Data Automation + Slate: 2 Admissions Teams Transforming Transcript Processing

You are here because you wanna learn a little bit more about data automation, how it interacts and integrates with Slate, and how you can transform the transfer processing in your office. We're fortunate to have two phenomenal guest speakers with us today from Miami University and University of Texas at Austin. We'll hear from them in just a bit. But a few housekeeping items before we begin. All of your computer audio is muted, so the way that you're gonna interact with us today is through the chat and through the q and a button. So go ahead and use the chat to introduce yourself.

Give us your name, your role, and the institution that you're with. What's really interesting for us in these conversations is I expect a lot of people will be here today from the admissions office, but a lot of times, we have registrars as well. But give us your role and the institution that you're with. That'll give us a good idea of the types of conversations that we're gonna be having today. So we've got a number of questions for the guest panelists.

Justin's gonna do a live demo, but it really gives us a good idea when you introduce yourself of who's here and and what you might be interested in learning more about. If you have a question, and we welcome your questions throughout today's webinar, use the q and a feature. So it's a little different than the chat. That'll allow us to track which questions we've answered, which ones we have yet to get to, and we welcome your questions. I'm sure you'll have questions for Lindsey and Allison or for Justin as he does the live demo.

This is a topic that always gets a lot of questions, so use that q and a feature so that we can track whether or not we we got to your question. We'll try to get to as many as possible. And if not, we'll try to follow-up and get your question answered after today's webinar. This is being recorded and will be sent out to everybody that registered. So if you have to hop off five minutes early or maybe you have a colleague that you wanna share this with, or maybe on a Friday night, you're bored, pour yourself a glass of wine and wanna watch this webinar again, feel free to do that, but we will be sending out the recordings to everybody.

Quick agenda. We'll do some introductions. Then Julie Wakefield is gonna cover just the overview. Why are we here today? What are we even talking about when when we're talking about data automation? What are the use cases? Why should you care about it? How have other institutions utilized it? Then we have a q and a with Allison from University of Texas at Austin and Lindsay from Miami University. And then we'll go through a live demo.

And, of course, we'll save some time for q and a at the end and try to get as many questions answered as we possibly can. So let's first meet our panelists. And, Allison, I'll kick it over to you first. Tell us a little bit about yourself. My name is Allison Redke.

I am an assist director in the office of admissions, and I was actually brought on to the admissions team about three years ago. Actually, slightly over three years ago to actually help implement data automation to extract information from high school transcripts, which was ever important as we looked for something to supplement our optional test scores. And even though we've gone back to acquiring test scores at this time, we are still utilizing the. . .

That automation to find, all the features that we still have interest in off of these transcripts so we can still use a predicted GPA and collect rank and size and GPA types off of those transcripts. So it's been an adventure. And thank you again for joining us. We. .

. Allison did a webinar with us last year, and it was. . . We have.

. . We got a ton of great questions, so glad that you're with us again. Lindsey, tell us a little bit about yourself and about your role. Oops.

Hi, everyone. My name is Lindsey Marnell. I'm the director of admission operations at Miami University. I've been here for about ten years, and I oversee things like student search, decision release, and what brings me here today, application review. And we're gonna have a q and a with Allison and Lindsay in a little bit.

Quick introductions for the Parchment team. My name is Matt Sternberg. I'm the director of customer advocacy. So I I work with a lot of our institutions and try to share the stories. It's way more impactful when we have people like Lindsey and Allison sharing their perspective on on the products and services they use rather than us saying how great we think we are, just trying to actually detail the real use cases.

And I'm, in Grand Rapids, Michigan. But, Julie and Justin, I'll pass it over to you. Yeah. I'm Julie Wakefield. I'm on our Parchment Pathways team.

I'm a manager on this side. So, essentially, what my team does is handle, everything that has to do with incoming transcripts and helping our customers improve their their operational transcript process. And alongside me is Justin Rayner. And, Justin, you can go ahead and introduce yourself. Yep.

Thank you, Julie. My name is Justin Rayner. I'm the senior solutions engineer here at Parchment. So all things technical, how do our systems work, and how do we make sure to create good consistent flows within systems that you have in play already on campus as well as how our solutions fit into those. I've been with Parchment for coming up on ten years now, and I come to you from Phoenix, Arizona, up in the desert.

Pleasure to show this off to you today. And before we really get kicked off here, I wanna start with a poll. So, Julianne, if you could get that poll started, and it should be popping up for everybody. We really wanna get an idea. Right? Data automation, it's a.

. . It's an efficiency tool. It does many other things. It can be more than just efficiency.

It can be data quality, a number of other things. But I think everybody's here today because they want this to be an efficient process. So we wanna get kind of a baseline. How much time does it take your staff to manually enter the data from one high school transcript into Slate? And I really would be interested to see what the answers are here. What's the baseline for people? You know, what's the progress that could be made? Where are you at today? So we'll leave that open for just a little bit longer.

And then, Julie, you can grab the screen whenever you're ready, but I'll give that another ten seconds to vote. And then, Julianne, I think you have to be the one to close out the poll, and we'll see where we land here. Okay. So 38% of people say under five minutes. Pretty good.

