Meghan McBride: Thanks, Holly. Good afternoon, everyone, and welcome. Thank you for joining us for this preview of our newly redesigned CAEP fact sheets. Before we begin, I want to echo Ali's acknowledgment and thanks to the leadership folks who are here with us, the California Community College Chancellor's Office and the California Department of Education for their continued support of CAEP and the work that we all do. Their commitment to providing you all with high quality data tools and TA helps ensure that you have the information you need to make informed decisions on behalf of your students.

So over the past few years, the CAEP fact sheets have really become one of the most frequently used tools in our data toolkit. So you all are using them to understand your communities, to do your three-year and annual planning, and to make data-informed decisions for your consortium. And so as we prepared for our new year of TA and PD for CAEP, we saw an opportunity to make the fact sheets even more useful for you.

Our goal was not just to refresh the look and feel of the fact sheets, which is what we did do, but we wanted to also make them an even better planning and decision-making tool. So our redesigned fact sheets look different, but they're also incorporating updated data sources. They have new visualizations and new ways to explore your regional need, equity and workforce demand.

So we've focused on helping you move beyond just viewing data to interpreting it and applying it in new ways. And so throughout today's demonstration, you'll see examples of how you can use the new CAEP fact sheets to support conversations about gaps and service, priority populations, labor market alignment, and strategic planning.

Now, as my colleague Greg goes through the fact sheets, please keep in mind they are very much a work in progress. There are new features that are ready for you to view, but others that we're still developing and we're hopeful that we can gather your feedback as you see where we're headed, and then we can determine, using your feedback, what would be most valuable to you in your planning efforts. So with--

Holly Clark: Meghan you're muted. Yes. Meghan you were on mute.

Meghan McBride: OK. I don't know what you heard last, but I'm going to just go to the end here. I'm going to turn it over to Greg. Greg has been leading the redesign of the CAEP fact sheets. So he's going to walk us through the new dashboards, highlight some of the new enhancements, and demonstrate how you can use these tools in your decision making processes. And hopefully, just in time for your annual plans, which are due August 15. So with that, Greg, take it away.

Greg Hill Jr: Thanks, Meghan. And hi all. Thank you so much for taking time out of your day to sit with us and learn more about these fact sheets. As Meghan pointed out, we've been doing this for several years. And they really are, I think, unique-- I think really is a unique tool that we make available to support planning because the data is particularly at a consortium level, allows for all sorts of disaggregations. But without too much ado, let me start this presentation and let me make sure this works.

Meghan McBride: Greg, while you're pulling that up, I just want to say quickly, I don't know if Holly mentioned this, but if you have questions, just drop them in the chat. Debbie, who is here with us somewhere and I will be monitoring. And we'll interrupt Greg if appropriate, and he'll also be pausing for questions throughout the presentation.

Holly Clark: I actually did not say it, but I just put it in the chat, so you and I are on the same wavelength there.

Meghan McBride: Good. Thank you, Holly.

Greg Hill Jr: All right. So I tried to do the full presentation thing, and it just made my screen blank. So we'll just do it this way. So just skipping ahead. So for those of you who don't know me, my name is Greg Hill Jr, I'm a senior research associate at WestEd. I am, of course, joined with my colleagues, Meghan McBride and Debbie Pezzuto, the three of us, among others. But I think it's fair to say the three of us predominantly work in adult education, and we've been doing this for a good long time at WestEd and elsewhere.

So as I mentioned, we've had these fact sheets for a while, and I will walk you through them because I suspect there are some of you who are brand new. This might be the first time seeing them. But just as a bit of context, what the fact sheets are right is they provide demographic and economic labor data, among other things, to help inform planning.

And we've iterated on this several times, and I think they're getting better and better each year. This iteration, one as Megan mentioned, recently, we just updated it with the most recent US census data, the most recent DataVista data, and the most recent labor market data and unemployment statistics.

Two, we wanted to see what we could do to modernize the look a little bit, and actually, kind of think about how we might present data such that it really can prompt action. So trying to move it in the direction, well frankly, of-- well, yeah. Directionality. Providing some direction as opposed to just sort of presenting data without a whole lot to be said.

Also, we made some decisions about how to present some comparison data, so that we can ensure that comparisons are valid. We're also incorporating geospatial information. Now, what you'll see today is actually very limited, but I do have a slide that shows a preview. Geospatial intelligence is one of my areas in particular, and I use it a lot in strategic planning.

