Lisa Le Fevre: Thank you, Mandilee. Welcome everybody. Welcome again to our session Using Data to Increase Equity in Education. We're here from WestEd. WestEd's an educational research, technical assistance, and development organization. I'm going to do some quick introductions. But as our team introduces ourselves, please take a moment to introduce yourselves in the chat with your name, your role, and the organization you are with.

So I'm Lisa Le Fevre. I'm a senior program associate. And I'm going to let the rest of our team introduce themselves, starting with our presenters.

Jessica Keach: Thanks, Lisa. Hello, everyone. My name is Jessica Keach. My pronouns are she/her, and I am a senior research associate with WestEd I'll turn it over to Mari.

Mari MacNeill: Hi, all. My name is Mari MacNeill. My pronouns are she/her, and I'm a research associate with WestEd.

Lisa Le Fevre: And Margaret.

Margarete Lee: Hello. I'm Margaret Lee, operations coordinator with WestEd.

Lisa Le Fevre: Great. Thank you. And of course, I want to take a moment, I'd love to pass it to Mayra who's going to do a quick welcome note from our partners here.

Mayra Diaz: Hi. Good afternoon, everyone. We're happy to be here. And my name is Mayra Diaz, program lead with the California Community College Chancellor's Office. I'm the program lead for the adult education program. I'm joined here with a few of my colleagues. I'll let them introduce themselves. Cora.

Cora Rainey: All right. Thank you. Hey, everyone. My name is Cora Rainey, and I am the program manager for the adult education program at the Chancellor's Office. Usually I'm working in the background so it's nice to be able to partake in this session today. And I will turn it over to Neil from CDE.

Neil Kelly: Thanks, Cora. Neil Kelly, California Department of Education working on adult education for CDE. And with me is my colleague Diana Batista.

Diana Batista: Good afternoon, everyone. Welcome. Happy to be here.

Lisa Le Fevre: Thank you so much, everyone. So as we move forward, let's quickly review the agenda. We're going to cover several items. We're going to be looking at setting the context for equity in adult education, reviewing and exploring a Data Equity Framework, as well as a equity walk overview. Of course, we're going to give everybody a moment to stretch. We'll take a five-minute break. And then we'll move into using publicly available data tools. And there will be a moment for you to really then dig in, practice your own Data Equity Walk taking a look at the process.

Again, it's an action-packed agenda. Some information may be new, some may not. But however, this opportunity really allows us to engage with one another, share our thinking, our experiences, and our expertise. We hope that we can be mindful of one another's experiences, our knowledge bases, perspectives as we move through the session. Of course, we are giving a break but take a break as you need to. Know that we will have that established five minutes about halfway through the presentation.

We do request if you can to please have your cameras on. Again, if you are able to do so. And please feel free to post questions in the chat and come off mute throughout the session. We do see this workshop as both yours and ours as part of a learning experience. And finally, please use the link on the screen to access materials for today's session. This link will be shared many times today. And know that our resources we walk through are available to you to use. You can refer back to them today after today's session. Again, thank you. And with that, I'm passing it over to Jessica.

Jessica Keach: Thanks so much, Lisa. So we have four primary goals for today's session. Our first goal is to define and operationalize equity in adult education. That's our setting the context piece. We also would love for you to become familiar with an equity framework that can help guide you through any data project, big or small. We hope that you're able to learn how to use publicly available tools and resources. And we're also going to practice a strategy called a Data Equity Walk that you're going to be able to use in your own institutions and in your own consortiums to really explore issues of equity in your work.

Here is an overview of the resources-- that link that Lisa mentioned. So this is an overview of the resources you're going to find in that folder. Most importantly, I want to point out the three resources: the Data Equity Walk design checklist, the Data Equity Walk Excel template, and the Data Equity Walk slide template. These are resources that you're going to get to practice with at the end of today's session.

So now I'm going to turn it over to Mari and we're going to transition into the first part of our workshop and really start talking about equity. So Mari is going to walk us through our setting the context exercise, and I'll turn it over to her.

