Sorry. >> All right. Who Who's not going TO >> What are some observations we're making? I I'm happy to see everyone so engaged with the data. We we were just struck by how well they were like especially on the students with disabilities because when we're talking about students disabilities there's a wide range of disabilities and you still have to pass algebra regions to get a diploma. >> So we're still requiring again this is why I go back to like what's the curriculum? How are we teaching because these numbers scare me actually >> that we're graduating students this way. >> Yeah. Thank you
053for sharing >> and and that's such an important question going back to our first question for the PLC process right what we want our students to know in identifying the priority standards that is right there the the focus right and and trying to help amplify it and and uh engage students in in their growth right so that's part of our PLC process that all of our collaborative teams are working on right now >> and sometimes it's useful I know that we were talking about why are we looking at these three exams together? But at the state level or even at a building level, it is useful sometimes to establish your red, yellow, and green zones and have all of the exams laid out because that's when you start seeing where the stark differences are and
054where you really need to focus in your attention. So in this case, let's say that there were only three regions, we would probably say >> we're going to need to focus in on algebra. >> Other observations? Some some schools teach to these tests and some schools >> don't put as much emphasis on that. >> Yeah. >> So that could be you know school to school that's a factor. >> Yeah. Those are all great comments. Again we're right now we're in the observation phase. >> That's great. >> Right. Okay, I'll I'll walk the fine line on this because part of it has to go back to the previous slide that we saw that >> you know the obser like the first one to all students where >> the observation was made that physics is an elective
055so therefore it makes sense that they score higher but here physics is an elective and there's still only 48% with students of disabilities. >> Yeah. Thank you for saying that. >> Yeah. >> Why is there that gap there if it's they're choosing to be in that class? >> Yeah. Yeah. Thank you. >> I was also I was surprised by the fact that the GenEd student scores track so closely. I would have expected to see the GenEd scores higher >> um than they were. I mean, they tracked pretty just a couple of points off from the original data, >> but I think some of that is the percent. I looked up just for that. Like for example, the algebra 1 in 20 June 2024, there were 223,000 students overall, 182,000 were gened, and 40,000 were students
056with disabilities. I think there's like the waiting to >> the numbers not being all. >> Yeah. But it is also interesting to see that none of these numbers, right, none of these groups of students are in our green zone that we originally established. So they're all kind of in our yellow watch zone. Any last observations? >> Uh yeah, I think for ELA that's a real disappointment. >> Um especially the fact that uh the students with disabilities fall into the >> zone establish. >> Yeah. Yeah. Thank you. Okay. So after we go through this data, our next step is in the process is to um take our initial student problem that we created before with David and expand it to note some performance gaps. So we filled in some of this information uh for you um
057and we're going to go ahead and share what an example of this could be. So we could say for instance 69% of general education students in New York state are scoring proficiently on the algebra one regions uh test as evidenced by the New York state report card while 35% of students with disabilities in New York state are scoring proficiently in the um algebra and regions exam. So that is um one example. Something else you might um you know want to come up with is for instance um in general ELA students are um 83% of general education students in ELA are uh scoring proficiently versus let's say 69% in algebra 1. So again every team is going to approach uh this problem of practice uh a little differently. So now that you've had a chance to
058look at the data and uh define a disagregated student problem, we're going to go ahead and again um develop some wonderings um develop any inferences that you have and any questions that you have at this point. So just for the sake of time, we're going to have you just kind of share and Jessica's going to go ahead and take some notes as you share. Don't be shy now. >> Feel like I don't have enough information to >> I do feel like we had a wondering though about I wonder if it's when students self- select if that has different test scores than >> when they're in a mandated course. >> I wonder if it matters what the disability is that the students have. Okay. the number of students that are in each test. >> How many
059students take each test? >> I wonder like how close were the kids who failed to passing? You know what I mean? Were there how many of those kids are >> on that test or were close? >> Yeah. >> On a cut score. that Bill and when you were speaking about like the methods the teaching methods or the curriculum that was wondering y >> if we were looking at sort of a smaller sample taking away from sort of a statewide look if we were looking at a different type of data that we might look at and say is there something is there a common thread for the students who are passing you know what what are some common characteristics of students who are not passing or versus passing is there something that >> thumbs up. >>