And so I'm really excited about this piece and would have loved to have that ready for you today, except I'm still working out the best mechanism to make that available because maps are computationally expensive. And the other thing that's forthcoming, and this is a pretty big initiative, is we're going to move from not just showing labor market need, but actually showing supply and demand.

And so we're in the process of extracting data from every adult school and community college website, catalogs, and will be sharing that in the final version. So this, for the record, if you're here and your website is out of date, please fix that. So that will help us tremendously to ensure everything is accurate.

OK. So this is just sort of a preview of the first page, but I'm going to just go ahead and bring it up and so I can show you the interactive features. All right. So if you're familiar with the last ones, you'll note that there is some similarity in terms of layout and at least the data points, but it's a lot more streamlined, I think, easier to print and does include some spatial information.

One of the things that we wanted to consider is how these fact sheets are used. And while they are web-based tools, we understand that a lot of planning happens in real, you know, face to face. And so we wanted to make sure that the data that are here can be used in a static format as well. And so not just pie charts, not just bar charts that you have to make some sort of-- that you'll have to try to figure out what the numbers are.

We want to make sure that all the numbers are there, as well as the visuals that help clarify those values. Once again, for those of you unfamiliar, the way that these work is, there are typically a number of filter menus at the top. The landing page just has the one where you can choose your consortium. I recognize that this is an abbreviated, very, very abbreviated list.

And some of you will say, we don't call ourselves that, and I totally understand that, and we'll address but for the sake of expediency, we're using these drop down menus. If you can't find your consortium, let me know. Although they're all listed. This is a feature you'll see on a few of the dashboard tabs. And always consortium, but also some additional ones.

For the landing page, you are also presented with a number of action filters along the top. When you land here, and when you select a consortium, it's automatically going to populate with adults 18 and over. So I can click it, and it'll sit there and think for a minute, but none of the values are going to change.

And so this was just a way to create that default without having to have all this grayed out. As you select each of these tiles, the values will adjust. I'll go ahead and pick no high school diploma. You'll see your age ranges differentiate, the race ethnicity lines adjust, and you'll see some map variations, but that's more kind of-- yeah. I mean, there's more to come on that that'll be a lot more robust.

I see there's a question, is there a link? Yes, you will get the link to this. They haven't been pushed to the website yet because they're kind of in beta. But that will be their home. At least that's what the plan is currently. And we'll make the link to these, the direct link to this version available at the end.

And once we actually push them live, we'll retire this, put a little note there that says go here for the real ones. So you can say that, but no problem. Make the font a little better? Sure. I'll see what I can do. I can zoom in a little bit. It has most of those-- I think it actually has all the same variables.

One adjustment that we made that is worth noting is, in the last version, we showed languages, but we included English. And while yes, that's useful, when we're looking at trying to understand that linguistic context within a region, what's more useful is what is the proportion, what are the shares of the population who don't speak English at home.

And so in our language detail chart, which I think is actually kind of a pretty elegant way to present this, it is focused on exclusively that population who speaks in a language other than English at home. One of the other changes that we made, if you're familiar with the old ones, is wanted to keep the focus on the data, not on the navigation. And in the old one, and in a lot of dashboards, all the navigation is up top.

And what we found is, you know, embedding it on that CAEP page, you end up seeing a lot of stuff, little tabs before you can get to the actual sheet itself. And so this version places the navigation buttons to the different tabs at the bottom. And it also lets us provide a little bit more descriptive information about where you're going.

So any questions about this page right now, there's a link to the fact sheets. Thank you for folks sharing fact sheet info in the chat. Yes. Just as a reminder, you won't see this version yet. Because those haven't been pushed. You will get access to this version, and once it's finalized, we'll push them out to the website, which actually I think will be pretty soon.

All right. No questions, then I'll move on. So first, let's go to the equity tab. So the current fact sheets kind of just shows a side by side of population against participants. And users are presented with the option to select the points of comparison. To answer Laura's question, the new fact sheets show the latest census data, which is 20. It's actually using public use microdata sample for the state, and it's the 2020 to-- I guess it would be 2019 to 2024 vintage, which is the latest.

