Move us forward. All right. So just a little bit about us. WestEd is an enormous organization. There's like a confederation of 700 consultants focused on K12 higher education around assessment or a variety of areas. Our group is a post-secondary education workforce group specifically. We're a small but mighty team and we consider ourselves sort of the social justice warriors of the agency.

So some of you may have come across WestEd either through CDE or other venues, and there are lots of people at WestEd that I don't know, so I wanted to establish an identity with this work. And we really see our role to kind of strengthen the role of higher education, workforce, and economic development programs to improve student access and outcomes in higher education. And specifically, we're very focused on ways to think about leveraging education to increase economic mobility for low income families and communities. Allie?

These are just some of our lines of work, and we'll spend a lot of time here. We do a lot of intersegmental planning. So the mapping work you're going to see today, we've also done for K14 pathways in different regions of California. We've done similar work in the State of Nevada. We're mapping every Perkins-funded program and building kind of an inventory, a map for the entire State of Louisiana right now, which is something we're very excited about.

We are team manages and we are the product developers for the launch board. So all of the data tools that you see within the launch board, we do all of the metrics, all the planning, all the development work, and then we have coders who actually code that and get it up there. I specifically am the project manager for the adult education pipeline, which I know many of you know. We're doing a lot of work and guided pathways currently, both in whole states and for individual colleges. And we're very-- really what the work is about for us is how do we use data to help practitioners increase educational success and mobility for the students they serve. Allie, there we go.

So we're going to do five things fairly briefly today. I'll tie this very quickly to the California Adult Education Program, the CAEP outcomes, and why we did this work. A little bit of overview of what the project is and kind of how we came to do it, the purpose, and then we'll pivot over to Allie, and she'll talk about the methodology that the team used to do the work, the kind of structure of the data sets, and how we've use that, and how we're going to use that to inform both a statewide report around CTE and career education programs in adult ed for the entire state and then a career education to career data tool that Allie built specifically that leverages the data so it could be used by practitioners to have regional conversations around building stronger and more effective pathways.

So just, Allie, go one more, and we'll see the highlight. So the outcomes in AB104 in 2015 pivoted pretty dramatically in California with the win with the creation of CAEP-- at that time a AEBG-- to really strengthen transition to post-secondary education, completion of post-secondary certificates, degrees, or training programs, and then specifically, employment and wage increases. So it really-- adult education has been doing foundational and amazing work in CTE for a long, long time.

Obviously the funding challenges over the last decade, decade and a half, kind of depleted that somewhat, but what we have found is that there's still really amazing work going on, but we wanted to highlight that work in a different way because there are some challenges in terms of understanding what kinds of programs are being offered, what kind of training is being provided to think about how we could support the CAEP consortia and building more effective both transition models and kind of CTE and career education programs that can help someone not just get that initial employment, but then secondary employment and actually move into a career pathway. Next.

So the purpose of the work, there's a couple things. So one of which is that there is no data set-- so I can tell you that what I see in the launch board when we build that tab every year is whether or not someone is or is not a CTE student with a couple of subcategories. So I can tell you if they're in a short-term CTE training program. I can tell you if they're in a workforce preparation program. I can tell you if they're in a pre-apprenticeship program, but I can't tell you what pathways students are pursuing. So we don't know what occupations adult education students are trying to get. We don't know what occupations are being targeted by the field as you build your programs and you're trying to respond to your communities.

And there's just sort of a gap in our knowledge about what's happening that we wanted to fill, not to evaluate the program-- so we're not trying to tell anyone you're doing the right work or the wrong work, that your program is good or your program is bad. What we're trying to say is, what are people offering? What really can we say to regions, and how can we put data together in ways that may allow you to have a better conversation between the college and the adult school and your workforce board to say, hey, we're actually offering these things, and we could build a pathway out of it that's more connected. So there aren't just different offerings happening in different systems, but things can be more connected.

