Driving Success With Data

Alright. That's such people already filtering in. Just gonna give it a few minutes today, like the past few sessions, but I'll just give everyone some time to filter in. New face today in science minus annex. We'll give some introductions. It'll be very simple.

As well. So welcome everybody to our third and final installment of this webinar series. Think we'll kick it off right now as a few people still filtering. We can start over the introductions. Today we're going to be looking at driving success with data with me.

I've got Alex Carey, our senior solutions engineer. Alex Carey is not only a time set up lookalike. I've read, I'd call him the old Tom Cettic, but most people call Tom Cettic, the old Alex Caring. He's going to be taking us to a deep dive of data within Canvas. So we'll be looking, and a lot of ways you can use that data to really help drive some of the success within Canvas itself.

For everybody else who's already been to some of these sessions, anybody who's new, you might recognize me from the past few. I have had a haircut, so I look a lot more like my picture these days. I am the head for UK, from UK and IFE sector within structure. That's why I've been part of these series in a, a familiar face. As always, if there are any questions, please feel free to throw them into the chat, and after the sessions, if there is anybody who wants to have a deeper dive into any of this book or some specific use cases, you can always reach out to me directly and we can set a session up together.

So, without further ado, I will hand it over to Alex Carey. And he will take us on the deep dive of Canvas data. Lovely stuff. Thank you, Joe. So I'll introduce myself as well just briefly here.

Again, so my name is Alex. I'm a senior solution engineer here at Instructure. You can probably tell from my accent. I am not British. I do live in London.

I'm based out of London, but I am Canadian originally. So I I've been over here for about five years now, and I've been within structure as well for five years. So I started with the team in early twenty nineteen on the implementation side of things. So, it was my job to work with institutions and organizations predominantly in the UK, but also around the rest of Europe, and help them in the early days of of getting up and running with the platform and with Canvas. So I've had many, many conversations over the years, relating to integrations relating to data.

Basically, any of that technical type of stuff I've had, infinite conversations. So the the topic today around data is really, one that is near and dear to me. And Joe, obviously, I need to go and update my photo. You're looking closer to yours. I'm looking much further away from mine.

So, I think we need to get some some new head shots in there soon. So with that, let's talk about the topic for today. And set the scene a little bit. Okay? And by the way, before I do that, actually, any questions that you have as I'm going through the the conversation today. Anything you want to put into the chat, Joe's gonna be keeping an eye on that.

So he might answer directly through the chat functionality. Or we might bring some of those questions, sort of into the conversation as well and answer them verbally. But, all of that is to say, please feel free to to engage directly within this webinar. It I don't need it to be just me sitting here talking to you. I want this to be a bit more collaborative and getting an understanding you know, where you all are in your your journey with data.

Right? So the power of data. What does this actually mean? Right. Regardless of industry, vector, occupation, whatever it may be. Data holds the power to unlock significant insight into what you're looking to achieve. Right? It holds a lot of that power.

It's telling the whole story of what's going on in the system, but it's up to us as the owners of that data to actually delve into it and ask the proper questions to gain that insight. Right? So let's put this into the context of education. Obviously, as we all know, the experience in education has really been redefines over the past several years, let's say, with the pandemic happening, obviously, driving the incorporation of technology, much deeper into the teaching and learning experience. Right? So the side effect of this is that we now have more data than ever before on this process on teaching and learning. So at least in my opinion and many others as well, it would be a massive misstop opportunity if we were to ignore all of that data, all of this new data that we now have access to.

Right? So the aim of the session today is to explore the power of data that's coming out of your ble. Right? Ultimately, we'll be looking to get a better understanding of that data that's coming out of the platform. Because if we have a better understanding, it means we can feed this back into decisions that are being made for things like course design, decisions that are being made for the overall user experience, and many other things as well. Right? So our main focus of course will be on data coming from Canvas in particular, But the questions that we'll be asking of the data will be applicable across platforms, much more applicable to just, education at large. Okay? So before we dive straight in, what I do want to do is get a quick understanding of sort of where everyone is in that data journey.

So we have a very quick poll, which I'm going to launch right now, and you can see the questions on the screen. But the idea here is or the question here is how does your institution currently utilize data and analytics in the teaching and learning environment. Okay. So there's no wrong answers here. And just answer the with the option that sort of is most relevant for you.