I know it sounds like forever ago, but it's unfortunately not both because, A, it takes a long time for the US Census to process ACS data, but also, I suspect that has something to do with cuts in resources to federal data collection programming. But yes, this, what you're looking at right now, does have the latest US Census data.

So as I was saying, the last version compared-- sort of presented users with the option to select different populations and different groups who are enrolled-- or individuals enrolled in specific programs for comparison. The challenge with this, and as many of you have probably heard me say this, is that you want to make sure that if you're looking at equity issues, you want to make sure that you're comparing apples to apples.

So for example, you wouldn't want to compare, let's say, the population who speaks a language other than English at home against-- well, I mean, I guess you kind of could. But against ASC students or ESL or CTE students, while absolutely, the students have multiple needs. If you're trying to look at proportionality, it wouldn't really make sense, especially if you say, cued in on certain populations.

So case in point, if we select here English learners compared to ESL, we can get some valuable data about potential gaps. If we were to just compare English learners against your overall participant counts, you might see a huge gap in, let's say, representation among individuals who identify as white.

Is that a real gap? No. Why? Because the predominantly, if you are white, you probably are speaking a language. You probably speaking English at home. At least proportionally. And anywho. The way that this works is you have a focus and you have your view. And so there are three options. You can compare overall to overall, economic need to CTE.

Economic need, by the way, reflects 200% of poverty of the federal poverty line. If you're unfamiliar with the federal poverty line, let me know. I'd be glad to explain it to you. It is not a great measure. It is completely outmoded, but if you multiply it by 2, you get pretty close to the living wage for the state of California, which is about $30 an hour.

200% of poverty, I think is like-- actually, let me look. I don't want to hazard it, but it's close. It's either like 27 or 33. I'll have to check. So it's a kind of a decent or roughly useful proxy. There are absolutely other data points we could use to identify economic need, but since we are looking at percentages, this seemed like a-- you know, a fairly valid data point for comparison.

And because CTE, you know, can serve lots, there are lots of different ways you could define economic need. We didn't want to over-complicate it for a tool that's meant to be utilized by everybody in the state. But yeah. Economic need. English learners to ESL. Populations and those without high school credentials against ASC.

This might be adjusted to include ABE. Right now, we don't have discrete counts of ABE and AAC students. And some initial experimentation showed comparability in terms of shares in some areas, but less comparability in others. And so this felt like the most accurate point of comparison. All right. We'll stick with ESL.

Up at the top, we have some general info around your population, right? Counts of reportable individuals, counts of council participants. And so as you filter, so if I go economic need, then you'll see that this changes. So this is at or less than 200% of poverty, 6.9 million individuals statewide fall into that subpopulation.

Currently, among those who are enrolled in CTE programs, about 499,000 are in CTE. You get a 7.2% reach of the adult population. And then we have a report of individuals also filtered by individuals enrolled in CTE programs. Moving down here, for some of you, this might be kind of a confusing view. And so I will help you understand it.

It is actually a lot easier to read than you think once you do get it. So what this shows is proportionality. It's a proportion. It shows the gap between percentages of the population either overall or, you know, the subpopulations most aligned to the program's adult ed programs.

So percentages of the population disaggregated by gender, age, race, ethnicity, as well as proportions of participants by those same subpopulations and of course, program areas. So the way that this works is we've got a 0 to 100 populations are identified by this little circle. So this tells us if I hover here, that, OK, 54.3% of the population overall of the population whose income is less than or equal to 200% of poverty are female.

So in other words, there are more women than men who are living at or near poverty than-- yeah. So yeah. Then then there are men, you'll see that the value is over here too. So you don't necessarily have to hover over this to get that. Again, this goes to the-- you can print it out and it'll have the information there. The square represents the pipeline. And so the pipeline would be your participants.

So for overall statewide, women enrolled in CTE, 57%, yeah. 57% of participants enrolled in CTE are women. Disproportionality. So absolutely, and I'm getting to that. So this is not a complicated disproportionality formula. In fact, it's just a simple percentage point difference. And so if you're looking at, if you scroll over to the right, remember, you have your number here for population, pop, and then your number here for pipeline, your percentage.

The difference in percentage points is expressed in that little pill. And so what this is saying is that there are 2.8 percentage points different. There's 2.8 percentage point difference between the population and the pipeline. This is not the same as percentage difference. It is just taking the numbers, subtracting 1 from the other. And we have a key here that helps to identify what those pills mean.