Five to ten minutes, 37%, and 16% say eleven to twenty minutes. But there's 4% of people that's more than forty minutes. My goodness. Okay. So that gives us a good baseline and some good information.

But, Julie, without further ado. Thanks, Matt. Yeah. That's great. It's, I think, really interesting to explore what high school transcript data you currently are capturing as well.

So maybe, you know, that's another pull down the road, because maybe you're just really inputting that GPA due to staffing limitations or, perhaps you don't have the ability to record the coursework or course rigor. So, you know, after we typically are having discussions with our partner institutions that are utilizing this data automation technology, and we'll dive further in that today as well. But, really, they've been able to recognize that this tool gives them the the ability to capture more comprehensive data. And the technology really opens up some avenues beyond admissions and placement, specifically including maybe some institutional research and and student outcome prediction. So really interesting to to gather that information first, but a little bit about us.

As you know, Parchment's a familiar name. All institutions on this call likely utilize Parchment in some capacity even if that's just with a Parchment Receive Lite account to securely manage your incoming transcript. So we really appreciate the opportunity to be here with you today as your trusted partner, and we do recognize that numerous institutions have also upgraded to Parchment Receive Premium. They've integrated with Slate. We've really helped, you know, the process that way in automating the delivery of transcripts.

Today, we're gonna focus again solely on that data automation piece of it, so the next step of the process. So just a little bit further, you know, about us, just as a quick reminder, for over a decade, Parchment's been a market leader in issuing transcripts and securely exchanging 31,000,000 academic credentials annually. So that's via our extensive network of over colleges, and professional organizations as well. So as we dive a little bit further here into, that admissions landscape, we know that things have changed quite significantly. I heard Allison say that she.

. . You know, they were they were test optional, but it sounds like you've moved to requiring testing. So things have changed really rapidly, and we understand that. We know that admissions teams are really under more pressure, juggling a never ending stream of transcripts.

You have tight decision deadlines. And really prospective students aren't just applying to four to five institutions, but sometimes more likely up to 10 to 20. So that demand for answers, decisions really continues to increase. I actually have the pleasure of embarking on another college admissions journey this summer as my third child will be going. .

. She's a high school senior in the fall. So it's really interesting to see it from that lens as well. And, of course, we show Amazon and Uber up here because we know the expectation is speed. Right? Everybody wants that Amazon Prime experience and and all that you do, which is inclusive of, you know, college admissions as well.

But all in all, really, you know, the quicker we can get those data points and information into prospective students' hands, we know that the faster they can make decisions. And, really, you know, having that manual time consuming error prone data entry is just not sustainable. So just kind of digging in a little bit further to this, we. . .

We've been your partner for a long time now, right, with Parchment Receive. We've co developed the Receive premium integration with Slate, and and we care as a team. I could tell you on our Pathways team specifically, we have several of our team members who have been admissions directors, counselors, even registrars. And over the years, that is. .

. You know, has become very important to listen to their experience, but also to our customers and asking us for more. They want more than the secure delivery of documents to Slate. They actually want transcript details, and and we wanted to deliver for our customers. That was very important to us.

So that's why we have partnered with the company, SmartPanda. It's two and a half years now going strong. We know that they are the market market leader in OCR AI transcript technology, specifically. The SmartPanda team that affectionately refers to their tool as Raptor will use the term data automation interchangeably. Those are one and the same.

But, you know, the end goal here really is to be able to, you know, streamline your process, aim to free up some of your resources for more critical tasks, and even expedite that data entry and provide students with faster, clearer information. So this, you know, type of tool can support admissions, course placement, but also research and really enhance efficiency for a better overall student experience. So we'll get. . .

Dive a little bit deeper into kind of the nuts and bolts of this and what this actually looks like. We're talking about taking transcripts, those from Parchment Receive or Receive Premium, as well as other transcript sources as well. I think that's important to note. This is not just a Parchment solution. We know you receive from other vendors, so you can also process those other vendor transcripts in this tool as well.

So it'll automatically extract details, GPA, class rank, course information, course rigor, also, and then send that information directly back to Slate largely without a whole lot of manual effort. And this digs a little bit deeper into what I just described, but I think it's kind of a better workflow of transcripts that are coming into Slate. Again, those non. . .

Parchment transcripts as well as transcripts from receive premium. Taking those image documents, moving those into the data automation tool could be both high school and college transcripts processing those. . . That information and then exporting those details directly back to Slate.

So before I turn this over, to, back to Matt here, just a few last pieces here. We know that this is a competitive market, but I will say that Parchment and SmartPanda, their solution really does stand apart, and you'll hear that more specifically from, Allison and Lindsay. The tool, again, was specifically engineered for transcript processing. So it leverages the OCR AI technology, that adaptive learning engine that ensures a really rapid and precise format recognition. So unlike, you know, conventional tools that maybe you've looked at in the past or even potentially have purchased, it's immediately operational that offer.

. . Offers an advantage for all transcripts that have been run and processed through the tool already. So the experience of the technology is super important that we note. There's no need to train train the the tool.