Mari MacNeill: All right. Thanks so much, Jessica. And hi again, everyone. Before we get started on talking about equity, I want to acknowledge that at times conversations around equity can be unfamiliar and uncomfortable. But because we've all opted in to being here today, I know that this group is ready to step outside of comfort zones. And ultimately, the goal is to be able to learn from one another and walk away with a better understanding of strategies and tools that can be used to-- can be used to facilitate conversations around data equity outside of this space. Thank you, Jessica.

So can I get a show of hands, either in the chat or using the reactions, if you've seen this graphic or something similar? Seeing few hands. Awesome. Great. If anyone would like to share, what do you see from this graphic or what these visualizations mean to you. You can share in the chat or unmute at this time.

Right. I gave it the three seconds of silence, so I'm going to go ahead and share what I see. So in these data visualizations-- or in these visualizations, the tree represents a system. In our case, that may be the adult education system but it could also be healthcare, affordable and healthy foods, higher education, or K-12.

On the far left, you can see that two people have unequal access to a particular system, which is represented by a tree that provides fruit. You can also see that the tree is curving left. And while a tree may naturally curve left in nature, it's important for us to recognize that our systems are human-made.

The second visualization attempts to solve the inequality through equal access to resources. However, the person on the left didn't need a ladder to access the fruits of the tree and the person on the right didn't get a ladder that was tall enough to reach the fruit. Equity is represented in the third visualization where the two folks are provided with specific ladders or resources that they need to reach the fruits of the apple tree.

And while equity is a solution for addressing unequal systems, justice can take equity one step further by fixing the system in a way that leads to long-term sustainable, equitable access. This is the long game. You can see that the tree has been permanently adjusted so that each individual has equal access to the same ladder and there's an even amount of apples distributed across the tree.

In conversations with other groups, it has been pointed out that this graphic does not fully represent equity nor justice as not everyone is able to climb a ladder. So it's interesting to think, how can we adjust this graphic to be more inclusive?

Jessica Keach: Mari, we had some great responses in the chat around what equity means to folks in their work. And Kathy said, meeting students where they are. Grace said, multiple pathways to reach the goal is justice. And then Wendy shared that equity means providing additional support to those who need it to truly level the playing field.

Mari MacNeill: Those are some great conclusions from the graphic. Thank you guys for sharing. Go ahead and move forward. OK.

So in our work with the field, we've done collaborative brainstorming to help uncover what equity means to you, practitioners and service providers in adult education. So what you see on the screen are responses from your colleagues to the question, what does equity mean in adult education? As I read off some of these responses, I'm going to ask that you tell us what equity means in adult education using the chat feature once again. So go ahead and enter in the chat what equity in adult education means to you.

So when I'm looking at these little Post-It notes, access is a major theme in these responses, whether that's physical access to classes or making sure the community has access to the knowledge of what adult education can offer. Another theme is supporting students and meeting them where they're at in life rather than having them tailor their lives to their education as we discussed before.

Give it another minute in case folks are furiously getting their thoughts down. Oh, yes. And feel free to come off of mute as well.

Jessica Keach: And I see-- I do see some familiar faces and names. So please don't make us call on you. It's going to be much better if we can all just engage. We just want to work together and hear what your thoughts are. We go, OK, we have someone in the chat, thinking through how appropriate access to the education and training resources is provided and measured. It's great. I love that measured piece since we're going to be really talking about data today. Anyone have any other thoughts about equity specifically in adult education?

All right. Grace. I see Grace put in the chat, equity in adult education means systems design that is geared around students' needs rather than around what is easiest for those in power. There are conflicts however in achieving this because some requirements outside of y'all's control are not aligned to the needs of students, which ties our hands. Thank you for sharing that.

Trichel, I'm so glad you're here, collecting student feedback on processes and procedures. That's really great. That's another piece of-- key piece of data. We often think of just numbers in a dashboard, enrollment numbers, but really capturing that qualitative information and student feedback on how our systems are serving them is a critical piece of that picture. Lisa, student voices. Yes, we always want to capture student voices. All right. Thank you all so much.

Mari MacNeill: Thank you all so much for sharing and participating. I'm going to go ahead and pass it back to Jessica for the next portion of our training.