So if it's blue, it is at or above equity. In other words, it is either the proportions are exactly the same or it's higher. And just as a explanation here, just for those of you who might be new, this is sort of a common way of quickly identifying potential equity gaps. There are absolutely more complicated ways to do it and probably-- yeah. More complicated ways to do it.

But basically, the idea is this. Is you should have, if you're recruiting and engaging equitably, then your populations should reflect proportionally the population that you serve, more or less. This is sort of a basic statistical principle. And when those are out of sync, it is an indicator that you might want to look into some of the reasons why.

Not in every case, if you see a-- you know, under-representation of one population, that doesn't necessarily mean that there is a genuine equity gap, and if you'd like again, to learn more about how to do that, how to calculate that when some of these formulas are more or less valid, we're happy to provide that.

This is really just a starting place. OK. So if we look at-- so scrolling down a little bit. You know, for example, we see, you know, individuals who identify as white a potential equity gap there. But is this really an equity gap? Well, if we agree that unfortunately white people tend to have higher percentages of employment and wages, this is probably not a real equity gap.

Because what this is really saying is that there are-- yeah. This may not actually be a significant equity gap. So ESL is probably a little bit easier. Oh, just to finish my thought there. If it's red, it means that there is a potential gap. So any questions about this? Hopefully, this is making sense.

Meghan McBride: Greg, you do have a question from Uihlein. And I just want to remind you, if you happen to get to the question before I do, read it aloud for the benefit of those who are watching this recording. So I will do that now. I'll model that behavior. Uihlein says, "Greg, I like that differentiation between pop and pipeline. Cool. What is the rationale of your choice of using pipeline rather than participants?" And then also Susan wants you to Zoom in some more, which I think you must have seen that because it looks like you might have.

Greg Hill Jr: Sorry about that. Honestly, it's just nomenclature to highlight the fact that this is, you know, your training pipeline, right? But if that's confusing, I absolutely it can be just because it is participants. So we can also just make it pipe. Make it participants too.

Meghan McBride: Yeah I think someone else had that comment when they previewed it as well. So hearing now two of those, maybe we should consider just changing it to participants. Thank you for that comment, Uihlein Susan I hope that view is good. It does look like it's all yeah. Susan's happy, so I'm happy, as you are Greg.

Greg Hill Jr: Thank you. Thank you. Third panel is demand. You can go back by hitting the demographics one. This includes the latest data from Lightcast and other sources on the industries and occupations that are going to be in the most demand for your regions. This has a number of filters up at the top, including consortium, county, typical entry level education, typical job experience, and then typical on-the-job training.

Let you kind of drill into the different kinds of occupations, and that you may wish to consider developing based on the need that you see. You'll notice that the default for entry level, it shows multiple values. So each of these has our multiple checkboxes. For most of the programs that you'll be looking to develop, so actually before I get too far there, Lightcast provides data that includes typical entry levels of education of like master's degrees and PhDs. Right?

And while that might be novel to know, and if you're really curious, I can show you where to find that, for planning adult education programs, that is a really long horizon. Most of your CTE programs, CAEP defines the CTE program area as short-term with high employment potential. And that typically means a year or less, which is-- so yeah. One year or less, maybe two depending on the robustness of the program.

But anyway. While it is valuable to be looking down stream for how you can develop programs that could ladder ultimately into bachelor's degrees, as a first step, when you land on this, bachelor's degree isn't selected. If you add it, then that's great. You'll see the values change. But this is going to be your view.

For those of you particularly in areas with, say, particularly low levels of educational attainment, you might wish to remove some of these options, so you can really drill into, OK. What job opportunities are available or what's coming down the pike for, say, my AAC students and so forth.

Just like the other tabs when you select a consortium, everything will adjust. This might take a sec. One thing that is really important to note is when you make that adjustment, because consortia can cut across counties and the data are only available-- I mean, they're available at county and metropolitan statistical areas, but those are actually larger than counties. Well, not necessarily it varies. But anyway.

These data include all of the counties that your consortium touches. For aggregated data, so for numbers like largest industries, things like that, you'll get for count data, it should be aggregating. For percentage data, it's going to adjust accordingly. But what you may wish to do if say you know that predominantly, you know, most of your population is in one county, to filter into that just to get a more accurate view of the specific population centers.