Okay. So I'll give a a couple of moments here until everyone has a chance to respond. Perfect. So I think I can share these. You can all see what those look like.

So this is great. Seems like we have a pretty widespread of experiences here with the majority looking at academic performance tracking. That makes sense. Operational, administrative efficiency. Right? Making sure that your administrative teams are being as efficient as possible with usage of the VLE.

And also a couple that are in the really early stages. So I think the the point I want to make here is that different institutions, different organizations, will always have slightly different priorities when it comes to approaching data. So, like I said, right at the start, there's no wrong answer to this type of question. And in fact, it it it makes the point that everyone is in a different point in that journey. Okay? What we'll do today is we'll try to level the playing field a little bit and bring everyone up to a bit of a foundation that we can begin to work from.

Okay. And one thing I'll I'll I always like to make clear in these types of conversations anytime I'm talking about data, talking about data and analytics, that side of things. When we're when we're starting out here, you should all be always be trying to map out your approach to data really early on. Okay. Specifically highlighting things like the types of questions you would like to ask and the outcomes you would like to achieve.

Okay. So keep that in your mind since we start having a look at where Canvas, where comes into play here in terms of the data side that we want to go in with questions to ask of the data rather than just going in expecting answers to sort of, open up to us automatically. Okay. So some of you may very well have seen this graphic before, possibly even in one of the other webinars in this series over past couple of weeks. But for those who haven't, these are the six key pillars of the Instructure learning platform.

Okay? Professional developments, learning management, assessments, content, online programs, and analytics. Okay. So I like to bring this up here because of the six pillars or, let's say, the six priorities, in our minded and structure. Analytics is one of those key priorities, those key pillars. Okay.

And analytics, in the context of instruction breaks down to sort of two major elements here. Okay. And that is the data and the analytics. And we've said it many times. We've basically named this session data and analytics.

But the way that we approach this is we actually just we separate those two a little bit in the way that we approach them, in conversation and the way that we approach them in the platform as well. So what do these two things mean? So we have data, wave analytics. In the context of data, what we're talking about is the instructor data access platform. Okay. The vision behind the data access platform is that it is to be the single source of truth of data and it will help provide efficient access to that data for various instructor products.

So for those of you who are, currently using Canvas, for example, you'll know that as our main product, our the learning management system. But You may also know we have Canvas Studio, a media management tool. We have Canvas catalog. Right. We have Canvas credentials.

So there's a suite of products that all constitute the Instructure Learning platform. The data access tool is what unifies those and allows us to deliver that data in a single format, okay, all while providing it with high fidelity and low latency as well. So that's the data side of things. What about the analytics side of things? So, when we're looking at analytics, we're thinking about the platform analytics tools. That would be our course analytics and our administrator analytics.

Okay. These are tools that are built directly into the platform. And are accessible by users through the user interface itself. Right. The intention here is to provide actionable analytics in an accessible way.

Alright. So we've set the scene here a little bit. What we're gonna do is we're gonna dive into each one of these. Okay? We're gonna spend some time looking at these two major elements of the data and analytics vision for instructure, and there'll be some elements of of demoing here. I'll show you in the platform as well.

And where we'll start is with the platform analytics, actually. So we're starting with this second one here. Reven being that really more often than not, this would be your first port of call for analytics within Canvas yourselves. Okay? So we're gonna start here. Okay.

So platform analytics. As I mentioned, it breaks down into course level analytics and account level analytics. Okay. So we'll take a look at both of these. Right? We'll see these in action in a couple of moments here, but there's a a number of commonalities between these two tools.

Okay. The main idea is that we want to provide these analytics in an easy to use way. So that users directly through the interface itself can interpret what they're seeing and action upon it directly. Okay? So it's gonna be aggregating data at the subaccount level, aggregating it at a the course level as well. And the key part here is that it will have built in visualizations.

So directly within the user interface, it will be, generating some of these visualizations so that you can, again, quickly and easily have a look and see, you know, what some answers to your questions might be. Okay. So with that, let's jump into Canvas and let's actually have a look at this. In action. Okay? So starting with the course analytics.

Right? Let's imagine that I'm logged in as a teacher here, and I'm looking at a course that I teach, and I'm looking at the analytics tool. Okay? We understand your that there is a massive importance of analytics in all aspects of education, but Specifically, there there are some extremely vital analytics for teachers themselves specific to their courses. K. Sometimes we, you know, we look at data. We look at analytics at way too high of a level.