Jessica Keach: All right. Thank you all for engaging in that conversation. Now we're going to turn to a framework that we can use to ensure equity specifically in our data projects. So it's your turn again. We know there are so many data sources out there. We have CASAS TOPSpro, the LaunchBoard, NRS, your institutional data. And we're going to look at some other publicly available data sources today later in this session.

We'd love for you to take a moment and let us and your colleagues know in the chat how you currently use data in your work. So that could be data sources that you use, how you use them. And also feel free to let us know if you have any ideas of how you would like to use data in your work. And I'll pause here. And again, feel free to come off mute if that's easier for you rather than putting it in the chat. We really want this to be kind of an open conversation. So how do you currently use data? What are your sources?

All right. I'm going to switch it up. OK, there we go. TOPSpro and MIS. That feeds into COMIS. So how about this, a show of hands how many of you use TOPSpro data in your work? Maybe a thumbs up or a show of hands. OK, we've got Pamela. Hands up. Hands up. There we go. OK. So we know that least some of you are using that TOPSpro data you can get out of there that kind of live data in your system tracking your students.

What about LaunchBoard? How many of you all use the Adult Education Pipeline? OK, we've got some thumbs up, thumbs up. Great, Trichel. So we have a few folks that are using AEP. What about community data? How many of you all have used data from the American Community Survey or the census? OK, good. We have some folks who have experience with the census as well.

And then what about-- how many of you-- I'm going to ask for a thumbs up or show of hands. How many of you would like to use data more in your work? Few of you. OK. All right. Good. We're getting some thumbs up. This is really great. OK. I'm seeing me, me, me. Perfect. So we're all here today. OK. So we're using TOPSpro, not familiar with LaunchBoard. OK, the backwards looking of the Adult Education Pipeline can be a challenge.

Grace, you did an exercise in your consortium where we looked at census data compared to adult school enrollment. Perfect. Analytics from the-- oh, analytics from a website. That's really interesting, seeing how many folks might be visiting your website for course enrollment. All about data. Real time is best if possible. This is great. With current more quickly Yes, we all wish that, Kathy. That is the trick. Sometimes with some data it can be really backwards looking, and that's where some of that piece of the student voice can come in. OK. Awesome.

All right. So this is perfect. So now we know a little bit more about the different types of data that folks use. Let's talk about that framework. So the Data Equity Framework. So the Data Equity Framework recognizes that there's really this increasing momentum around using data, visualization and even big data with machine learning techniques and AI to drive decision-making. How many of you have seen the AI chat bots recording our meeting minutes and those kinds of things? Data is here. It's with us and it's growing faster than ever. But there is also a corresponding movement for more responsible use of that data.

And in this part of our session, I want to introduce a tangible seven-stage framework, seven steps, that we can apply to any data project, big or small, to help us ensure that we're using data responsibly, transparently, and equitably. So the Data Equity Framework was designed by an organization called We All Count. That link is at the bottom of this screen. And it's really a systematic way of moving through data projects and it organizes every project into seven stages. So we're going to walk through that right now.

So these seven key stages-- and along with each stage are principles and practices, and they can help anyone who works with data. You do not have to be a researcher and analyst. This is a really broad and for anyone who's working with data to really approach that project through a lens of justice, equity, and inclusivity.

So step one is really about funding. So what is the relationship between data, money, and power in your project? It's always there. It's the society that we live in. And really understanding that relationship and just acknowledging where those ties are can be really helpful and really transparent and build a lot of trust in your work. So it's about understanding and acknowledging that relationship between data, money, and power in your project. And we're going to apply this framework in an activity a little later so these are the broad principles.

OK. So we have funding. Motivation is our next stage. So what are the goals of your project? You want to really define your goals. What do you want to get out of your project? Project design is all about the process that will be used to achieve your project's goals.

So examples in this stage can be thinking about your methodology, which data you're going to use crafting unbiased research questions. And we're going to go through a process called a Data Equity Walk and we're going to give you the tools to understand what process you would go through to design and facilitate that activity at your own institution.

All right, step four. Step four is about the data sources that you use and setting equity standards for how, when, and where you get data. Again, someone's already said this. It's about making sure that voices are represented in the data. That can come from both qualitative and quantitative information. It's really being intentional and transparent about the sources of your data.