Additionally, when you select your county consortium, you'll see the living wage adjust. Living wage, just as a point of reference, it's going to default to the largest number across the counties as opposed to averaging. Another feature that we have here also includes living wage. And we'll provide some guidance on this.

So the way that living wage is calculated, is it's not just one number. It is a number based on household size. And so living wage amounts are calculated for county and one adult with no kids, one adult with one child, one adult with two kids, you know, two adults, you know, working, one child so and so on and so forth.

We wanted to be able to show what the living wage might look like for different household configurations, without overwhelming folks with too many options. And so this is kind of a simple way to do it. When you land, it's going to always show you one adult that's at the 1 A. But it also includes information about populations-- one adult, one child, two adults, two children with only one person working.

If you click up top, and these will eventually look more like buttons, you'll see the values will change. What is you will also see colors change in the fastest growing occupations area. And so before I get into that, over here, you see the top largest industries, which I think should be pretty clear to everybody what that means, you got your bars, you've got your counts. And then over here, you've got your fastest growing occupations, which is to say that they have the highest percent rate of change between 2025 and 2026 or 2020, 2030 rather. 2025 and 2030.

If you're wondering why 2026 isn't included, it's because we're not done with the year. Yeah. As you to make changes to the living wage field, the coloring will change. And you can see the key here. If the occupation is above living wage, it'll be this sort of greenish color. If it's somewhere near threshold, which is a plus or minus 5%, then it's going to be that sort of brownish yellow color. And then if it's below, it's this reddish color. Susan makes a point. In growth outlook that the color coding and the length of the line.

Yes. That is true. They are not correlated or they're not connected. The length just represents percent relative to the other occupations. It's just the color that changes. Scrolling down is a more detailed view of the occupations table. Our occupations chart, but it actually includes all of them. And so you can scroll to get to see, you know, other ones that might be of interest to you.

You can also just enter here some like if I'm looking for, I don't know, medical, and I just hit Return. It's going to give me everything that has the word medical in it. So it's like, oh, medical assistance, right? OK. That's what that's looking like. And then you can clear that out, and it'll give you everything back.

Likewise, you've got filters here to adjust for counts of jobs. The projected increases in jobs for 2030 percent growth median wage. Percent 55 and over. And so this actually is new. So what this is showing is the percentage of workers within that occupation who are age 55 or older. Anybody want to just quickly tell me why that might be a useful metric? I can wait. I'm cool. Bingo. Right? Exiting employees. Thank you, Carla.

So you what you're looking at here is a population who should be approaching retirement. Having said that, I will also say that lots of adults, older adults are not retiring. Likewise, yeah. Anyway, just pausing there. And yeah. Thank you. That's exactly right. Retirement and recruiting. Primarily retirement, it's showing you where the gaps are going to be in these occupations within your region.

So if you've got, you know, welders, this also gives you a little bit of an indication around the cycles of hiring that you can kind of plan into potentially. As with the bar chart, or actually, it's a lollipop chart, we included a little indicator pill indicator for whether it is, you know, at, above, or near living wage. You can filter based on, you know, above or near living wage alone or just above living wage.

And if you make adjustments up top to the entry level education, that will also filter further. OK. All right. So before moving to the next part of, I guess, our presentation, I wanted to pause, see if, again, there are any questions on anything I've shared. Feel free to come off mute right now for a minute, if that's helpful.

Meghan McBride: If no one comes off mute, Greg, you are caught up in the chat. Uihlein may have had her hand raised earlier. I don't know if she meant to do that. She took it off kind of quickly. But Euline feel free to--

Euline Olinger: Hi, Greg. Hi, Meghan. No, I think it was answered already because I never really understood all those columns. And so Greg had told-- said something about this is like 2030, which is like 10 year's projection, right? Or 2025, which is the current number of people employed in those occupations, is that correct? And then you have the projected 2030. Sorry.

Greg Hill Jr: Yeah. No, no, no. You're spot on. So yeah. The 2025 jobs, this is a job count.

Euline Olinger: Yes.

Greg Hill Jr: This column actually shows what the increase will look like by 2030.

Euline Olinger: Right. OK. Got it.