It's processed over which is inclusive but not limited to 17,000 plus unique high schools. The data automation tool delivers accuracy and speed, speed, and it's been trusted by colleges and universities for the past five years. How accurate? That's always a question that we get. We've been able to analyze over 16,000,000 data points resulting in 98. 5% of the time the user accepts those data points with minimal editing.

And then how fast? That's always a big question as well. The transcript processing time averages approximately eighty five seconds, so that's the point of the transcript entry of the document to that data transfer back to Slate. So what. . .

You know, does that all kind of equate to and look like? And this kind of illustrates it just a little bit further, as far as potential time and resource savings. So, if you consider that annual volume, and we we know that this work isn't done annually, like, consistently. We know this is done more as a sprint to meet admissions deadlines. But just for the process of. .

. Point of this example, if you're processing 20,000 transcripts yearly where each transcript, maybe it takes you about fifteen minutes to process that versus the 1. 4 two, which represents the eighty five seconds, you could realize that using the data automation tool really can enable reallocation of resources, quicker decision making, and then also reduce potential backlogs and overtime as well. So that is it from from my side of things. Matt, I'll turn it over to you because we know that the ideas and experiences of our customers are the best resources for those seeking similar solutions.

So Yeah. And if you could stop sharing and then. . . Yeah.

Great. So we've got some fantastic questions in the q and a. So keep those coming. We will get to those. I think we'll have a number of those questions answered during the live demo and some other things, but we will be coming those throughout, so please keep putting your questions in the q and a.

Allison, we'll start with you. You shared a little bit of your experience with data automation, but let's go on the time machine once again. Why were you even looking at a solution? What were you trying to solve for initially with a data automation tool? So initially, UT Austin, when we pivoted to being test optional during the pandemic, the solution was data entry. And we were one of the schools that was extracting course information, course. .

. Title, of course, the grade, the credits, the rigor, the grade level, and then the subject type. So we were collecting all those data points, and we were doing it manually. And we were one of the schools that was at twenty minutes per transcript to extract in data, enter all those those features to it. We really didn't have a good QC process just because it took so long to extract the information.

Literally, the year that I came onto the project, I came onto it the last year when it was done manually. And as I said before, I'm not sure if it was in this group or prior to this group meeting, but it was to find different solutions to get that information because we were. . . Had a small army of of contract and seasonal employees that were doing this pretty much four to five months out of the year.

So when I started, we had, like, a staff of, like, a 100 plus doing this. Now they weren't all full time, but there's still. . . That's a lot of part time, full time.

At some point, it doesn't matter. It's a lot of people to manage expectations of and train and make sure that they're all on point. So we. . .

When we went to data automation, it was to streamline the process to get more consistent results. Because if there were errors, there'd be a pattern as opposed to I have a 112 people, and they're all making different calls on the data. So if we. . .

There was an error, it would be, you know, easier to identify because it would be a pattern across multiple applicants, multiple schools, multiple transcripts. So it'd be easier to identify and then hone in on and correct. It changed from data entry to being quality control. So I was able to reduce the staff by almost 75%, So that's great. I now have roughly, on average, The core time I do this is pretty much October through the end of January.

And so it's so much time saving and energy saving. And it. . . Like I said, it's easier to train those folks, and it's easier to identify patterns of errors and to work with SmartPanda to take corrective action on those things.

And, you know, Paul with SmartPanda has been extremely receptive. Actually, before I came into this meeting, I was actually having. . . I'm actually just meeting with Paul because we are taking up the AI functionality.

We had been using, you know, the the old OCR model of learning and, you know, and using strings to identify courses and get data on there. So now we're pivoting with using the AI and leveraging more of the AI, and we're working on some of the customizations with it because, you know, AI does a great job with extracting courses, but not every course that, like, high school or AI understands to be math. UT Austin wants us. . .

Wants to tag with the subject type math. So right now, we're going through and making some customizations to make sure that what's being extracted is tagged on. . . Tagged as, you know, the subject type math is actually what we want as a math or vice.

. . English is being tagged as an English. You know, we don't want a lot of remedial classes. We don't accept those as.

. . And then some other very unique and one off courses that, like I said, AI does a great job with understanding it's English, but UT Austin doesn't necessarily want it returned as an English course. So it's just. .

. Like, it's too short. It's just been time saving for us. It's a lot of. .

. Saved a lot of energy and a lot of resources, and it's allowed us to reduce staff, which allows me more time to work with Paul, SmartPanda to make product improvements and pivot and make adjustments as necessary going along as opposed to be supervising an entire army of folks and having almost little time to interact and train people and get things remediated and updated appropriately. Yeah. You were running a small business of, you know, having to do all the QA and the QC. It was significant.

A 100 part time people is is a lot. And we did get the question early on in the q and a. This could be for undergraduate admissions. There's many, many institutions that use it for transfer applicants as well, college transcripts, and Justin will get into that a little bit more. But just to clarify for everybody, you know, today, we're talking largely undergraduate admissions, but a lot of the same rules apply for a college transcript.