So we're trying to understand the continuum of courses offered by adult education and non-credit practitioners-- that was the first thing we did. We're trying to look at the relationship between adult education and credit programs and regional labor markets. So I'll show you a slide in a second that shows-- I did a really kind of quick and dirty, rough version of something like this from my own consortium when I was a dean of workforce at the Contra Costa Community College District to help have a better conversation about what everyone's offering to actually look at here's what the adult school's offering, here's what the college is offering, here's what the labor market tells us. Let's figure out some pathways that we can build together or support collectively out of that data and information.

So what we really want to do is support two kinds of conversations. One of which is the conversations you can have locally looking at this information to use for your own planning purposes, and then there's some conversation that they get to state level by how we gather this kind of pathway information to really create a stronger data system so we're not having to do this manually, which is the way we had to do it this year. Ally?

So this is what I sort of talked about earlier. So this is something I did when I was still a dean. I literally went into the catalogs for these seven adult schools in my county, and I pulled their offerings. This is an industrial trades, and I looked at entry level certificates or standalone courses. I looked at apprenticeship opportunities, and then I stacked all of the certificates-- low unit, high unit, and degree programs at my colleges. And then what I also did is I did a labor market chart that actually was on the back of this page. It was like a one page thing where you could see everything on front and back to have a conversation so you're not sorting through tons and tons of data. But it gets everything in one place to really have a stronger, better conversation.

So I'm going to talk just a little bit generally about the methodology we used to kind of establish the arc, and then I'm going to let Allie dive in on the details. And then I'll be in the chat trying to answer questions as we go along. So when we tried to think about how to collect this information there's no single source of data about CTE courses and programs for adult education. We looked at sources like OTAN and course approval. We looked at other data sources. But really, unlike MIS in the community college system, where I could tell you every program that every college offers and I can say whether it's CTE or not, and then I can track whether or not students are actually getting jobs out of, say, a program that only has 25 students in it and what the pathway would be, we don't have the ability to kind of pull that data from any single source.

So we actually made-- we chose to basically work off of the publicly available information that the adult schools provide to their communities about the programs and pathways and courses that they offered. And so it was basically was we looked at those catalogs for the last academic year and cataloged every CTE course that was offered by an adult school or a community college non-credit program that we could say it was CTE or workforce preparation. And then if we didn't find a catalog actually for an institution, we would actually reach out to them and ask them to provide us whatever information they have about the programs they offer.

And we're not claiming that this is a perfect methodology, but they're using what adult schools and colleges say to their communities about what they offer and what occupations they lead to seem like a good way to kind of produce at least an initial inventory of what's out there. Then we could over time maybe kind of correct and refine. So just letting you know that one choice we made early on-- and Allie is going to talk about some of the choices we made about thinking about classifying occupations and things like that. And so, Allie, why don't I hand it over to you?

Yeah, absolutely. Thanks so much, Randy. So Randy is very generous. We very much luckily had a team of people helping me lead this work. We started out initially with a group of three other research associates and myself, and we did an initial scan of a sample of adult schools, as well as non-credit community college programs to develop that data structure and decide on what data elements we would be able to collect and how we would be able to align what is publicly available regarding adult education CTE courses with non-credit community college offerings.

And then we luckily had a team of two research assistants who helped us gather those course catalogs, organize them in an effective way, and do a real inventory and data collection, really just scraping PDFs, and that took four or five months of data entry work. We also downloaded the data from COCI, the CO Curriculum Inventory, and we use employment data from EMSI. We ended up aggregating everything and analyzing it side by side adult education, non-credit, community college, credit community college, and labor market, and we will get into some of those visualizations later on today.

And as we were going through, particularly in the initial scan of developing the data structure and deciding on specific data elements, we decided to do a short series of interviews. 14 interviews were conducted and two per region, just to have some follow-up questions to understand how adult schools are deciding on what to offer, what kind of labor market they look at to decide on the courses they offer, and what's the process for communicating with the community, how do they get courses out there, how do they publish their catalog. So just a little bit of qualitative understanding about how adult schools function in that way so we could help support our understanding of what we were finding in the publicly available data.