This next stage, step five, is about the choices you make in the review and visualization of your data. So you want to explore and be transparent about those choices that you've made. What groups are you disaggregating by? How are you making those decisions? It's really about transparency.

And then interpretation. Again, this is about being transparent about your work through open conversation and a shared framework. You really don't want to have just one person sitting down and interpreting the data and then sharing that out. It's really more equitable and more beneficial for everyone involved if you engage around the data and have open conversation with your colleagues, with students, with folks that you work with so you can kind of have this open and shared framework.

And then lastly, the seventh step is around communication and distribution. So you want to create a dissemination plan and next steps for a wide range of audiences, making sure that it's not just a few at the top who get access to this data, but that it's folks all around because we know that it's not just those at the top who are making decisions that are impacting students. It's students are engaged as well. So we really want to be transparent and share data and our findings with a wide array of audiences. So again, this framework works with any project, including traditional research projects as well as facilitation or planning that involves data.

So now that we have this framework for any project that involves data, we're going to walk through a specific type of data activity that you can apply at your individual schools or with your consortium members to facilitate conversations around data and equity. And this practice is called a Data Equity Walk.

So what is a Data Equity Walk? This process, along with a customizable toolkit, was originally developed by Education Trust-West. It's a facilitated activity to engage with-- engage with data and discuss equity issues. You can think of this as a gallery walk with data as your art. We all know that visuals paint a really powerful picture.

And a key part of this process is disaggregating data to examine differences between groups of students. And really importantly, it offers participants a chance to explore the data individually before collectively discussing observations. So it allows folks to really reflect and sit with the data in their own understanding and reflections before you come together as a group and share collaboratively.

And then finally, what I really love most about this process is that the Data Equity Walk participation it doesn't require prior experience with data and it's really geared towards all audiences.

So here's another moment. I would love to know, now that what a Data Equity Walk is, can you let us know in the chat or by a show of hands, how many of you have ever participated in a Data Equity Walk? So give me a thumbs up or let us know in the chat. We have one person at least who's participated in a Data Equity Walk before. Another. All right. It's looking like this may be new to some of you, which is great. OK, folks have not. Not participated. Great. I'm so glad that you all are here. So we have a lot of newbies and some that have participated. OK, that's great.

So thinking about this activity, right now that what it is, even if you haven't participated in it, where do you think an activity like this might be useful? Feel free to drop that in the chat or come off mute. Can you think of any instances where reviewing data collectively through this kind of gallery walk might be useful?

Lisa Le Fevre: Sorry, Jessica. Identifying gaps came up there.

Jessica Keach: Yeah. Anyone else? Can you think of-- OK, learning communities, reviewing trends, helping you identify gaps. OK, at a consortium meeting, maybe a conference topic. Learning communities. Oh, this is great. Program planning, identifying strengths, staff meetings. It's great marketing. This is really great. These are all incredible ideas and we hope that today, after you end the session, you will have the tools that you need to take this practice back to your own institutions and really implement this Data Equity Walk in all of these contexts.

OK, another person agreed marketing as well. Another example, and this was mentioned, planning. Another example of where you can use the Data Equity Walk is during CAEP three-year planning. So this is guidance from the 2022 to '25 three-year planning guidance document. There are two sections that I really felt a Data Equity Walk could fit perfectly in your collaborative review of data.

Section 2 is that assessment piece. So when you're gathering data needed to describe your existing services, identifying those gaps and determining where more resources could be effective and really used in the community. And then section 3 was around metrics. So looking at data to identify barriers and metrics to be addressed by the consortium and really specific targets to be reached. So this process that you're going to go through could really be a space to reflect on the data that currently exists.

OK. So as a group, we are going to model the Data Equity Walk process so you can get familiar with implementing it at your own institutions. And to do that, we're going to imagine that we are all colleagues and we want to conduct a Data Equity Walk as a part of the assessment phase of CAEP three-year planning. The purpose of this project is three-fold. We want to review available data on programs and services, we want to capture insights on what the data shows, and we want to use those reflections to inform next steps for three-year planning.