But when we break it down to it, We go down right to the course level. There's some extremely important data and analytics to look at there as well. Okay? So course analytics tool is built into every course within Canvas. And what constitutes it is a number of, dashboards. Okay? So we have a course grade dashboard, weekly online, online activity, students, reports, and so on.

So what we can see here to begin with is the course grade dashboard. Alright. Quickly and easily, I as a teacher can see what my average course grade is. I can see average grades across different assessments as well. So for example, I have a midterm assessment where the average grade is just about fifty percent.

I have a final assessment where the average grade is right right up around seventy five percent. Okay. So very quickly and easily, I can visually make some assumptions here, right, where the power starts to come into this tool is that I can start to ask questions like how does the overall average compare with that of an individual student. Okay. I'm gonna add in Nathan Leach here.

Suddenly I have a number of, additional data points on here. And I can start to compare Nathan's performance versus the overall average. Okay. On that same midterm assessment, Nathan did better than the average. Right? Significantly better here.

But for that final assessment, Nathan did not very well. Okay? So we can start to map out some of those differences, in comparison to the average here very quickly and easily. The final point I wanna make here is that it's all well and good to see this. Right? I can see that there are some differences here. I can see that you know, there's a particular spread of averages here, but the power is being able to action upon these types of analytics, okay, this final assessment.

I'm gonna click on this data point here. And first of all, I can see the distribution of grades. Okay. I can see that five students were between five to eighty, seventy five to eighty percent, to students ninety to ninety five. Okay.

That's all great. This envelope icon, this will be visible sort of throughout the course analytics tool itself. And it's called the message students who functionality. So if I click on that, this gives me the ability with a couple of clicks to communicate directly with students given particular criteria. Okay.

Let's say in the range of zero to sixty percent, I want to message these students. There's only two students who are in that range of zero to sixty percent. Clicking that, I can see who that is. David and Nathan. That's perfect.

It's gonna BCC see them. They're not gonna know who else is being, contacted. But I can now send off some communications. Maybe they just need a little bit of extra help. This was final assessment.

So they need some some additional information before sort of, let's say, a final exam. But Again, I've not had to go as a teacher. I've not had to go and figure out who's who's graded what, who's where. All I need to do is use this tool and say, you know, zero to sixty, or maybe I wanna say from seventy five to one hundred. I wanna give it congratulations to the other seven students who did quite well.

Okay. So that is the most critical part of the tool here is that teachers, while it's important to give them this type of analytics, the most important thing that teachers are going to be, concerned with is what is the outcome? What can we change by using this? And the first step towards that is the communication tools that are built into built into this analytics dashboard. Okay. Similarly, we can look at the weekly online activity. I'll put Nathan back in here for comparison as well.

So we can see, for example, in this case, instead of looking at grades or scores, we're looking at engagement levels. Okay. Page views, participation, By the way, participation here, this will be, submitting in assignments, contributing to a discussion, completing a quiz that type of of, engagement within the platform. So again, we can see, Nathan's participation levels versus the course average. Okay.

So that's pretty helpful. Same thing down below. We can start to see by resource how many page views there are. So the homepage makes sense It's a landing page. Everyone's viewing that.

But we have the grades being viewed. We have, of course, discussion topics being participated in. So we have the spread of data here. And if we wanted to again, we could use that message student too. Functionality.

And let's say, within the discussion topic, I want to message all of the students who did not participate. Okay. Again, I don't need to go in as a teacher, go and figure out who has added to the discussion who hasn't, I can use this functionality in the click of a button by messaging these five students about, why they didn't participate in that particular discussion forum. Final main dashboard here is the student dashboard. K? Looking at this, you can probably already start to pick out some, some trends here.

So things like, you know, how does a student course grade compare with the on time submission rate. Right? Something like how does the participation level line up with their grade. Okay. John Woods has only participated seven times and has a forty eight percent. Is there a correlation there, not necessarily, but servicing this type of information allows us to start making some informed decisions and informed communications as well.

Okay. So we've looked at the course diagram of the analytics. Let's switch gears a little bit. I'm going to show you the admin analytics tool now. So I'm I'm taking off my teacher hat.

I'm putting on my admin hat now. So administrators will have access to this tool. Okay. So it is there for the administrators, rather than sort of the teaching stop for example. And is there to give visibility into usage and adoption across the institution.