So this is our field structure. This is what we decided on to collect in the initial development of the data structure. We have some pretty basic data elements regarding the regions and school characteristics. We have course groupings-- so how we categorized the courses that we found. And then we have of course characteristics, alignment with community college, and certifications and apprenticeships.

So these last three categories-- course characteristics, alignment with community college, and certification and apprenticeships-- were found directly from publicly available course catalogs or websites, anywhere where adult schools are posting what they're offering. And we were looking for any offerings between, really, the summer of 2019 and spring 2020 of what was offered. We realized that now a lot has changed, but we did most of our data entry in the fall of 2019.

So the course groupings are the ones that are our categorization process. The first process of categorizing all of our data entry was developing, quote unquote, pathways. So we categorized first all the courses we found. Once they were cleaned and entered into pathway areas that were similar to K12 Perkins, it would be entrepreneurship or business development or a healthcare pathway. From those kind of groupings, we did a deeper dive to understand, OK, well, what type of force is this? Is it something that is very specific short-term vocational training, where a student is intending to get a job immediately after this program? Is it a skills builder course? Is it a short one-off kind of course to help support development through a career pathway, or is it something else? Is it workforce preparation or career exploration?

The occupational categories really just, is it occupational, or is it something else-- whether that's workforce preparation or career exploration? The SOC code-- so for each occupational training program, we assigned a specific SOC code. And then all courses, whether they're a specific occupational training programs or skills-based shorter term courses, they all are associated with a standard occupational sector.

So we scanned 521 institutions, including every CAEP consortium member and additional community providers relevant to this study. We started off with a list of all the adult schools we knew of from LaunchBoard to validate what we were looking for. But we also looked outside that list and did a general scan just in case. And we found from those 521 institutions we reviewed, 225 agencies provide some kind of CTE and workforce preparation course. And from those 225 agencies that we were able to find some course information for, we found 7,000 course offerings between community college non-credit, adult ed, K12, and other agencies such as our ROPs.

So here's a little bit of a deeper dive as I was talking about of the course characteristics or the course identifiers. These are the specific course types that we came up with. So the first, most rigorous one that we found was the occupational training, and these are courses that are generally longer term courses. They're teaching towards a specific identified occupation, such as a medical assistant or an office assistant. And they're often teaching towards a specific certificate, often industry recognized but not always. And perhaps a state licensure if it's something that would need that, such as a cosmetologist.

These programs often include some kind of work-based learning, whether it's required or optional for the program, and the course descriptions often note that specific occupation. We found a large majority of the courses were really occupational skills builder courses. These were shorter term courses-- 10 to 150 hours. Sometimes they also included a certificate, but the certificates were focused on a specific type of skill, such as the Adobe Professional certificate or another certificate.

We also included recertification into this occupational skills builder category, although there weren't very many recertification courses that we found overall. These were courses that were geared towards a specific industry, but we couldn't find a specific occupation associated with that course necessarily. And course descriptions really described exactly what they are is that they were intended to be a short-term course for a skills builder. Let's help you move on with your career and build these specific skills in this area.

We found a small number of career exploration courses. These are courses that are not included as required introductory courses, as we found a lot of health care, occupational training courses required some kind of initial introduction. These were separate independent career exploration courses that were focused usually on a general industry, and they were usually very short-- maybe one day or a couple day courses.

And then we found some workforce preparation courses, so anything basic skills that's not related to a specific industry. We found quite a few keyboarding courses, Microsoft Word courses, basic soft skills unrelated to a specific industry or occupations, such as communication or conflict resolution. And then basic certificates per the WIOA Title II guidelines ServSafe and OSHA. And these were also generally pretty short-term courses.

So as I was saying earlier as well, all of the occupational training courses that we found, the more rigorous long-term ones that are intended to provide a student with a job immediately after, we assigned specific SOC codes for those courses that are related to the specific occupations that they were training towards. All of the occupational skills building courses we also associated with a general industry, as they were related to those more rigorous training courses.