060Yeah. And to build on that, if you're at the state level, you might be asking yourself regionally, how does this fallact, right? So, is it it has implications for you at the state level supporting the various districts? >> Yeah. >> When we say proficiency, are we just because I that's the first time we just use the word pass or fail. >> Are are we actually saying you're proficient if you just get a 65? >> So, proficiency as defined by New York State are levels three, four, and five. >> Yeah. Mhm. >> And three is typically what is that? Is that a >> is that a 65? >> 65. A three is a 65. >> So it is kind of past fail. >> Yeah. >> And then there is data even on like each of those
061five category like you can see the breakdown of like how many students got a one two three four or five. kind of back to your >> Well, I guess that's my wondering then actually in those levels how many students were in a one two three >> right was kind of saying there >> you know that >> um a lot of times folks would kind of look at those broken down into now >> uh upper and lower and so you can start breaking those all down >> we're this close to getting that student to spill over to the next space >> so then that's where you'd have that conver so this is the part that the team this is where it really starts shifting to now getting feel like oh there's like this energy what can
062we do >> and so I know this is going to state level but even the short list we've got there you could start to see that whichever team has come together like this will kind of drive it down so >> yeah and at this point um as a team you might say well we need to go back and we need to collect more data um perhaps at a school level you might say okay well we have the state data but what do our classroom assessments look like what does the recent NWA or so on assessment look like um you know what what are our students grades like and and how does that look like if we disagregate it? >> Um and and you might say, well, you know what, we have enough enough data at
063this time to go ahead and take some action. So then you'll just move on to to the next step in the process and you might come back to this uh several times during during your cycles of learning. >> Like your wonderings get richer with the more data you're looking at, right? >> Absolutely. Yeah. I just want to also >> Yes. Um, so from here I do kind of want to bring us back to this 10,000 foot view. So one of the reasons that we really liked this key acronym, it honestly started with the shape and then we just dove into it because this beginning part we could cycle through that. we cycled through once but we have more wondering. So we might ultimately decide we want to cycle through again. We want to look at
064more data sets. We want to analyze this at a deeper level. Or what we could do is um go through that fork in the road which I'll get to. But this framework here is a framework that we're going to be doing with all of our staff and they're going to get into these deep data conversations along with uh their teams with data that's relevant to them. So for the purpose of tonight, we wanted to show you just that beginning part because ultimately we're can't take action on statewide data, but our teachers could use this protocol to have a unified way to look at data so that they can then take action within their classroom and their instruction really related to supporting all learners. So to kind of pull you through the right side of that
065key just really as a preview of what we do, uh within here we would start to look at verifying cause and effect. So we might start with that aspiration, that little thought bubble of like what do we want to see differently? We want students to improve their test scores maybe related to a certain subgroup and then with our collaborative teams teachers can talk through each of these six categories here. Our instructional practices, intervention strategies, um professional learning opportunities for what can we do differently to have an impact on this. So, just to show you an example of what that might look like for this specific data sets, these are some of the questions we might be walking through with our staff. So, I'll just pick one. Um, but what instructional strategies have been effective at
066developing content mastery with students from disabilities. Maybe we can help support a certain subgroup of students. Maybe we're doing strategies for all. But within here, we're going to start with that aspiration and that goal of increasing that proficiency rate. And then we're going to be talking through what can we do differently and walking through each of those sections. Um, from there, once our teachers have hashed that out, they'll be taking all of the data from all parts of their collaborative inquiry protocol and using that to develop a learning goal being a smart goal, smart, measurable, attainable, and coming up with the language of by a certain date, this is what we hope to see differently. And from there, we have our smart goal. will then be moving into an action plan. So, what are the
067things that I'm going to do differently? Maybe I have to do some new learning. Maybe I have to do more regular data monitoring within my class to help get to that aspiration. So, the document on the right here, which is also in your packets, um is just a document that we can use internally to memorialize those action plans. And then from there, once we've done the action plan, maybe we've met that goal that we had in our little aspiration bubble and that came into this learning goal here, we can then go back and repeat the whole process, which might include diving into this data set in a different way or even diving into other data sets to continue that data inquiry process and then be able to transform learning based on our findings. >> So,
068can I just add something real quick? I want to go back to this fishbone analysis for a quick moment because I do think it's really important. The way we created this process was to ensure that we're approaching this from an assetbased mindset. So rather than talking about the deficits of our students, we're really thinking about what can our students do and how do we work from there? What are the practices that we currently have in place and how do we build on those? um we feel that that is a much more effective way of talking about our kids and really moving um you know their their learning um as opposed to you know saying oh you know what what can't they do and and what is going wrong you know it's really what can they