The again, they can help it can help them make better data informed decisions to improve student experiences, improve outcomes, and so on. So, you know, things like You know, we can start asking questions like how do we define an active course, how engaged our learners throughout the system. How do we make the learning more engaging? These are all very open questions, but they're things that we can start to ask of this data that we're about to see. And administrators can start to, you know, pull some trends out, pull some insights and possibly tweak the way that, they're approaching the usage of of Canvas. Okay? So just like the course analytics tool, the admin analytics tool is broken up into, dashboards.

Right? So we can see we have an overview dashboard. A course dashboard and a student dashboard. Okay? Each one will have its own set of reports that are built into them that we can see below, and we're gonna have a look at these, a couple of these in particular. Each report you can drill into as well as their interactive, the enrolled students, I can click on this with activity, and it's gonna bring me up a full list of the use the students who are enrolled and have activity. Okay.

And I can even download that. As a CSV file, a text file, Excel spreadsheet. However, you want to look at it. So, again, a couple of clicks of the button, and we have filtered targeted data that we can pull out of the system if we want to. Okay.

The other commonality between all of these dashboards here is that they will all have the filtering functionality. This is one of the most powerful parts of the whole dashboard. Reason being is we can pull this up, and we can start to hone in on exactly the the, subsection of data that we're looking to see. So we can filter by sub accounts, by term, by course, Right? By course status. We can put some course start and end dates on there, get some date ranges.

Most powerfully, We can set criteria for course activity and for student activity. Okay. So what I mean by this is you as an institution can define what constitutes an active course for you and what constitutes an active student. Okay. We just looked at that enrolled students with activity, without activity.

This is completely defined by this filter right here. So if I started to change this here to, let's say, minimum number of five page views. You can see these change very slightly. Now we have a much smaller percentage of students with activity, about thirty five fewer students with activity. Okay? So because different organizations will define activity, you know, in different ways.

We give you the ability to to own that filter and say, I don't think a student is active if they've only viewed one page. I think there needs to be much more than that. So you can again begin to build those filters completely on your own. Okay. So the overview dashboard, the idea here is that for administrators, you're getting an idea of, across the institution, across the the usage of the platform, what sort of the landscape looks So for example, of all of the courses, you know, what are the core statuses? What are some average grades? Interactions by category.

So you can start to see of all of the features in the system what is getting the most usage. You can see files, are way up there. So maybe, as time goes on, you want to actually have some of that type of engagement be moving over to our more interactive pages or discussion forums, for example. Right? And we can even see over time interaction, whether those are page views or participations. Okay.

The course dashboard, this is much more around, the design elements and and the types of features that are being used at the course level. Again, You'll see throughout your feature use in courses with activity. That's all going to be based on those filters and those criteria that you've set. For course, for an active course. Right? But we can see the Canvas features that are being used.

And, also, the feature use for each type of, for each category. Right? So our file is being used quite a bit, modules, discussions, grades, and so on. Okay. And finally, the student dashboard, here's where we start to, put more of a a student experience lens on. So we're looking at, in this case, sort of the submission breakdown.

K. Are we looking at on time submissions versus late submissions excused, information about overall averages? And down below, for each student, we can actually see the landscape of what they're enrolled in, what their grades look like, average scores versus sort of the institutional average. Okay. So we can start to get quite a lot of information out of this as well. Okay.

So in summary, you can see for both the course analytics, as well as the admin analytics, they're built to be easy to use, and they're built to save your teacher's time, save your administrator's time. But still to help them identify trend and actually action on those directly from the platform as well. Okay. So we've we've tackled one of those major key elements. If we remember back to that graphic, we've tackled the reporting and analytics side.

Let's step it up a little bit. Let's have a look at the data side of things, the data access platform. Okay. So to to reiterate the vision behind the data access platform, the idea is that it will be, and it is the single source of truth of data within the instructional learning platform. K.

So for all of the different products that are out there, all of that data will be unified into one place that you can retrieve it in bulk with high fidelity and low latency. K. So you can quickly get access to very granular levels of data. K. So This will help to streamline pulling that data from the system, and leveraging that in sort of whatever way you want to whether that is pushing it into your own data warehouse or database to use in your own learning analytics, that's sort of one of the main functions or whether it is being used, by partners, for example, to push into their own tools.