So we categorized everything into the standard occupational clusters because the vast majority of adult ed courses are really focused on career-- either people who are long-term unemployed or who are looking to advance in their career and need a little help. And so most of the courses are really focused on people who have been in the workforce or are continuing in the workforce or are specifically looking for a job. And we felt that this categorization process was the most effective to understanding what's currently being offered and how to align what's offered to the labor market.

There are a lot of standard occupational clusters, and so we did a little bit more condensing, because we found that a lot of the adult education courses that we were associated with these SOC clusters, they were ones that were perhaps associated with a sales and related occupations-- a specific occupation that cluster might be related based on skills and content area to office and administrative support occupations. And we really wanted to see how those courses and how those occupations related to each other at a larger scale within the regions, so we clustered these-- the standard occupational sectors together in ways that we hope support understanding how related occupations with related content areas and related skills could help build a career pathway for someone.

So here's a little bit of a summary of our occupational training courses versus occupational skills builder courses. So these are the larger meta-clusters of the standard occupational sector. So here we have public and community service, production and logistics, and we can see within public and community services meta-cluster of the standard occupational clusters, 38% of the courses offered within this area are occupational training courses, and 62% are occupational skills builder courses.

So you can see that across the meta-clusters, really the majority, with the exception of is being 50/50, the majority of courses are really occupational skills builders intended to help people continue on with a career. But there were areas-- specifically construction and repair services and health-- that had a very high number of occupational training courses across the state.

Allie, can I drop in here for a second?

Yeah, of course.

I'm realizing when as I'm looking at the deck that there's a another slide we should have had in here that has some kind of summary information around courses and institutions, and I just want to share that with you verbally. So when the team did the analysis, they scanned over 500 institutions across the state and found 215 agencies offering specifically things we could identify as CTE courses, but the total of courses was over 7,000 courses. So one of the takeaways for our team from this is that in spite of the fact that CTE has not been well-supported in adult education, adult educators are offering a ton of really valuable instruction and are really doing their best with limited resources to try and meet the needs of their communities.

And what it spoke to also in the interviews, people-- we tried to figure out how people were making their choices, a lot of people identified, they just don't have the resources to actually build new programs. So I think the fact that you're doing such good work can really become a validating message to the legislature that we could-- about what you could do if you actually had more resources to support this work. And I just wanted to give a little bit of a nod to kind of the scope and scale of this study and how much really good work we've found out there. All right, Allie, go ahead.

Yeah, that's exactly right. And we found in every single interview that everyone was looking at labor market data and already had a wish list of their ideal core training courses they would want to offer it if they had the funds and resources. So we know that everyone is doing a really good, interesting, and difficult work, and this project definitely showed that.

So of the workforce preparation courses, these are the types of courses that were offered. There were a majority of Microsoft applications and basic computer skills and workforce preparation courses. We found quite a bit of workforce readiness and general business skills associated as well. So this is just a general breakdown of what types of workforce preparation courses we found.

And here's a chart of our career exploration. We found that the vast majority of career exploration was in health, even when it wasn't associated with a specific training occupation course. And there were very few overall career exploration courses, although we are not sure if there are other career exploration workshops that are happening that perhaps aren't listed with the courses. So this is an area for further exploration.

So after we analyzed all of the clusters and organized everything into a categorization process, which was also very much-- I had a lot of help from our senior program manager, Aurora King, in advising and how to do this categorization structure. We began to try to understand how to align what we had in adult education with the labor market to have a better understanding, as Randy was saying earlier, of how to help build really clear pathways for students back into the labor market or into the labor market or to help advance their careers.

So in all of the next visualizations you'll see, we have adult education CTE offerings, non-credit CTE offerings, community college credit offerings, and occupations based on educational level. We really focused on middle skill and above middle skill, but these visualizations include all of occupations within those. And then we also segmented the data statewide and in regional view. So primarily you'll see regional views in the dashboard itself.