069do and how do we move from there >> I have a question um this is really great and it's work that takes time right and so >> how help me visualize how we manage this for example if we're doing this work in a third grade setting, right? Um it's going to take time. You've tested them, you've benchmarked them, and you're going through, you know, this sort of process. Those kids are going to keep how do how does that all connect together as the kids are moving on from grade to grade because, you know, you're you're identifying a problem, but it might take time to come up with a solution. You might by the time you're ready to sort of retest and see what's going on. just help me picture what does this look like in
070an academic year? How does it connect to other grades as kids are progressing through the system? I I love this. I just want to have a better sense of what what it's going to look like. >> No, it's a great point and that's where that professional learning community, the professional learning teams meeting is so valuable because at a 10,000 foot view, we are want to come up with a multi-step process of training teachers on this. Right? So this presentation that you saw tonight might be slightly adapted but that'll be their first dive into the mini. We have a whole other adding a whole other layer that we add on with assetbased uh questions strengths opportunities and from there it would be a multi-series training. But then when they're meeting with their professional learning teams during
071that carved out time whether it's weekly or daily that's once they know the process they don't need us to walk them through it because then with their team they could say hey we just gave a math test on Friday. third grade, let's all sit down together and let's look at the data together. So, it's a way for them to really have those conversations and to maximize strengths so that at the end all students from all of those teachers have that same unified experience that wow, we looked at the data and saw that across all our classrooms if we made this change it might help. And >> I say at an elementary level like that example right now we're meeting weekly with the professional learning teams and I think it eventually will be a nimble tool
072that like kindergarten team might be in a very different place than the third grade team in where their unit cycles are or what assessments they've been given but like you're saying a formative assessment in the middle of the math unit. You could do this. It might be over one meeting. It might be over two meetings. Come up with a plan that you're the next day is going forward to address and that would be like so much more granular than this example. But like addressing those math standards, the following stand. >> So it can be used in in a much quicker. >> Yeah. >> I can give you a quick example for that. So let's say as a math teacher maybe in September, no, October as we're giving out the diagnostic assessment, this process is going
073to take a little bit longer because that assessment is a little bit bigger, right? We might be looking at the state data and other information right at the start of the year. But as we start going through this process, if I'm giving several formative assessments, skills based checks or whatever, I can bring in that information to a quick meeting. We can identify very quickly, well, where do we we expect our kids to be? Let's set our red, yellow, and green zones. and now let's quickly look through this data and now what do we do next? So it doesn't take as long as you get become more and more familiar with the process. In the beginning it will take a little bit more time because they're just learning it. >> I can also see how this
074would be useful from test period to test period. So say a team isn't happy with their scores how and they go through the wonderings of how can we improve the scores what different teaching strategies we can use. they implement one of the teaching strategies that they identify as potentially helpful and then they can go back and look did that help did that work you know if and if not what else could we change or so I can see how that helpful just from test to test >> or quiz to test seeing if what they're doing and we've actually got the science of education now and data that we can look at and so this is this is fantastic >> you know and Barbara Also, one thing that caught my attention that you said too about
075students transitioning into the next grade, right? So, within those collaborative teams, you have those conversations. Sorry, the Italian. I mean, when my hands are keeping um the conversations centered around that vertical alignment, right? So, thinking about how concepts and and skills are translating to that next level for for students. So, that's all part of of of this process as well. And one more thing to note is um if you look at that smart goal worksheet document that we have here that Jess went through, there is a target date and timeline. And typically we we try to do these within an extended period of time. So it's not we're doing this today and then we're doing it again in a week. It's you really need to try something over a period of time, identify how many
076times you're trying that strategy and implementing those interventions to really see whether or not something is working. Uh so it it is meant to be as a cycle. >> I I'll just add that there's a larger hope here too. Like so if you think back to the the ways the data has been used in education, right? I think back to like accountability and No Child Left Behind and and and it and it changing to like a gotcha and a and a thing that became punitive and teachers around the country started talking about children as numbers and levels, right? like and and having to move them and we we lost sight of the the child as a human in a to an extent and then also we we we everything became punitive. So then um it