So how can we apply the Data Equity Framework to this project that we want to take on, this data project? So first, let's talk about funding. So it's really about transparently acknowledging where funds are related to the data in this project. We might be using data that we use for CAEP reporting or WIOA funding-- or WIOA data that we report for WIOA funding. We might be using our own institutional data, other sources.

Folks mentioned looking at data from analytics from the CAEP website, looking at maybe how many folks are accessing the website to learn about courses and things that are offered. So a whole host of different sources of funding relate to the data that you're going to look at. And all this is, is about being open and transparent.

The next stage of the Data Equity Framework for our project is motivation. Now we've already done this. We identified those three goals of our project on the previous slide. So again, we want to review available data on our programs and services, we want to capture insights, and then we want to use those reflections to inform next steps for three-year planning.

Now project design we're going to put a pin in because we're going to have time later in the session for you to design your own Data Equity Walk and we have resources to support you in doing that.

Stage four is all about the data sources that we'll use. So in your Data Equity Walk today and our practice activity, you're going to look at data from the Adult Education Pipeline. But in your own equity walks, you can look at whatever data is most relevant to you. If you have real live data, that's great and you can use that data in your Data Equity Walks, but we want to be open about the data that we are using in those choices that we're making, including are we using qualitative, quantitative data, a combination of sources.

The next stage is about what choices are made in the review and visualization of the data. So as you walk through this Data Equity Walk, we want you to notice a critical eye. What choices were made about the display of this data? And you'll notice when-- you'll start to realize that when you look at any chart or any visualization, you notice that a choice was made to display this particular information. And so it's just about being transparent and noticing that.

And then the sixth step around interpretation, we're actually going to engage in collaborative interpretation through this process. Collaborative interpretation is built in to the design of a Data Equity Walk. And then lastly in this project, we determine next steps together as a project team, so really that collaboration again.

So today's Data Equity Walk, we're going to look at www.learner.org in California. Again, like I mentioned, the data comes from the Adult Education Pipeline. And we are going to explore data on access, so the number of reportable individuals over time, and we're going to look at some earnings data. We're going to look at median annual earnings.

Again, remember this is a virtual gallery walk. What we're going to ask you to do, this is an overview of the activity, we're going to ask you to independently visit this link and review the data on each Jamboard slide. So you're going to start with slide one and you're going to reflect. We're going to ask you to spend about two to three minutes reflecting on what you observe. We're going to ask that you record those observations and those reflections on sticky notes on the Jamboard.

And then you'll move to the next slide. And you'll repeat this until you've gotten through all of the slides. And we're just going to ask you to be mindful of time but also invite you to move at your own pace. And once we've spent about 10 minutes doing this, doing this reflection, we'll come back together and have some group discussion and review the reflections and summarize out.

I'm also going to share before we do that and then I promise we're going to get you to your activity. I want to share on the right a mindset. So Mari mentioned earlier sometimes conversations around equity can be uncomfortable and challenging, especially if we haven't engaged in them before. In order to set the context for Data Equity Walk, we really often ask folks to approach the data with a particular mindset. And you can feel free to take this mindset and use it in your own work.

So the mindset that we're going to ask you to engage in today is as we reflect, let's remind ourselves to frame our thoughts and our inquiry around the systems and programs that exist to serve adult learners. And it's really important that we continue to remind ourselves not to attribute data to the intrinsic qualities of people or groups. So try to just observe what you see in the data and not assign meaning to it just yet. OK.

I'm going to ask if there are any questions. And I believe Margaret has put the Jamboard link in the chat. OK, I'm seeing a yes. So please feel free to access the Data Equity Walk Jamboard. It's about 2:32 so we're going to give you until 2:42, 10 minutes, to reflect. And you're going to want to think about four questions, and these are listed in the Jamboard slides. What do you notice? What data resonates with you? What data surprises you? And what do you want to know more about?

OK. Are there any questions before we start? OK. I will bring us back together at 2:42. So feel free to go off camera if you'd like.