So here's what an initial graphic looks like. At the very top left, in blue, is the adult occupational training courses specifically. In the gray, just below it, is the non-credit occupational training courses or CTE courses. And then in the green is a community college credit awards. In the gold or yellow, all along the right side, are the highest occupations with annual openings across the State of California within the health meta-cluster. So these are all related-- each chart is related to the health meta-cluster, which includes health care support occupations, health care practitioners, and life and physical sciences.

Just a quick note-- when we say "college credit awards," because this always creates a little bit of confusion, we're talking about the awards offered by the institution. So we count the number of different award types offered by institutions. It's not the awards conferred on students. Otherwise, these would be really horrible data for an entire region or a state.

Yeah, it's the awards offered. So the opportunities to attain a credit award or the different types of credit awards offered. Down at the bottom, we have examples of occupational skills builder courses within this health cluster. So we can see that there are 716 occupational skills builder courses related to health care, and then we have an example of those types of courses. And really, we made a lot of these visualizations initially for their report as a static visualization for each meta-cluster within the State of California. And then we thought, wouldn't it be cool if we could actually visualize this in a way that could be interactive? And so that's what really our dashboard is.

Here's a closer look at the occupational training courses. And these charts are really to help us understand at the statewide level what's happening across all these diverse institutions and within the labor market. So they're trying to get some kind of clear picture about what's happening across very different systems with very different data sets and to get at some more deeper questions about what's happening and how we could better align to the labor market. And here is a detail of those occupational skills builder courses related to health care. We have nursing skills refresher lab, first aid, technical skills training. And this is just a sample of some of their courses that are-- occupational skills courses that are offered.

Here's the same chart, but with the business meta-clusters, so we can see that it's organized in the same way, but the data's changed a little bit. The annual openings include all types of jobs, including middle skill and below middle skill jobs as well, and it's organized by annual openings-- the total number of annual openings. And we also have the occupational skills builder courses for the business meta major cluster as well. Business has some of the most occupational skills builder courses, and examples include QuickBooks, customer service, principles of accounting, et cetera.

So here's a look at what our dashboard looks like. It's very similar to those specific graphics, and we're going to hop into that real quick. I want to pause and see if there are a bunch of questions here. Randy, can you help me go through the questions and see--

I mean, I've been answering questions in the chat. There was a question related to, would it be helpful if people were able to enter zip codes for their courses into TopsPro enterprise, and they said, yes, that would be helpful. There was a question about the grain size of the data. Someone is asking if it could be broken out by institution. And basically I shared that the data tool currently is designed to show regional or statewide views, but underlying data set can be broken out by institution. We've discussed at least doing a consortium level view potentially in the future, although that then affects the way in which you cut the labor market data. So we haven't-- that's not in there currently, but it has come up as a question before.

There is a link in the tool, as Allie will show you, that provides access to the raw data. We've had questions and requests from the COEs and from researchers about whether they'd be able to get the raw data that sits underneath this to do their own analysis. And yes, that is available to you. Basically we're trying to be as transparent as we can, also realizing that some of the decisions, the cuts were just our first cuts at this that we made based on feedback from the field be looking at this data in different ways as well.

Someone is asking about what the field name is for the meta-cluster in the public data. I don't know what name the name of that field is in the underlying data set, Ally.

[interposing voices]

I haven't added the meta-clusters to the underlying data set, but I can absolutely do that.

OK. So Bonita Steal. Some of the regions do not reflect the same local micro economy-- for example, Central Valley motherlode had two very different local economies-- South San Joaquin Valley and Mountain Sierras. So this is not addressed in the data set currently. It is something we could definitely look at. I mean, in some ways, what's important to understand is this is not a finished product. So this is kind of a proof of concept that we could pull the information in, and then what we want to do is use feedback from the field and from the region and from consortia to help inform ways in which we could refine this or make it more useful to you as well. Also, I mean, if you know the institutions within each of your kind of subregions, then you could use the underlying raw data set to kind parse some of that out yourself.

Awesome. Yeah, that sounds good. So on my screen now, you should be able to see the dashboard itself. We're going to go into the live demo so you guys can look at each meta-cluster and the regions themselves and how the data will be updated and how to analyze it.