077was very demoralizing for some folks involved, right? Um I was a special education teacher at the at at a time where it was very much like why would I be encouraged to teach high-risk high need populations if um I was going to be punished for like lack of what was perceived as achievement right so when we're having these more lengthy conversations shifting it to an assetbased language and doing it in a way that highlights it's asset so you're speaking about what do the children already succeed at and can excel in but it's also asset based for the teachers like what are you already doing well that you can enhance like where are you where is what you're doing working and growing that process and I think that will have much longer term benefits than the
078data talks of old where it's just like uh how are you going to do better like when you're you know 69 get it to 75 and not paying attention to what's causing that right >> yeah this is the appreciative inquiry in action right >> can I ask a little bit like what are the we talked a lot about the PLC's um it's a great model and you're saying they meet weekly like just how often are they meeting and what does that look like? That's hard on a union contract like what what does that look like and how do we >> I'll so sure >> I won't knock this one then I >> don't have the AT. So it's different for different levels and that's kind of where David was saying at an elementary level right
079now those teams are meeting roughly on a weekly basis and then at the middle school they're doing a couple times a week and at the high school right now with the community to lunch that a key shift of that allowed for uh daily touchston. >> Wow. >> And so that's a piece and um the clarity to be uh it is a struggle at the elementary level >> not just here at at every space. And so we're really tactically trying to look out how do we go ahead and empower our grade level leaders. So some of the work that they're doing can we do this? So really leaving this time as much as we can. Some of the other elements that are on that kind of flowchart like identifying priority standards. Those pieces are are going
080to be challenging for particularly elementary just because of the time constraints. Middle school and high school have a little bit more piece. So, we've we've got some thoughts and wonderings and we come step forward in the coming months and coming years how we might be able to assist that at the elementary. It's tough. >> It's not just here. It's it's every every space. You just don't, you know, you've got 5-year-old kids and stuff like that. So, you you can't pull students together for, you know, a group study hall or or have them be having community lunch and stuff. It just doesn't exist. And so, but again, what I've seen and what we've started to see here is that there is just like this evening, we were roughly 35 minutes into this work. I hope you
081felt the energy and the pieces that we're moving forward that um you could see where teachers are in the same spot. And again, this is that they'd be looking at with kids and they're in front of them and so they're saying, "What can I do to help support my kids?" >> That's a great model. >> Yeah. >> Should we have time? >> Yeah. And that's we're going to struggle. uh we're and you know we may come back and say here's do we have some flexibility just like last year we came forth and and Stephen and the team worked through that whole new schedule right key part of that was as we worked through with our partnership with our teaching staff was using that half hour a day to do these kind of works and again
082with the school at the high school they've been seeing that part uh of that work so how do we help the other schools >> and just to kind of close this out so ultimately for some of us we really like data but there is this history and education where sometimes it can feel like oh we're going to look at data. So our hope with this is that as we eventually roll this out with our teachers as a unified learning experience and then roll this out with new staff by taking this more appreciative lens people will be looking at data as a positive and something that can really impact their instruction and support learning for all students. So, if it's all right, be before we broke, um just maybe board if we can just bring on
083the table. Um if you were asked about your board meeting tonight and somebody, oh, clever inquiry and they asked you, well, what's the one thing that you might be able to share out with a community member about kind of the highlight about your experience tonight? You wouldn't mind. I'll >> sir. I'm excited that we're digging deep into data. Um, I think that's been a common thread through community conversations throughout the year is, you know, oh, the kids are doing great, but how do we prove it? And you're proving it in a in a science-based way and using tools that we use in the real world, too. I use fishbones every day. I use some of these smart goals, you know. So, I I kudos. This was a fantastic presentation and I would encourage everybody to
084go look at this to see the great work you're doing. >> Thank you. >> Anybody else have any things you'd tell your neighbors? >> I agree with Lorraine that we're looking at data and we're using it to better our kids uh education. >> And then just as we close I'm sorry. Were you going to see that? I was just going to say that we're creating processes to inspire our teachers and staff and bring out the best in them and looking at data in a way that's focused on positives, strengths to to continue to move our results forward. >> I mean, I I think I like the fact it was said before, what are our kids already doing? Let's look at it from the lens of how can we help promote what they're already doing well?