Lindsey, why did you start on on this journey? Why even explore a solution? Like, what were you trying to solve for at Miami University? So Miami got about 43,000 applications for first year admission this year, which is a a 12% increase over last year, which I believe was an 8% increase over the year prior. So so every application we receive is reviewed by a person, and review consists of kind of, I mean, picking through the transcript to find these courses in a handful of subject areas and then calculating a rigor score based off of those courses. So so what I'm I'm sure many many of my colleagues on the call can empathize with, more applications means I need to hire more seasonal people to review them, and more people means I need more money. So my budget was just kind of ballooning year over year without any time savings or improvement in data quality whatsoever. So I started looking about this time last year for something that, at minimum, would allow us to standardize transcripts in in a certain certain layout so that we could improve that data quality and save some time.

And my my pie in the sky dream was to automate the calculation of our academic rigor score altogether. But but that was that was our main goal, was really cost savings and data quality improvement. So when you say the rigor score, that's basically taking courses that you want and then kind of taking into account weighted versus unweighted and getting a better idea of the the strength of an applicant based on all the different factors that may come in from a high school transcript. Is that Exactly. Fair, Lindsay? Yes.

And tell us a little bit about where you're at. You're kind of nearing the tail end of implementation. Right? Allison's been live with this for a little bit, but where are you at with implementation, and how has your implementation experience been? Yeah. So so we started implementation, I believe, in I believe in early March perhaps. So we we.

. . We're hoping to use this with our twenty twenty six applicant pool, but we're actually going to get to start using it early on our deposited fall twenty twenty five students to pull down their final GPA and their graduation date and their Ohio statewide student ID, which up to now, this has been just a a totally manual process for our processing team. So so it's in it's in great shape for the fall twenty twenty six cycle. It's pulling down all of our courses that we wanna pull down and labeling them correctly.

I do have it rendering in our application reader in a standardized format, which is such a huge win. And I I can tell you I'm really excited about being at kind of the tail end of implementation. I think we're gonna move to production next week. Our processing team is really excited about being able to use this immediately to kind of streamline some of the manual work that they're doing. And and I, you know, I I really wanna actually give a shout out to Paul on the Smart Candidate team for being so very responsive in helping us train the AI driving Raptors' OCR and interpretation of the course So it's it's going really well, and we are almost done and ready to move to production.

I'm glad that you highlighted that. Pretty much everybody we talk to highlights the attention that they get in terms of customer service and implementation, and I think that is a very underrated and kind of. . . When you license or purchase a product or solution, this is not just true for us.

It's true for anything in higher ed. You really kinda don't know. Like, here's what the product does, but things happen, or we have a unique environment, and I've heard that time and again. And so it's great to hear. And for everybody, you know, listening to this, I'm sure they've had experiences that went phenomenal with a partner or vendor and ones that they have a horror story with.

So, you know, I think that is an important thing to highlight, one thing that we're proud of, and and Paul at SmartPanda certainly does a phenomenal job. This is a data automation plus SLATE webinar. Allison, I'll start with you. Tell us what is the flow of information at University of Texas at Austin? How does the data come in, get into Raptor data automation, ultimately into SLAIT, CRM, SIS? Anything you can highlight about where the data goes. So it starts with us export.

We take in all the transcripts first. They could come from Parchment or Speedy or any of the other systems that we take transcripts in mail. We image them into our system, and then we export them over to SmartPanda. They then do the data extract. My evaluators go in there, review the data, make any sort of corrections that need to be correction.

. . Corrected. Because like I said, we've switched over to more of a QC. They are still looking at all of the transcripts to get the data point.

. . To make sure the data points are correct. But by and large, you know, their interactions with most of these transcripts, it's, you know, a minute or less with these, which is important because, as Lindsey was saying, you know, these applicant pools, they're going up and up and up. With UT Austin joining the SEC, you know, we saw a huge increase of, like, You know, we went from 65 applicants to 91,000 applicants.

So, you know, one of the reasons to do, that automations is because, you know, as Lindsay also pointed out, you know, the applicants are gonna outstrip your resources and money isn't growing on trees around here. So we need. . . You need to be savvier with how you get this information and extract this information.

But, once we get. . . Once my evaluators, make their corrective actions, they hit a submit button, basically, and it imports it back over to SLAIT. SLAIT is our.

. . The system that we use to administer our emissions applicants. We used to be a mainframe school, homegrown SIS, but we're pivoting to SLAIT to manage all of our admissions records for freshmen as well as transfers, and now they're opening up a graduate instance for that. And once we have it back in, Slate, that's where I come in and QC it because, these are seasonal part time staff that I use.

So there still might be some, errors, and that's where I run all my reports out of Slate to catch any more data integrity errors before I release it to, get, an academic index or basically a predicted GPA calculation. We are doing that in Slate, and that was. . . That predicted GPA was, the touch point we were using instead of test scores to understand an applicant's, strength to be admitted and strength to be admitted to a specified major because, as we all know, everybody wants to be an architect, an engineer, computer science, and those are, you know, majors that, you know, are very restricted.