All right. Thank you, everyone. I'm going to ask that you come back to us in the group and we're going to take some time to split into breakout rooms. So now that we've done that individual reflection component of the Data Equity Walk, we're going to move into small groups. We'll do that for about five minutes to review the comments on the slides, and you'll do that collaboratively with your group members. So we ask that you reflect and create a short summary of observations that you can share back with the group.

So again, we'll put you into groups. You'll review the comments on the slides. It's up to you if you'd like to review across the slides or if you would like to focus on just one slide and look at those reflections, but we'll ask you to talk about it and notice what folks have-- other folks have noticed and share with one another and identify someone in your group that can come back to the main group and share a summary of your findings.

So with that, I'm going to ask that Mandilee please start moving folks. You're going to be put into random breakout rooms.

Mandilee: Rooms are open so you should see an invitation. All right, everyone should be slamming back in right now.

Kelly: I pushed the wrong button. Sorry.

Mandilee: Oh, no.

Kelly: I don't know. My finger just-- doesn't work. Can you send me back? I'm sorry.

Jessica Keach: No, you're OK. We closed the rooms and everyone was pushed back into the main room.

Kelly: That's what happened. I thought I pushed a button. OK.

Mandilee: Not you, me.

Jessica Keach: It wasn't you. Yes, welcome back, everyone. Welcome back to the main room. I'm going to give folks a few more seconds as they kind of trickle in. OK. So now that you've had some time to chat with one another and reflect in your groups, I'm going to ask for a representative from a few of the different groups to share out any of the main things that stood out to you or that you'd like to ask or know more about. So I'm going to ask for a few volunteers. Feel free to just come off mute and share what your group talked about.

Grace: OK. I guess I'll be the first victim in the deep end.

Jessica Keach: Thank you, Grace.

Grace: The uncomfortable silence, right? The wait time. So I was in group 3 and we weren't sure exactly what we were supposed to look at. We were supposed to look at all of them but we thought maybe we were supposed to look at the one that corresponded with our group number. So we looked at slide 3, which was about gender enrollment. And we had a lot of questions like we didn't make any big ahas but we wanted to know what programs were these people in? Why were we seeing this gender gap?

Is it because there were more-- there was more employment for males that didn't require a high school diploma, for example, so maybe that's why they're lower enrolled in adult ed because they don't necessarily need it to be able to get a job but maybe women do. So we were like-- we had a lot of questions. We were talking about how did childcare factor into this? We thought about the various different CTE programs and how they tend to lean in one direction or another based on gender and how that might influence that.

So mostly we just had a lot of questions that we would want more data behind the scenes to really start to dig into that gender gap to see what's going on there. And maybe it's not a problem. I'm not saying that it's a problem but just like knowing what's kind of behind those numbers.

Jessica Keach: That is perfect. And I'm so glad that you shared that you had more questions than answers because that's often what data does. And this activity often really is used to point you in a certain direction to go collect more information or to dig a little deeper into what you're seeing. So great observations and particularly resonate with the different program area breakdown. So looking at data by different program areas could be a really helpful disaggregation in some of the data that you've seen.

OK, anyone else? Would anyone else like to share their thoughts in their group discussion?

Trichel: I can share for group two.

Jessica Keach: Yes. Thank you, Trichel.

Trichel: So we kind of breezed through all of them to really think about what were the biggest questions that we noticed from those particular slides. And for slide number one, I'll talk about the obvious elephant in the room, which was, of course, COVID. Shock and all that enrollment would drop. But what's interesting about this is that it really brings home the fact that many people may be unaware that the enrollments were dropping pre-pandemic.

And so the question was like, well, why did that happen? And I posited that we must think of a certain election that happened during that time and that people were genuinely fearing for their lives to go into educational institutions where they were being swept up by ICE and everything else and their children were being swept up as well too. And we would be curious and be interesting to look at the K through 12 enrollments in comparison to these.

And then also the enrollment-- I mean, the numbers for individuals in California period, was that same decline and uptick as well too or do we see that that's staying steady and the bees themselves have only dropped as well too? So that's what we saw for particular for slide number one.

Slide number two, one thing that we noticed which was just weirdly interesting was that if you look at all of the race and ethnicities, the only one that has not begun to pop back up again is the Filipinos. And the question as to I wonder why that is was a question that we had for that one.