085Because I think that when students find themselves at a deficit, they always think about like, what did I do wrong, right? And they're they're afraid. You know, my daughter got the worst test score she's ever gotten and she came home and she said, "Well, that's it. My GPA is over." Right? And so I was like, "Well, no, that's not the way that works." And and so I really like this idea that we're going to use data to help inform our instruction for our kids as opposed to using data to say what our kids are doing wrong. or teachers for that matter. >> Yeah. >> And Arwin, if I just kind of pull on that a little bit, thinking about tying it back to the pillars or that idea of this process really thinking about um
086the idea of shifting from blame, >> right, >> to this idea about curiosity. Oh, there's so many great things. How could I help move that forward? So, just that little shift for the well-being of our kids, >> this idea about shifting from here to there. And then the other idea about just from a sense of of belonging and I think we saw it tonight very diverse perspectives and thoughts ideas and ro in and then working with the team to get in a spot like okay now where do we collectively think we should go next best. So the idea about belonging and having folks matter in those conversations and then now the ability for us to say okay now where are we going to go next in our work. So I >> think one of the
087tie-ins from the increase Miller tour to tonight was that when we looked at Hadtie's research, I think that's hard to explain to the public, but what I would share is that collective teacher efficacy is shown to be one of the biggest um improvers of student performance. And so what we're doing as a district is working together and data is one of the things we're working together on and helping to bond our teachers and look and and work more collaboratively. and that that's not just good for our teachers, it's it's what is best for our students. >> Thank you, Caroline. Ty. >> Yeah. >> And then just before this team leaves, so I did not introduce this team, but last year or four folks here, we had started the district data team, >> Esqu we we
088started that group last year. And the charge there really was to provide tools to our professional learning community teams to do the work. And so this, as you can see, where they've gotten us to now and then again where we're ready to just now getting closer to closer. We felt it was a pretty appropriate time. Again, we're right with our teachers, but now it's okay. Here's a tool teachers, so you can do that work. And as we continue to think about that, but I just wanted to thank my colleagues for being here tonight and starting this up last night, summer and other things. So, thank you very much, everybody. Really appreciate it. Thanks very much. Thanks for having something guys. So, uh, firstly, There's one part about this that I appreciated and that was a
089good step of this. I think that's really important to like see like what we think it should be before actually looking at it. Obviously shift. Um I did have a question. Uh do you guys see that this could be implemented like in teaching students this process as something that they could bring into like the real world and learning how to like look at data before they? Great question. And tomorrow I have a post- observation conference with one of my math teachers and I'm going to be showing him this because he was just doing a lesson on linear graphs with the students that I observed and ex excellent point because pieces of this can 100% be implemented uh in an activity for students in the classroom. So, thank you. >> I love your questions and I
090was so grateful you asked that because I'm I'm dying to know like as a student like this is all about trying to help students. How >> do you you're feeling good that teachers are going to be looking at student data like this? >> Yeah. I mean, uh, I I think it makes it like feel like a little more like, okay, it's not like just like planned out like every day you're doing this kind of, oh, it will shift if like people are falling behind or if everyone's doing great like teach something else about this. Uh, and I I do like >> awesome. Thanks for sharing your opinions. >> Thanks. Awesome. >> Yeah. >> Thanks again everybody. >> Thank you. >> Yeah. And I will say um you know you now have me thinking your question
091of you know how do we compare stateto state it's you know that is a it was a great question and now it has me thinking like how do how do we get that national data and because that's going to affect our college admissions and stuff. So I really appreciate all your contributions and you're always >> Thank you guys so much. >> Thank you very much. Have a good evening. Not not every state has the regions, right? Exclusive of quality of New York. >> It's one of the hard things when you look at at at K12 data is that there's not really a good I mean the SAT, you know, there's really not a lot of national data. >> I had no idea what regions were until my kids started high school. >> There is, but
092every state has its own >> test it own little test. >> Another plug for something like an NWA >> Yes. map. >> I was just going to say I feel like all the new assessments you have just you know by next year they'll have such good data >> and that's what's nice those are nationally normed >> across and multiple millions of children across the state have taken those and have historically taken those. So >> it does help. >> Well, thanks again. I really appreciate the flexibility board for letting us truly more of a workshop as it presents itself. So thank you. >> I would have liked to have seen that. >> You can't give up because I can't. Nobody can get it. It's like >> I would have liked to have seen that more granular