But we have a question about how the data will be updated in coming years. This is really-- this project is really just a snapshot in time-- as Randy said, a proof of concept. It took quite a lot of human hours to enter this data from publicly available resources, and we hope that we can make the case for continuing this work in some form.

Down at the bottom here, I'll jump ahead to this real quick, is we have a data update request. So as you look through this dashboard and download the publicly available data set we've collected and cleaned. And you have any questions or any concerns about how things are categorized or you want to update the record on what was offered or maybe change some of the ways we've categorized things, you can absolutely do that through this form. We'll be doing that hopefully throughout the rest of this year, of 2020, is updating this data set with any comments or questions or edits you guys have about what you're currently offering. And so that should be an easy form, hopefully, for you guys to fill it out. And then we will continue discussions about how to keep this data set up-to-date and continue doing this work.

And I just want to call attention to the fact that we fully acknowledge that this is a clunky way to update information for us. Our goal was not to create a whole new data system. And in fact, this is something I think the CAEP office would have to think about, about how to have a way for people to enter basic course information similar to the way colleges actually enter that data into something like COCI and MIS so that we could have a-- really see what's happening in pathways in a dynamic way from year to year. We felt it was important to have a way for you to share information and feedback with us, and so we created that mechanism in this tool, just so we can think more about this as this is kind of an evolving body of work that we're trying to think about how we refine together with the field.

So when you first land on this dashboard page, what you're seeing is all educational offerings and all labor market data for all meta-cluster, all standard occupational clusters, and all regions. And the first thing you'll want to do is select a meta-cluster. It's really designed to be tailored around those standard occupational sectors.

So let's look at health-- that's always a fun one to look at. And you'll see that our dashboard updates based on that health cluster. So this is now looking at a statewide view of the data of all of the occupation-- adult occupational training courses, all of the educational data, and then all of the labor market data for the whole state.

If you would like, at the statewide level, you could also select a skill category. This is directly from Centers of Excellence, how you categorize above middle skill, middle skill, and below middle skill occupations. So you can select that as well at the statewide level.

We've also selected a self-sufficiency wage filter. That doesn't work very great at the statewide level because it's really tailored to the regions, so we'll get into that in a second. So I'm currently in Oakland. I really want to look at the Bay Area. So we can tailor this now just to look at what's offered in the Bay Area.

We're now looking at all of the education offerings here across the Bay Area and all of the labor market data for the Bay Area. We can then tailor this to the self-sufficiency hourly wage for the Bay Area. So I just want to look at occupations that meet that self-sufficiency hourly wage, which I think is about $21 an hour.

And then I'd also like to tailor this a little bit to the just middle skill occupations. So now it's really targeted the labor market data. These two orange filters are just for that labor market data, and we can kind of get a sense of, OK, well, I want to look at medical assistant occupations in the Bay Area. You can see that it has the second most annual openings in the area, and it definitely meets the self-sufficiency hourly wage, so that's a great thing to know.

These occupational skills builder courses are also tailored by these meta-clusters. So we can look at hospitality, and then let's look at the whole state. Selects all the regions, and we can go down, and we can see all the occupational skills builder courses for this meta-cluster and for the whole state. We can tailor that as well to a specific region. So all the occupational skills builder courses are also tailored to the specific filters at the top.

And as we go down a little bit, we have our data sources listed here. So through this first link, the adult education non-credit data, you can click on this, and you will be directed to a box folder where you can download the publicly available data. So this is our public data set that is now officially published and live for you guys to download and review and have comments and send me emails with all your lovely questions. I'm really excited to hear back from you guys.

We also have here at the labor market data information and then how we categorized the self-sufficiency wages, which is based on Insight Center's calculator and the University of Washington data. And as I said before, we have our update data requests, so you can type out your name, organization, email, and send a comment to me, and we can get started on updating your data or addressing any comments you have. And as well, we have definitions in here. So you will not necessarily have to refer back to this PowerPoint. You can click here on the definitions for the meta-clusters, and you'll be directed to a document that has the definitions here. We also have the skill category definitions and the self-sufficiency wage indicator linked here in the dashboard as well. So do we have any other questions at this time?