And then for the gender, nothing really shocking about any of this information here as well too, but we were interesting to see that in the future how non-binary students begin to enter into the conversation because, of course, this time we didn't collect it so we don't know. And so now that we are beginning to collect it, what will that look like in comparison to others as well too moving forward?

Slide number four, their work-- we said that we weren't too surprised at the different disparities of those by race and ethnicity. One thing that I had said that I've observed, I have family, a lot of family, a ridiculous amount of family who was in healthcare. And there is a very, very, very, very, very, very, very, very large population of Filipino workers in the healthcare industry. And healthcare workers in general have good access to higher paying jobs in the healthcare industry. And that could be part of why we see that the Filipino students who are here are making above that living wage as far as the average.

And then the fifth one, again, nothing surprising there. The wage gap has always been a thing, will continue to be a thing until some people maybe die and get out of the government and then we can make other things happen. So thank you. That's what we have to say.

Jessica Keach: Thank you, Trichel. Thank you so much for sharing. I really resonated with your comments about bringing in additional data points to I wonder-- I see what's happening here in this data. I wonder what other data is telling us about the context?

So I really resonated with that and I think that as we move through the day and as you think of designing your own Data Equity Walks, you all have the freedom to choose and seek out some of those additional data sources when you're the builders behind the Data Equity Walks versus just being a participant. So I look forward to hearing more about what decisions you all make. OK.

So this is a brief slide. I'm not going to go through it in depth but these are resources for facilitating conversations at your own institution. So the first one is the kind of standard Data Equity Walk toolkit from Ed Trust-West. In your drive and that link, the bitly link that we've shared, there is a template specifically for adult education providers in CAEP consortia members.

So the template is based on or the Data Equity Walk that you just saw is based and was designed by the template that's in that drive so you're able to freely use that and access it and update it with your own information. And then this last resource is a link to ground rules and tools for facilitating some of these discussions around diversity and equity.

OK. We promise-- we've been-- we promised a break. We've been together for a little over an hour now. So I'm going to invite you all if you need to get a drink of water, take a bio break, please feel free to do that. We are going to come back together as a group. Let's be back here to learn about the publicly available tools and walk through some demonstrations of tools you can access on your own. Let's come back at 3:06. So you'll have about two to three minutes. So if we're all back together by 3:06, that would be great.

All right, it is 3:06 so I'm going to ask you all to come back to our group and we're going to talk about some publicly available data tools and resources. OK. We're going to start with the Adult Education Pipeline. So we know that from our question earlier, we know that some of you are familiar with AEP and some of you have never used it. So let's dive right in.

The Adult Education Pipeline is a publicly available tool in a tool called the LaunchBoard, and it merges data from the Chancellor's Office MIS system with student enrollment and outcome data from CASAS TOPSpro to really provide a comprehensive picture of adult education students from both noncredit community colleges as well as K-12 adult schools across California.

Its purpose is to improve educational practice and economic mobility for adult learners. It's to provide that complete source, merging those two data systems to provide that comprehensive look at adult education in California. And then also to provide data for consortia and institutions to develop and track progress on their three-year plans and annual updates that are submitted to the state under the CAEP program.

What you're looking at is a snapshot of the Adult Education Pipeline's homepage. It's also often referred to as AEP. So if I say AEP, it's the same thing as the Adult Education Pipeline. You can see that there are filters for location and institution across the top as well as for year. And then there's what we call tiles that represent a grouping of metrics. So this includes a tile for the AEP scorecard, which are key metrics identified in the Measuring Our Success report and provided to the legislature every year.

There's a tile for students and programs. This is where you find information on reportable individuals overall by program area and by different demographic groups. There is a section around progress. This includes measures like EFL gains and immigrant integration. The next one is transition. So this includes metrics around transition to ASE, so transitioning from or ABE or ESL to ASE as well as transition to post-secondary or transition to CTE programs.

Success includes metrics on earning a high school diploma or a GED as well as the high school equivalency. And there's also information around the number of adult learners who obtain a post-secondary credential. And finally, there is a section around employment and earnings. So this should start to feel a little familiar. The data that we just walked through in our equity walk came from the Adult Education Pipeline.