093not maybe like for our kids or for something because I think what Barbara was having a hard time conceptualizing is how do we run this through on a small level and that's because we they chose such a big thing right like the state test. Like, so I would have liked that to have been maybe like our math scores on a math test to show how it can actually cuz you're right, we use smart and corporate, right? So, how can we actually do this on a smaller level? I think that that would have been nice to see. >> I think you set us up for next year's presentation. >> Yep. Yet to come. >> I thought it was helpful. >> That would be fun. >> I thought it was helpful to focus on the process and
094not the data. >> Yeah. and and um even though I I had a I went through the presentation once beforehand and then went through it again. I was like, "Oh, I see. This is about the process. We're not looking I'm not looking at this data." >> No, I was just saying again from a granular level though, you can understand the process. You could I think that's what Barbara was trying to say like how do we do this on a long term if you looked at it at a smaller level, you could see how you could do it >> or >> just a test for next week's unit. >> Yeah. But I can see that with a group of math teachers getting if they all give the same test and 90% of the kids miss number
095two, then they're figuring out like how are they missing this concept at number two, >> right? >> So I I can see small level, >> right? >> Faculty like grabbing around and we're growing that muscle, right? Like as I said, like we still have to take this through with all the staff and and and so so point out to them that maybe they should do it granular. >> We I actually told them they couldn't. not on me. He's like process process process right now >> tool get it ready and then anything we should be able to cycle that data set through and again in the future we would be coming back and looking at some of our own information again with our with kind of our roles at the the kind of board level are
096not looking about um this idea about how do you get kids to grow and things just how do we check in to see how they're doing. So again, we walk through part of those pieces. We wouldn't get into the fishbone piece as we did in tonight. That's a piece, but we will come be coming in the future times to look at some of our own localized set as we move forward. So yes, it's a very nice uh kind of forecasting. So thank you. So we will be coming >> and also John and further to what you were saying it um you know if three of the four class classes you know on question two everybody got it right but that fourth class there was so there's a good place to share with the other teachers
097like how were you doing it differently and how can you improve it so not only it's just a good way to share different learning methods and and teaching methods >> and I I know that Julia and I we kind of did a brown bag of lunch a little bit earlier this year and you may or may not recall this though is tied into larger systems. So the idea what's called the strategic implementation guide was developed uh and continues to be adjusted by what's identified as our district guiding coalition. They're putting in structures for fresh learning communities. That's the principles from each building. We have an assistant principal in there. We have some teacher leaders in there. Basically this tool is going through that team. And once they kind of say, "Okay, we're set." Then it's
098a responsibility out in the field for that to be run and coordinated by the principles. Make sure that's happening in APs. Training comes from here. So it it's it's all clearly connected, but it doesn't just get released and saying, "Okay, so it's it has to go through proof of concept all the way through deliverables." And this has gone through many iterations. And now we're at a spot tonight where it's like, "Okay, we think we're ready to now start releasing that." And then we'll make adjustments along the way. But that really is how that process goes. And just as much as Julia is working with some of our other team who are looking at how do you identify a priority standard? What is a priority standard? So we have that common language across the organization. Once
099that's ready to go, the district guiding coalition will kind of give their headnot on that and then that'll go out and then the feedback comes back to that coalition on a monthly basis. Schools each have their own school guiding coalitions. So that dance back and forth comes that way. And so again, it's it's more about the structures and the system. So the process and then the work product we'll be looking for in the future is hey now let's go see some smart goals. Building principles are going to be looking at that information. Let's see the some of the learning results are happening in those classes. Let's see how the kids did on that assessment two weeks later and stuff. So So that's the general idea. I'm just I'm I'm glad you're staying there for a
100second because none of this existed before like I mean it existed in other formats. They casually mentioned we adapted Nancy Lo's >> um collaborative inquiry method which has has the bones of it but they built these tools and um you know we had a a dead fish for our fishbones. >> It's okay. I remember where Mallerie and I were sitting when we were like we need to make it not dead aspiring if it's going to be aspirations. And so like every shift, every graphic, the positioning of things like was an iteration of of a of something that has been growing as we're learning about it and simultaneously learning about all the other elements of this which is so each each bullet is its own um enormous amount of learning. So, it's a it's a real