We had a question about if we've considered using-- considered skills mapping from occupation to kind of related occupations. And while we do use skills to inform this tool, this is not a skills-based tool. We are actually looking everyone should know we are very interested in the issue about how you would identify an unemployed worker who comes from a certain kind-- they did a certain kind of job and helping them map their skills and understanding the relationship between what they've done to related occupations that they may not be aware of.

The translatability of skills is very important to us. We're actually doing some work with Paul Downs in San Joaquin Valley where we're actually trying to do some of this mapping between transportation and logistics occupations and advanced manufacturing occupations to build pathways that can move laterally as well as vertically along a traditional career path.

Yeah, and separately, we've started to build meta major clusters that include-- that are really based on skills. So we take occupations related to that educational area, and we analyze the skills based on MZ and create a little map. But we haven't gotten very out with adult education, so hopefully in the future we will head there as well.

We have a couple of private questions from people about their specific data that just if you asked that question, we will actually get back to you with a response.

Yeah. And you can use the form here in the dashboard for those questions, or you can email me directly. I'll type my email into the chat in a minute. And you are welcome to email me with any private questions you have about your data.

Allie, maybe do you want to pivot back to the deck. We had that next steps slide, just to--

Yes.

--move very quickly.

Hold on one sec. Here we go.

OK, so we're preparing a report with findings from the study. They're not going to be evaluative as much as just really capturing kind of the richness and depth and complexity and kind of discussing the analysis. We want-- this is obviously our kind of initial kind of blast of doing some state level kind of information releasing and a little bit of professional development.

We built into our scope of work on actually the strand for professional development related to specifically these data sets. So if you wanted to have us come-- or I guess we're not going anywhere currently, but if you wanted us to actually work with some of your local institutions around the data, what the data says, both to kind of refine whether we're getting the right information or whether we've made the cuts correctly between, say, occupational training and occupational skills builders to help us kind of refine our understanding about how to make that distinction, we are really interested in doing that.

We want to continually refine this as a tool. What we're trying to do is we're trying to build a proof of concept to really get-- the CAEP office is very supportive of this initiative, but the agencies overall to pay more attention to the actual career pathways that are being offered and not just students who happen to be in CTE. That we need to be providing data that really improves the ability of folks like you to have the right kinds of conversations, and we want to provide the information to do that.

The other, which is there is just-- to me, it's a pretty enormous data black hole in the launch board that I can tell you if a student is CTE or not CTE, but I can't really tell you what the heck they're doing. So we really want to know more about the enrollment patterns of students in CTE programs. And I can pull that for non-credit programs, but I can't get that for K12 adult education programs, which is not a knock on the system at all as much as it is a call out to the state agencies that there is some data systems structure issues that we think long-term need to be addressed. Because we want adult education to be at the big kids table around CTE and career and career development and career education in this state, and to do that, we need to make the data available the kind of support the really strong work that's being done and to kind of get additional state investments into this work for the future.

Looking at questions. People seem to generally like the tool, which is really nice. We know it's not perfect. Yes, the regions are the community college meta regions currently.

I know there is a super regions for the K12 with adult education system, and so we could have discussions about that. But currently, that was the easiest way for us to divide up the consortia. We send the recording. The recording will be available.

So we have actually 13 minutes. We were faster than we thought we would be. So we will-- Allie and I need to talk about kind of making the link available for people to begin to play with. We haven't set a target date for that, but what we might do is look at a little bit of the feedback here to see if anything pops up. I did see one concern of someone who's not seeing their institutional data in the underlying data set, so we would want to address that. Or if you see things that you're concerned about, you should feel free to share this with us. Allie, is the link public at this point?

Yes, it is.