Greg Hill Jr: So basically, this is saying or increase or decrease. So right now, the way that you would read this is so for aerospace engineers, and operations technologists, there are about 1,181 who are currently employed in that occupation by 2023 or why do I keep saying that? 2030. I'm lost in time. You should expect that to grow by about 100.

So that means that if you're planning a program, a CTE program in aerospace technology, you can reasonably assume, OK, that means if I'm developing this in five years, there will be 100 new jobs between now and the next five years. Is that big enough to really commit resources to develop a program for? Maybe, maybe not.

It also gives you a sense of what cohorts, how you might wish to cohort things. Right? So something that's really small and manageable but of high need, it's like, well, OK. You know, I'm able to turn out 25 per year. That actually gets us to the need, if there was a local need for that. Anyway. OK. Any other questions?

Meghan McBride: Think you're good for now, Greg.

Greg Hill Jr: All right. So I'm going to just hop back over quickly. We've completed our live walkthrough of what's there. In the slides, there are a couple little activities that I don't know if we necessarily need to go through as an exercise, but thought it would be useful to provide a few, so that you can do this on your own or do it at home.

And so everybody is using AI. This is a given these days, and just quickly thought it might be useful to show you how you could leverage the fact sheets and AI for your regional planning. So as a starting point, ultimately, here are some our rules that you can use. So first, export the data, provide it, probably the data itself or a screenshot.

Currently, you can only download PDFs, images, or PowerPoints from the fact sheet view. I'm working on maybe seeing if we can get a crosstab export. Unfortunately, the data export is currently only available if we share the entire workbook for download, which we can't do. But most AI tools can read PDFs, and so that would actually be the most effective way to share it.

Once you feed it in, make sure that you verify-- you don't have to verify every figure, but take a look at the numbers that I brings back and just confirm. Sometimes AI doesn't read numbers correctly, right? It might also round things in weird ways or aggregate in strange ways. Some of you might also be inclined to leverage AI for making comparisons with your very specific populations.

That is a good planning approach, but not necessarily the best, depending on the grain size of the data. Not a great practice for using AI. So if you have aggregate data, great. If you are trying to feed it, even de-identified individual unitary data, I would probably recommend against it. And then just as a reminder, and I think everybody knows this, you know, AI will draft, but you got to decide whether it's valid.

And so three different approaches you might use with some very simple prompts. One, what are the populations in need? And so this would be related to that first panel. So pick your sub-population, and either download or download all of them as the PDF, and I'll show you how to do that mostly. I'll show you that in just a moment rather.

Do your own thinking about, OK, what are you seeing? And then give it to AI, and ask it to find three or four patterns and gaps most relevant to planning and programs. Be sure to examine disaggregation and look for equity gaps. You could also modify this. Another thing that you may wish to do is provide some explanation for how this sheet is to be read.

So in the case of the landing page, I might include something like-- and we can expand these into true plug and plays. But this is an export from an interactive tool. The bar along the top represents different populations in the aggregate. Clicking on tiles filters them. The view that you've been given is filtered by adults 18 and older.

And that way it also has that context because sometimes it might just clue in on one small thing and not really understand the whole of it. And then, like you said, check figures against dashboards, see what it says, and then you figure out what priorities are. For the second one, yeah, it is better.

So AI is better when you convert PDFs to markdown. In fact, that's what it does. When you give it a PDF, it turns it into markdown text. Now, where AI is not great on its own, in that regard, if you try to turn this into markdown text, it may not understand the relationships between the parts, because while it does do that to read the text, it still uses the image to understand the visual rhetoric.

And so if you are savvy and know how to export, how to transform a PDF into markdown, if there are visual elements, there is a unique visual rhetoric, then you're going to want to also include that image, so that again, it can understand what those relationships are graphically. But that's a really good point. Thank you.

A question you might use to drive for that second panel, the equity panel, is, you know, to what extent is my consortium equitably meeting regional need same basic process and prompt? Find three or four patterns most relevant. Don't infer beyond the data recommend approaches to improve equity. Right?

And then just quickly third, what occupations or industries have the greatest need for skilled workers. And then in this one, for example, it's just like OK, rank occupations with the greatest need for skilled workers. Wait large projected openings above living wage pay, dah-dah-dah. You could do this on your own, but it can also save you some time.