101testament to just this >> um the dedication of these guys, the data team and and and um trying to make >> There you go. Exactly. We're bring it back full circle, but true. Yeah, it's it is um a work in progress. There's one thing I just don't want to miss and I know we probably have to move on but uh this is going to be great for outcomes but I don't want to miss the impact that this is going to have on culture and climate in terms of who we are as a district and how we operate and um how we work together the the sort of positive strengthsbased perspective collaborative approach sort of built into the the way that we operate. So I think outcomes fantastic but really wanted to underscore a little bit
102and and that comes from the leadership team in terms of how you guys there there are probably 20 if not more different ways that you could have proposed to have some sort of system in place to improve outcomes. You chose this and it's it's a very culture and climate setting um move and I really appreciate it. >> Thank you. Thank you. So we can do it again. Another workshop will be done. Great. >> This was fun. This was I enjoyed it. This was a lot of fun. >> You got to share the markers next time. >> Oh, good sh. >> Thank you, board. >> Thank you. >> Uh moving on to 7.02. Um somebody want to make a motion on the resolution regarding financing of the energy? Caroline >> seconded by John. Um I am
103informed that because of the nature of this that we need to take a roll call vote before we do that. Is there any discussion that anybody has about this contract? I think we've talked about it quite a bit and she's not here but I I would just be remiss to not say a huge thank you to to Lisa to her persistence and bringing this community and and exceeding the numbers she thought she was going to get. So this is amazing. Um so I will start. Does it does it matter? >> All right. Carolyn >> I John >> I >> motion carries 6. Thank you very much. >> I really wanted to say in favor though. >> Um as there is no one here for public comment. I'm going to close our 8.01. 01 public forum
104for the night. Um, next, can somebody want to make a motion to move the consent agenda? Motion by Bill, second by Arwin. Does anybody have any discussion points on the consent agenda? Nothing to pull out. Um, all those in favor? Hi 6 uh 14. Recognition of donations and gifts. We have a great donation tonight from Acme Markets Foundation's Nourishing Neighbors Program. Um they are donating 126 $25 gift cards for the amount of $3,150 um that will be used to benefit families um in in our community. Um does somebody want to make a motion to accept this gift? A motion by Barbara, second by Arwin. All those in favor? I 6. >> Thank you, Acme. >> Go Acme. Yeah. Um, that brings us to board dialogue and discussion. Anybody? >> I don't have something huge to
105say, but uh, as a testament to our student population and our administrators, I know of a family whose daughter recently found out about those trees in the mall where you could pick gifts to for a family. and she had this wonderful idea to actually do one for senior citizens at a lowincome nursing home. This is one of our students. She wanted to do this all by herself. She reached out to Dr. Sicciliano and he said the school would be very on board for doing this with you. put her in touch with Kim and and they're working on it together and it's just these are the students that are in our schools and this is the administration that we have and it was just such a heartfelt story that I >> have goosebumps. I swear like
106I'm actually tearing as I'm telling you the story >> that's amazing. >> Yeah. top that >> maybe I can't top it. I It is the season, right? Um, we're coming up on Thanksgiving and I just want our administration, Kim, our faculty, our staff, monitors, bus drivers, just to know how grateful we are for the work that you put in every day because we wouldn't be needing to be sitting here if it weren't for you taking care of our children. So, um, just thank you from the bottom of our hearts. Everything that we've seen this week, everything that we've seen this year, um it's we are going places. What's the name, Dr. Seuss? We are going places. >> Places we will go. >> The places we will go. Thank you. And um I'm just grateful to
107be part of this community and everything that the community does to support um everything in the district. Um, with that, if nobody else has anything to add, uh, we will look at future meetings >> tomorrow morning, November. Oh, no. Well, tomorrow at noon. >> Sorry, I'm used to it being in the morning. Uh, the policy committee meeting, policy committee meeting will be held in the district office at noon. Um, December 5, I can't believe we're saying December, but Friday, December 5th, the sustainability committee meeting will be held at 8:15 in the district office. The finance committee will be meeting on December 10 in the district office at 6:00 p.m. And then we'll be back here again on Thursday, December 18th at 700 p.m. um for our board of education meeting. Um, and with that I
108turn >> I just wanted to say thank you again for the the time time tonight with the team and then same echo just uh hope you all have a good uh Thanksgiving and enjoy some family times. Thank you. >> Um I'll make a motion to adjurnn. Do I have a second? Second by Arwin. All those in favor? 6. Thank you very much. >> Happy Thanksgiving everybody. It's a long time till December 8th.