So who is in the Adult Education Pipeline? These definitions are likely very familiar to you all but I'm going to revisit the types of students that appear in AEP under the CAEP program. So reportable individuals. This is the universe of students that are included in the dashboard. And these are students who have at least one hour in an adult education program or have received a service. Participants are students that have earned 12 or more hours in adult education programming. And importantly, outcomes are only tracked for participants.

And then on the right, you can see that students fall into one of the CAEP program areas. And this is how they're organized in AEP. So you have ESL students, students and adult basic education, ABE, adult secondary education students, so students pursuing a high school diploma or high school equivalency, and you have your career education students, which includes workforce preparation, formerly referred to as workforce re-entry, pre-apprenticeship, and short term CTE. Then you also have programs for adults with disabilities and programs for folks training to support child school success.

So what I'm going to do now is I'm going to take a few minutes to do a live demonstration on how to find some of the data that you just saw in the Data Equity Walk. So we're going to look for and find the number of reportable individuals in our regions or whatever location you choose. So I'm going to invite you to follow along. So I believe that Margaret is going to drop a link in the chat while I switch my screen. Here we go.

All right. So this is a look at the LaunchBoard home page. I'm going to invite you to follow along with me so you can see how to access your reportable individuals. So I'm going to visit the Adult Education Pipeline. You can see I have filters across the top. I've navigated to the home screen. You can see the different tiles that we just reviewed and our filters across the top.

So I'm going to start by selecting a location, and I'll actually select a region. So I'm going to click on this radio button next to region. And then I'll start typing and I'm going to select the San Diego Imperial region. That's where I live. Another option would be to select a consortia. So you can start looking-- you can start typing and things will pop up based on your search terms. But right now, I'm going to go ahead and stick to San Diego. I invite you to search for your region or your consortium, whatever you prefer.

So now that I've selected my region, I need to click View. It's going to switch or apply my filters. So now I know I'm looking at the home page for San Diego and Imperial. Now our number of reportable individuals, if you remember, that's in our students and programs tile. So I'm going to go ahead and click in to students and programs. So I clicked on that blue button, View students and programs. It takes me to a summary page.

The most helpful information in AEP actually lives in the detailed data pages. So I've been taken to a summary page. I want to click a level deeper into detailed data. So now you should see this kind of chart that groups together a variety of metrics. And on the left hand side, you will see your metrics. So this is what we kind of call the left navigation panel. And these are your metrics. This will be the same for every data tile.

So I'm going to go ahead and click on reportable individuals because I know that's the data element that I want. So you can see this chart looks very similar to the one you saw in your Data Equity Walk because it's showing data over time. You will also see a table below. And this is true for almost every data element on the dashboard. And within these tiles, you'll see the visualization and then below you'll see the raw data. So if you're trying to get data out of the dashboard, what you can do, and you'll practice this in your equity walk, is see the data elements from the tables and take that information and enter it into your Excel to populate some of your visualizations.

OK. And now I'm really interested in looking at data by race and ethnicity. So you'll see here we have two different filter options. Right now we're using the time trend feature but I actually want to look-- let's do gender. I actually want to look at data by gender or the number of reportable individuals by gender. So I select gender from the dropdown and it automatically switches. So again, we can see here there are more females than males in terms of reportable individuals in the San Diego Imperial region and we also have our table below.

Now to get from year to year, in the disaggregation space, you're going to want to go up to the top and filter for a particular year and click View. You're going to have to come back and click gender again to get that information. So that is how you navigate between the trend information and the disaggregation data that you've seen in your Data Equity Walk.

So this has been a very quick review. You are going to have more time to practice when you start building your own Data Equity Walk, but I want to pause and see if anyone has any burning questions before we move on to using census data. No. OK. I'm going to stop sharing my screen and I will take us back to our PowerPoint and turn it over to Mari who is going to walk us through how to access census data.

Mari MacNeill: Thank you again, Jessica. We wanted to give you an overview of another important publicly available data source, which is census data. I'll invite you all to follow along with me on the website as I walk through the slides. Margaret, will you put the link in the chat so folks can follow along? Oh, thank you. Perfect.