You know? And with 91,000 applicants, it's even more important that we understood the appropriate placement of people. And being in Texas, you know, we're beholden to a lot of legislative rules about who's gets automatically admitted. You know, UT Austin, most schools in Texas have to admit top 10%. Ut Austin has an agreement with the legislature as long as we keep up, diversity. We can admit to, we can change our, admit percentages based on our applicant pool.

This last year, it was 6%. Next year, it's gonna go to the top 5%. Those folks are gonna be auto admitted, but. . .

To the university, but then we have to have a way to understand strength to major. And like I said, that this data extract as as well as test scores is this most recent cycle where we were introduced to acquiring test scores is how we got that information. And a lot of good questions in the chat. So we had a few questions. What is SLATE? So SLATE's an admission CRM, student life cycle management tool.

So a lot of people use it to manage applications, admissions, and basically make sure they have all the information about a student. And it. . . Some people use it for, as Allison highlighted, not just undergraduate admissions, but other other things as well.

So if you are not a SLAIT school, you can still use SmartPanda or data automation. I saw a few questions about that. So you don't have to use SLAIT to use this. Think about it like you have a CRM or you have an SIS. It really is kind of agnostic in terms of where the data can flow.

So, you know, you say, what about. . . Can it go directly into Banner? Yes. What about PeopleSoft? Yes.

No. There's a. . . There might be another CRM you're using.

It really is pretty customizable. So Allison shared one example of of how the data flows, but you may have it very different. And, again, if you're using it for transfer, it might look a little different. Allison, one clarifying question for you. Are you using this for just admits or just.

. . You're doing it for all applicants. Right? All applicants right now. Right now, we are focusing on our Texas applicants because, as I said, we have a lot of laws surrounding those.

So there's a very high level of interest to making sure we get those Texas applicants under the automatic admit rules correctly in the system, identified correctly, and calculated that predicted GPA correctly so we can make those admit decisions very rapidly. The OCR data automation is is really helping with that, get that information extracted timely, very quickly so we can turn around those predicted GPAs and have those out there to our leadership so they can make those tough decisions of who admit to. . . Admits and admit to majors.

And, Lindsay, same question for you. How do you, integrate with SLAIT? Where does the data flow at Miami University? So we are also kind of going from SLATE to Raptor back to SLATE. So that's just a pretty easy back and forth transmission since we don't involve our our SIS in this in this process whatsoever at this point. So so that's done just via an SFTP drop and just an automated pickup that SmartPanda helped us set up. So our process actually works exactly like Allison.

So we we export our transcripts from Slate. We have Raptor read the transcript, and then our readers make any corrections they need to make in the Raptor user interface and then submit that transcript for reimport as a data file into Slate. And and it's from there that I can use I can import those those files and then use Raptors course categorization to display those courses in that standardized format for our application reviewers. And, Lindsay, you're right in the middle of, you know, implementation or maybe at the tail end of implementation. There's probably people on the line that are like, I'm excited about doing something, but, man, another project for our IT team, or they're just wondering what the lift is.

You kinda highlighted when you started, but, like, who was involved on your team, and and what does it actually take to kinda make this happen? Like, how many resources are necessary to to get this up and running? That's a great question. So so we did not involve our IT team at all. So, really, the the implementation team consists of maybe maybe six people. So me and someone someone who reports to me who just knows our our transcripts backwards and forwards. And, actually, I I would say that she has been doing the bulk of the legwork to train the AI.

And and we have a few people also in our enrollment operations office. These are our kind of, like, our application processing team. And then we have one person in our registrar's office too. So no IT presence whatsoever. I think the the the bulk of the time that we've spent on implementation has been just kind of training the AI and troubleshooting the problems we run into, running those by the SmartPanda team, and and then kind of running running transcripts through again after that issue has been resolved or after the AI has been trained a little bit better.

And it it has not been that heavy a lift, I would say. I mean, it's been about three months, and, you know, it's gone gone a lot faster than I expected it to go. Well, Lindsay and Allison are gonna hang out. I wanna make sure we have enough time for Justin to go through the live demo because I see we have a ton of questions in the q and a. Justin may be answering some of those during the live demo, but we'll circle back, we might have some more questions for Lindsey and Allison.

But, Justin, I wanna give you enough time, so I'll pass it over to you. Great. Thank you, Matt. I'm really excited to show this off to everybody. And I will share my screen now so we can take a look at the SmartPanda application that we mentioned.

And you'll notice that familiar name, Raptor, we talked about as the affectionate name of the AI tool here that we have on screen. And its job is to take in documents, which you can upload or I like to also do a little drag and drop and process it. And the AI will take a look, at the transcript highlighting key features like coursework into a readable data format that we can send to the next system. We've got a lot of questions in the chat about, you know, SLAIT, but also Banner or the SIS. We really wanna create a format for this data that's deliverable to that next system.

And we might target things other than coursework, like the GPA or the dates attended, class rank or size to help you determine those, data points to send over. And this will just take a second to process, but while it does, I wanna also show you the dashboard. This is where most of our users interact with transcripts because they're coming from a system like Slate. We can send them from Parchment. However, you're collecting them today, we wanna help funnel all of those transcripts into this tool so that you can review them already extracted with the number of courses here.