Right? There's a number of different ways that we can start thinking about this data access platform. But today, what it allows you to do is it allows you to download raw data from your Canvas instance, with four hours of latency. So four hours of data freshness. Okay. This is top of the top of the line right now, I would say, in terms of latency levels, four hours means that within a day, you can get a full picture of what's happening within your platform.

K. There are, just under a hundred unique data sets tables here that we're able to to capture today. That number will continue to increase as time goes on as we bring in, more tools and bring in more more data sources into the data access platform that will go up. And you can choose to download this data however you want to in terms of formatting. So JSON CSV, CSV, If you wanna be super efficient, you can use parquet.

Right? So there's we give you the flex flexibility in terms of how and and, in what format this will come out in. And one of the main differences here when we're thinking about this compared with the analytics tools, is that you will connect your own business intelligence tools for visualization. Okay. So this is the big part. Our built in tools have their own built in visualizations, but they are standardized.

Right? We have built them to reflect the needs of the larger user base, whereas when you get your own access to your raw data, and you connect, let's say, Tableau or Power BI, you have full control over that and you are able to connect and and create your own visualizations really to your heart's content. Okay? So we're gonna take the next probably ten minutes or so and have a look at end to end what the process might look like to leverage the data access platform, hold down some data. I'm gonna push it into a database, and then I'm gonna show you an end product, what it might look like once we connect our BI tool. Okay? So to start out, we'll have a look at the documentation. Okay.

So I'll back up to the top here. So we have full documentation with the Data Access platform. Okay. We do have an entity relationship diagram as well because this is a, relational schema it means that things can get a little bit messy. Right? Lots of connections within the system, a course is a pretty, base level building block within Canvas, which means it's gonna be connected to many, many things.

Okay? The idea here is that because it's a relational database or a relational schema, I should say, it will have a primary key right here. And it will have a number of foreign keys connecting it to other tables. So the account ID, grading standard ID, and so on. Okay. Another way to look at this, if we look under the Canvas tables, and we go and look for courses, this is that same information so we have an ID.

That's the unique identifier for the course. And we have all of the other attributes that are here on this object, okay, in the description of the, format of that particular attribute and a description of, what type of information we're actually gaining from that attribute. Okay. So our documentation is extremely thorough, and it goes through every object that you have access to. So it's a great place to start when we're thinking about, the data access platform.

Now the second big thing is that we do have, an API that we've built, okay, to go along with the data access platform. This means that you don't have to go in and manually say, I want the courses table, and I want this table, I want that table. The API allows you to script all of that. Okay? You can automate the process of pulling whichever tables you want, maybe it's a collection of twenty tables, pulling those, and pushing it into a database somewhere. Whether that is a local database, whether that is a, cloud database, something like Amazon Redshift, Right? You can use the API to programmatically provision those those databases.

Okay. Even better, we've built a command line interface tool leveraging that API so that you don't actually have to do the API scripting yourself, leveraging this tool, which is a Python tool. We've built a couple of standardized commands that will allow you to sort of build up that process much much more quickly. So for example, common use case, obtain a full snapshot of a table and push it into a database. Okay? This is called the init DB command initialize database.

And all it requires us to do is to select the table that we want to initialize. And also to to provide credentials. So username, password, and the database that we're connecting to. I'm not gonna go through that part today. We're just looking at sort of the the concept behind this.

But again, what I can do with a single command, I can pull data from Canvas and push it into a database. Okay. And that's what we're gonna do right now. So I've taken the Liberty of setting up a connection. Okay.

So I already have, set up a Postgres QBO database called DAP demo. K. So that's already running, ready to go. I'm going to be connecting to it here so I can see some things visually. K.

But most important is on the left hand side here. I'm gonna run a single commands to show the power of the CLI tool. So DAP, right, I'm calling this program, this Python program, Gonna use the sync DB commands, which basically is just it's going to go check to see if there's any changes since the last time. That I pulled this table. And I'm gonna specify the courses table.

Okay. So what this should do is it should start pinging the data access platform, it will check to see when the last time I did this was, and it's going to look for any deltas in that courses table. Okay? And if there is anything, it's gonna pull it down, and it's gonna push it into my local, SQL database. Okay. So I'm gonna go ahead and, launch that commands.