If it's helpful, we can also provide you some more robust prompts that you can use. And I think if we're able to get crosstab exports of the data, that's going to be even more useful. As a point of reference, if you did want to download this, where you would go, and this is true both here, and on the fact sheets, as they're currently-- the fact sheets that are on the website, navigate to the bottom here, find this little guy that has an arrow, and you can choose the format to download in.

You will probably see data crosstab and workbook grayed out. But again, let's see if I can work on that. The best option is really PDF. And if you do that, it'll give you the option of either choosing what you're looking at right now or what it says specific sheets from this dashboard, which I can actually make sure the names. This will be every single sheet, if you just wanted to export like one of the panels.

But ultimately, the easiest thing is just to pick the current view and then hit Download. I believe if you-- and you can also as PDF here too, or sorry as PowerPoint. You can also download it as a PowerPoint, but all it really does is it puts the image in a PowerPoint. So it's not particularly useful to my mind.

Now, oh, shoot. We're almost over. OK. So really quickly, I didn't realize. This is the cool stuff that you don't get today, but you can look forward to. So what is coming? Geographic detail. And so basically, what we're going to do is have maps that show you distribution of particular sub-populations. It'll be more nuanced than what we currently have. So for example, some of the ones that we're looking at are distribution like dot maps of distribution of adults who speak a language other than English at home by language.

So you can drill into not just where your limited English speakers are, but specifically, what languages are they speaking at home to the extent that's available. Likewise, some views that include income ranges, educational attainment, among others, as well as some visuals to help you better understand what you're looking at.

Actually, currently, if you drill into one of your consortia, you'll see sometimes it takes a minute. Oops. Let me zoom in. You'll see institutions listed. Actually, let me refresh with little points. Little indicators. And then what the next version will include, we'll actually kind of let you drag. There you go. Drag sort of-- at least you're trying sort from a radius, from one point to be like, OK. How many people are within 20 miles of my particular campus, of one particular campus? And then you'll see visualizations change.

Likewise, we've got a supply and demand view forthcoming that will hopefully point you in more concretely to where gaps are based on what programs are available insofar as we are aware. And as I mentioned before, we are in the process of gathering all the non-credit. CTE programming available at adult schools and community colleges and putting it into a singular kind of database type structure that will also include jump links to the programs themselves. And I think that's it. Yeah. I'm going to stop sharing. Oh, wait. If you want to find it, all right, I'll put this link in chat. You can play around with it. I encourage you to--

Meghan McBride: Greg, will I be able to drill down to individual member agencies?

Greg Hill Jr: So what it will let you do, it will show individual agencies. But in terms of the comparisons, like on the equity tab, no, not currently. I mean, it's something we could look into, but it will have the locations and in the geospatial visuals, yeah.

Holly Clark: OK. Well, Greg, thank you so much for presenting for us today. I have dropped some links into the chat. The most important one is the evaluation. We had several people attend with us today. Please do take a moment. We send your feedback to the WestEd team here to let them know how they did. We send it up to the state leadership to let them know the presentations that we have-- you know, that we've hosted for them.

They read. They're data people. They read all of your comments, and please, if you're asking for any follow up, this is an anonymous evaluation. But a lot of times what we're seeing is you guys are asking for a follow-up. What we want to see is this can you please provide us this? If you have a question like that, in the evaluation, can you please put your email address?

A lot of times, if you're asking for something, the facilitators want to follow up and give you that, but they don't know who it's coming from. So if you are asking for a follow up and you provide your email address in parentheses next to that, then the facilitators and presenters can provide you with what you've asked for. Otherwise, it's 100% anonymous and we don't know how to reach you.

Along with that, we did drop registration for the summit. Proposals for the summit are open. We are still accepting proposals. So if you have anything you would like to present, we'd love to hear from you. To the WestEd team, Greg, thank you so much for presenting. Again, we will get this remediated. It's going to take longer than normal because of the year changing over and getting our expenditure contract up for remediation. Once it's ready, we will email everyone and let you know it's ready, but it's going to probably take between six to eight weeks before this is ready on the website. So please do give us a little bit of grace for getting that up.

Thank you all, and I hope you have a great rest of your day. Bye-bye. And the PowerPoint. Yes. I see that. The PowerPoint, well, once it's ready, it'll they'll send it to us, and we'll send it out to you. All right. Bye-bye.

Meghan McBride: Thank you. Bye-bye.