You can open up a a preview panel to see some of that spreadsheet data as well as the fields we've captured. It highlights the student's name and the school source and then some statistics about how well it did in processing that. So users normally will go through here for any documents that need review. We also work with schools to help automate this further and determine your level of confidence with the AI reading and saying, okay. Well, if I get a lot of Bishop Gorm High School transcripts and I don't make any edits to this document, then I can just fire that over.

I don't need somebody to review it. But for many staff members, there's still a review process that takes place to make sure that I'm grabbing what I need and it's organizing it as, we want it to in the next system. That transcript that I uploaded, it's done processing now, filled out that spreadsheet. And off the transcript, we get a lot of these columns here, the year, the title, the units, the grades. But sometimes we have to interpret or pull out things like the honors, rigor, IB, as we find them on the transcript.

You'll see it's been highlighted in green and red to pull out those important categories and the grades associated to them. Dividing up the credits, Here, we have point five across this English one course that appears in two line items, because of those two grade splits, and we divide the credit. Same thing with this one course here. We've got two credits to associate with the IB course here, and so it's divided that up by one. So it's all about constructing the data in a very presentable way to you that you can then edit.

If I wanted to change this English course, I could add something, honors, if I wanted to. I can delete rows of data. I can insert anything I want. If it doesn't capture coursework I was looking for, then we can help make that adjustment. This is your spreadsheet that you can drag and drop like you'd expect Excel or Google Sheet.

You can copy and paste, control c, and then control v on my keyboard. And you can even grab things off of the document. This lasso tool is a favorite of a lot of our members to draw a box and highlight different text that it's highlighted, whether it's green or red. The green is just trying to show you what's on the spreadsheet, the coursework, and some of this other data that it either ignored or maybe you wanted this whole English title. And we can take that and paste it in here if you wanted the complete set.

It allows you to grab multiple sets of data. And if you grab two lines here, it'll paste them in both, since they're, you know, indented there. But that allows you to manipulate it to your heart's content. Our our goal is to shorten your time to get this data into that usable format and then push it on into the next part of your process for evaluation. Talked a lot about that happening in slate today, but we also work with a lot of members, that wanna have that happen in their SIS or, in another system.

This is a general workflow that describes where this would typically be slate. We kind of showed a workflow like this before, where you're collecting transcripts from other sources and parchment. And those are usually associating to maybe even, like, records, an application, a student, and then, transcript received. That system can send us the transcript, that is ready for processing. I saw some questions about, fraudulent or, you know, detecting those transcripts.

We're really trying to tell you what it says, and usually depending on a system like SLAIT or an imaging system or CRM to hand us a transcript that has been determined, I I wanna capture this data and see what it says. We do compare it against other ones that we've seen from other institutions, but its goal is to read it and deliver that out. And in our conversation today, that's been back to slate, which I'm gonna show you some screens of as well. But for many of our, transfer applicants or graduate admissions, we might wanna deposit that directly in systems like Banner, Colleague, PeopleSoft, and deposit that in the external education pages. So we work with schools to determine what that workflow is, what documents are in scope for you today, and how can we connect those to the next system.

I also wanted to show you, on this dashboard here, I have some sample documents. We can open up and see that read is already accomplished here, and some of that coursework. It's highlighted the honors in, some of these cases when it found, those different markers. And if we move down to the raptor fields, you can also see it'll highlight for you the weighted GPA or unweighted GPA if it found those items. We also work with schools to calculate these items.

Some members do that in systems like SLAIT, but sometimes we work with them to say if you have rules around how you wanna transform this data when I get a four point o or a five point o scale, we can work to transition that to the next system. You also might want test scores, ACT scores, of the like that come off of the transcript so we make space, for those to be transitioned as well. And, in here, I wanted to highlight Allison's point on the AI evaluation of some of these, transcripts we're doing for schools. If I click this, we're looking now at this subject area. Is, AI determining based on the course title and pieces, how do we bucket these things, so that we can get accounts and help calculate some of those areas before we send it over.

So this is using different logic to help make that determination. But, you may not need buckets. You may want different buckets of, course titles or subject areas that you can pick from. And we work with members on what you wanna organize this data into. Because our job is now to either, from this screen, you can deliver as well and submit it or, approve the data here and push it.

I'm gonna highlight now, into, our UT Austin example where they have, we have an example dataset, from them here of a high school transcript that has those subject areas, grade level, and rigor that we might pull out. And in their slate system, this is that courses table that is delivered into, English, the credits, the grade level, the honors. And if we were to highlight one of these rows, for example, this is the row detail of us delivering that coursework line by line so that you can now work on it in that next system. As I mentioned, we do this for, SIS systems as well. We just wanna talk with each school individually to try and target what are your specific needs, where this data goes.

And sometimes that might be setting up one instance of this tool for high school transcripts, one for college transcripts to go, to that other system. It all is up to your workflows as they exist today. So I also, with this, wanted to then highlight the end goal. We saw that workflow and where the data lands, and I mentioned it previously that if you're coming in here to our tool to review all of the transcripts that are coming in, this gives you that proof check, quality assurance, AI is doing what I expect, and we can run reports that show you how many edits you're making on the proofs of those documents and say, if you wanna automate this and not have to review documents from certain schools or based on these statistics, sometimes we work with schools to utilize how well is the AI filling in the data, how well is it comparing this transcript versus others and develop a confidence score. That is usually more prevalent in automating it in our, transfer applicants of, college to college coursework.