Oh, I misspoke. I did not know that. So we're basically inviting you to take friendly shots at whether this is working or not or what you would like to see. This is the sort of-- we want you to try and break it, which I'm sure you'll be able to do in various ways. So please feel free to reach out to either Allie or I with questions about this or if you'd like us to do some more dedicated local work with your consortia. Can you post the link? Allie, can you type-- can you copy the link into the chat?

Yeah, it's a super long link, everyone, so apologies for that.

[interposing voices]

Up soon.

We probably need to create a proxy link or something.

Yeah.

It's in Google Data Studio. We have no idea how that's going to respond if 100 of you get on at one time, so that will be part of our learning experience. I'm not seeing a lot of questions at the moment. We will just hang out for a few minutes. And if nothing else pops up, then we may end a little early, but we're gonna hang here for a moment. We like the word "awesome." The word "awesome" make me happy. Thank you, Annabelle. Jodi said the link in the slide didn't work but the bitly did. CAEP TAP is reminding you to register for upcoming CAEP webinars.

Thank you, Randy. I was just going to chime in here [interposing voices] if there were any additional questions. But first and foremost, thank you, Randy, and thank you, Allie, for today's presentation. It was a lot of great information that the field definitely appreciates and is looking forward to hearing more about.

I did post a link for our upcoming events. That link also contains upcoming events and webinars for classes 010 as well as CALPROs, so definitely be sure to check out those resources and continue the process of learning with us throughout the fall. As I mentioned before, we will start our regional network meetings on Monday. So if you haven't registered for your region or another region you'd like to engage with, please be sure to register for those.

Again, we are centering the conversation around topics that are important for you all at this time. Additionally-- and I will post the link-- the CAP summit will take place October 26 through 29. At this time, registration is not quite open, but yet, we are accepting proposals. So if you have a strategy or a best practice or promising practice that you'd like to present at the summit, definitely be sure to submit a proposal.

We have a lot of great activities planned for this virtual space that we are in, so it will definitely be a meaningful event, although we will miss seeing everyone and interacting with you in person, but we're definitely planning for a great virtual event. So we hope that everyone will be able to either attend and/or present. So I'll post the URL for the call of proposals here in the chat as well.

And there were a couple people who were asking for the recording, as well as the PowerPoint presentation for today's webinar. That will be available first thing tomorrow morning, and I posted the URL exactly where that will be located. So definitely feel free to access that recording, as well as the PowerPoint presentation and share with colleagues who were unable to attend or if you'd like to reference it at a later time for yourself. And I'll also include the links to the dashboard so that that is available to you all as well on the website.

I want to-- Allie just responded to a question from Ryan that I actually I want to sort of call out publicly. So we have not in any way password protected this. It's not secured in any way, so it's generally publicly available information because we pulled it from publicly available sources. So there was nothing we were pulling from proprietary.

However, and we had this question come up because San Diego has been talking to us about this data because they knew we were collecting it, whether this was something students would use. We have not designed this to be community-facing or a student-facing tool. What we're just trying to do is collect the information and make it useful for practitioners.

Obviously, if you wanted to take the underlying data sets and create some sort of career pathway inventory for your region, if you wanted to use it to build a-- here's a career pathway system in San Diego and Imperial Valley across these institutions, do something kind of like what Orange County has been doing with program finder for K12 CTE programs, this is information that could be very useful for you in that effort, but we did not design it specifically for students. Thank you for all the very kind words I'm seeing in the chat.

It looks like it's getting pretty quiet in the chat, so I think what we may do is go ahead and pivot and sign off. When you-- mine and Allie's emails are both in the PowerPoint. So if you have pulled that down, you should feel free to email us or reach out to us. Things are pretty crazy around our shop for a week or two, and so if you don't hear-- if you email me tomorrow and you don't hear from me for two weeks or Allie for two weeks, just be aware that we're getting sort of setup for the new program year and getting a bunch of things moving forward.

And the federal government decided to release every funding available opportunity within like a three week window this summer, so there's some craziness around our shop, but things will clear up for us after the 14th, and we will try and be responsive to things like if you want to set up time to meet with us separately, we will definitely try and honor that as best we can.