I wanna give credit, but that you wanna just push that data in. Because as we saw in my example today and and from Allison, there's evaluation on what are all of these subject areas that these courses fall into. So there's more time for some institutions spent in this tool. And others say, you know what? I wanna try and send everything over to the next system and I'll deal with it there. I just am confident that the AI is able to read what I want and push it over in the format that I need.

So that's the goal of most of our members is to, automate it and and have this review process with minimal documents, but I can successfully send over ones that, read, with that high confidence level. We can also develop other queues, for exceptions or other team members. We really wanna adapt to how you wanna make sure the data comes off transcripts correctly. Alright. I wanted to try and, leave us enough time for q and a as well at the end here.

So I wanna also look to pass that back, to our team and and handle some of those questions so I can pull back up the tool if we we get specific, but let's talk through some of those that, have come in. Yes. Some great questions. So, the international transcripts question, Justin. Can it handle international transcripts? Yes.

We take in international transcripts for a lot of our members today that typically go through a translation service like WES, and we we view that report. The challenge is that the data is not always structured in the same way that we expect units and grades and titles. And so there might be missing elements, but our goal is still to target what is there and pull out and structure that for users. It may take, candidly, more copy and paste or editing that I showed because it's not format the right way, but it is a vast improvement starting from just the document that you have to type all that data out. And we're getting a lot of.

. . . . .

Military. Sorry, Matt. Military is in our scope as well that I like to include in this international piece. And we're getting a lot of questions about, just the flow of of information. So someone asked, why does the transcript have to go to Slate first instead of being sent directly from Parchment to SmartPanda.

Allison or Lindsey, do you wanna tackle that one? Yes. So with so many applicants, we don't always getting transcripts. I get pretty nice pictures of where they went to school or their dogs sometimes, But sometimes they're not sending us transcripts. Sometimes they're sending us profiles instead. So there is a validation step that comes in Slate where somebody says this is actually an official transcript.

It's not something that's not a transcript. So it has. . . We have to validate that what we're getting is a true transcript before we release it over to, SmartPanda because we.

. . There. . .

It's gonna be very difficult to extract courses from, like I said, a, you know, a picture or a profile. And I'll just add that it can come directly from parchment if that is your. . . What you would like and you prefer, so, there is an opportunity to do that as well.

Yeah. I most of our members find that putting that validation step in play to make sure this tool is getting all the right documents that it knows where they go, in the next phase. Matching always has to happen. And so you can do that in our tool, but we found a lot of success with members who take in transcripts first and qualify who that is and then send it to this tool. So that's our preferred, but like Julie mentioned, we'll we'll send them directly and show you the results.

I just need you to then tell us where to put it, decide who that person is. Lindsay, did you want to Yeah. So at at Miami, we. . .

Our our transcripts are coming from multiple sources. So, so we would love to receive every transcript through Parchment and just have a feed from Parchment directly into Raptor. But but that's not possible when they're getting uploaded through Common App and through Score. And, you know, sometimes students will upload unofficial transcripts or sometimes we'll receive them on paper. So so for us, it's just not not feasible to capture every single one of our transcripts using Parchment alone.

And I'm getting a lot of questions about transfer as well. You know, we're. . . Specifically today, we're kinda focused on the admissions workflow, but I just put in the chat a webinar we did with the University of Cincinnati who's using it specifically for transfer.

And so that that can be sometimes dual enrollment, you know, transfer courses, but I would recommend checking that one out too because Molly McDermott Fallon goes into more detail about kind of the use case for transfer and and their experience. And with transfer, it's a lot more, okay. How do we think about the SIS, and how do we import that information into our SIS for for transfer credit? So that would be a good one. But in terms of transfer credit, Allison or Lindsey, are you doing anything with transfer credit today? Because I think it would primarily be, like, dual enrollment courses. Is there anything to add? Right now, UT Austin is not using it for transfers, but the end goal is to use it for transfer.

. . Transfers. It's. .

. We want to use it for as much end to end and as many populations as we can. And I know that SmartPanda got its initial start doing the college transcripts. So. .

. But the end goal has always been to do it to extract high school and then leverage it for transfers down the road. And we are also, right now, just using it for our first applicants. So. .

. But I know our our registrar's office is looking forward to having data from dual credit coursework and AP courses sent to them in a in a data file instead of having to retrieve that just by looking at transcripts themselves. We have a question for you, Allison. People are basically asking how. .

. What kind of quality assurance and quality checks you're doing. Like, are you individually going into Tron? How do you know which ones to actually review? Tell us about how you do those queries. Sure. Sure.

Right now, my folks are. . . Look at a 100% of them, but they usually aren't making corrections on all of them. They spend usually less than a minute just long enough to look at it, scan through it, said, yes.

We extracted all the courses we need. Yes. They're the right subject types. We got credit. We got grades.

And they, push it back, to slate for us. What they're doing in there, there are some that just the transcript is not the best quality or it's something that SmartPanda hasn't come across before or it's. . . You know, there's an option in SmartPanda to do alternate views because maybe this is a transcript or a school that likes different types of formats or layouts.

So we've we've done different things to, we do different things to improve the quality of the work that we're pulling back across to us, and those can take a little bit longer depending on the quality. And those are the types of things that we still do a call out to Paul and let him know that we got a bad extract. This. . .

You know, it's maybe 20% accurate, but that's few and far between. But my folks are looking at a most of them are, you know, a minute or less with review. And we have a a question about that too. Like, help. .

. You know, you talked about a Like, synthesize for us, like, for each individual transcript going from your previous process to today, like, what would you. . . How would you highlight the difference in time for each individual transcript? Well, in good old days, which depending on your point of view weren't that good, it was twenty minutes to data enter that information.

There was very little QC that was going on in there on the front end. It was usually falling out as a problem when it came time to do that predicted GPA. It was. . .

You know, their class rank and the predicted GPA were just dramatically off or something was just saying this kid's got a point two GPA, but yet they're the valedictorian. Where do they go to high school? What's happening? You know? So there were things in that the early days when it's just data entry that, you know, there. . . Because just so.

. . Because it was so time consuming to get the information we need to get that predicted GPA. There wasn't a whole lot of q. .

. QC involved in it. This has changed the entire dynamic of it to say that it is now a QC heavy process. That's strictly all it is. My folks are not that entering these, you know, once in a blue moon, maybe something just needs.

. . Somebody manually needs to go in and just fix a 100% of it. But that is just few and far between. This process is now strictly QC.

It is not data entry at all. And we we got a few other questions on. . . Can it tell that this is the final transcript? What about the initial? I.

. . Do you recommend waiting to process the final? I think you have to process the initial. Right? You're. .

. Order to grant admission, and to do that, you have to do the initial. So I think that's gonna be the process. We do do the initial. We do request that it be an official transcript, so there's at least some, you know, assurance that we're getting what we need.

But. . . Yeah. But we need to do those initial ones.

I know there are some schools who are. . . Depending on what the school's needs I know some other schools aren't doing. .

. They're only doing it to make. . . To look at it after the admissions is over.

I'm not sure that I understand that exact process, but I do. . . I've heard from other schools that they are using it in different ways. But, yes, we need to.

. . We use leverage it to make admissions decisions and admit to majors, we need to do it on the initial applications. . .

On the initial transcript. Sorry. And I just put the book a demo link in the chat because there's a ton of questions we didn't get to. Every institution has their own unique CRM, SIS, use case. That's gonna be the best way for us to show it to you in more.

. . Even more in-depth than what Justin went through, and you can tell him to stop, ask questions. That's the best way that you can get all your questions answered. So great.

Thank you, and great job from all of our panelists. We really appreciate you sharing your perspective. Lindsey or Allison, is there anything I should have asked you or anything that you wanna highlight before we wrap up today's webinar? We're pretty certain that Paul is AI generated. I hate to be that sharp. .

. . With that. He's that sharp with the customer service. Yeah.

He built himself. Yeah. We're like. . .

I actually asked him in one of our first meetings as we took. . . Started taking on the AI. I was like, are you AI generated? You can tell us.

Well, what. . . Would the AI tell you the truth? That's the question. Well, join me in thanking Lindsay and Allison for joining us.

Again, we we try to have, you know, your peers and colleagues share their perspective rather than just Parchment saying, we're so great, and you're gonna love this. Right? To actually see its its use in action is fantastic. And we're really proud of how we we serve institutions in terms of customer service implementation. And so I wanted to share this. Like, don't take our word for it.

Talk to other people. You know? Like, if if you wanna talk to other people that have implemented data automation, we try to do as many of these webinars. I shared the University of Cincinnati story in the chat. Check that one out, but talk to other people. Like, we're not a perfect company either, so there's lessons learned.

An institution might tell you, hey. We wish we woulda known this before we embarked on implementation. So we wanna utilize the community so that, we all get better from it. But I wanted to share this. our average customer satisfaction score.

We're really proud of that, and, very satisfied with the people that they work with at Parchment. And, yeah, we share that because that's the standard we hold ourselves to. Are you gonna be at the SLAIT Summit in Las Vegas? We will be there, so check us out. I don't know if you'll be there, Julie. Maybe.

No. Julie's shaking her head. No. I won't be. No.

But, yeah, two of our our wonderful colleagues will be there and and happy to chat with you further. So, yeah, wherever our booth is, I'm sure we'd love meeting you there, but appreciate everybody joining. Again, thank you to, excuse me, Lindsay and Elson for for joining us. And I just realized that I am wearing same exact shirt that I have in my profile picture. That is.

. . I need a bigger rotation on my shirts, I I realize. So just wanna leave you with that little funny tidbit. We will send out the recording to everybody.

And, yeah, if you have any questions, don't hesitate to reach out to us. But thank you very much, and we'll talk to everybody real soon. Goodbye. Thank you.