Here it goes. It's asynchronous. So what it's gonna do is gonna check every five seconds to see if the job is completed. So it'll probably take fifteen or twenty seconds here. As it's as it's running.

One thing I'll mention is that we do also offer a service, that will mean that we basically take ownership of this intermediate part. Okay? So we will push your data into an Amazon Redshift space and just give you access to that red shift space. Okay? So if some of this is starting to look a little bit daunting, that's fine. We have a service that will sort of just allow you to connect your BI tool to Redshift and start building reports. Right? Okay.

But the documentation I was just just showing on screen there shows full information on how to install these these tools, it's generally a pretty quick and easy, thing to to take care of. K? So as that's still running here, what I'm gonna do is I'm gonna connect to that database. This, again, like I said, it's just a program so I can show you what the the end result looks like. So I'll connect in here. And I can see within my tables.

All of the the the previous tables that I've pushed into this particular database. So for example, I can have a look at the accounts table and see all of the information that has come out of the, out of the instance. I can look at the users in here as well. Right? And this is where we will be connecting that the, the BI tool, the business intelligence tool, okay, where we'll start building some visualizations. Okay.

So that is that final step. And what I'm gonna do is I'm gonna pop over to to Tableau here. And show you what that end result is. Okay. So this might look a little bit again, a little bit daunting on screen here.

These are the same Pables that I was just showing you. Over on the other side, we could see the accounts, we could see the courses and so on. All I've done here is I've indicated what those relationships looked like. So I've connected the courses table to the accounts table based on the account ID. Okay, the primary key of the accounts, the foreign key within the courses table.

K? So really simple connections here, but I've built out a full relationship three here. And ultimately, the sort of things that we can start to to create from this, and this, you know, these are just examples. I want to be clear. There's many, many things that we can that we can pull from this data. These are just a few that I spent a bit of time in in in whipped together for this conversation today.

But, you know, for example, one of the questions we might ask is what do interaction levels look like across the VLE? And how does this compare to comp to performance? Okay. So we can see things like enrollments, submissions, discussions, quizzes by year, Okay? We can see pass and failure rates by term. Right. I've set an arbitrary cutoff of think I said fifty five percent or sixty percent. Though if it's above sixty percent, it will be a path, and otherwise it will be a fail.

So we can start to see so in comparisons there over time, over our turns. We can even even see things like distribution of battery percentage. So when we're looking at outcomes based assessment within the platform, we can see, the percentage of mastery by students. And if we're looking at average, average mastery, we're seeing that across the different departments at this particular institution. Okay? What about the second dashboard? What sort of course design figions are being made.

Okay? We can see the spread of external tools that are installed by some accounts. We can see for all of the modules that are within a given, department or a given school, for example, what status do they currently hold? How many have been completed? How many are still in a locked state? Right? And then we can even look at sort of, higher level data of course composition. Right? So by course, How many modules, how many pages, assignments, discussions, quizzes. So we can start to look at the spread of those base level elements within the course and see you know, maybe we're seeing some correlations by the type of content or the type of courses that are utilizing them. Okay.

And finally, one that I know is obviously quite important for, the further education space, at least in the UK. I was time being spent in the VLE. Right? Average activity time by account and hours. So we can say we can see a forty averaging around forty hours per enrollments, twenty five hours for the history, accounts. Okay? Average grading time.

So from the time of submission to the time of grade posting, what is the delta? Keep in mind, this is all demo data. So I'm hoping that most people wouldn't have two hundred and nineteen days. In between submitting something and getting a grade doc, that would at least drive me insane. And finally, quiz percentage by time spent. Right? Again, if this was real data, we probably see slightly more of a correlation, but we're seeing a scatter plot with percentage So how well we've done versus how long we've spent.

Okay. So the idea here is that, you know, the again, these are just examples, but with the data access platform, we're giving you full control over your data so you can build your own custom reports and dashboards. Alright. What I've shown here, this goes well beyond the built in analytics and reporting that we have at the platform level, which means you'll be able to answer much more nuanced questions because you will have that full control and full flexibility. Okay.

So throughout our conversation today, we were asking questions of our data. I talked about this right at the start, but the most important point I want to get across to you is that we should be asking these questions. If we're going in a little bit more blindly and exploring data, That can be helpful at times, but having a more targeted data approach tends to be much more rewarding and tends to lead to much better insights into that data as well. Okay. So I'm seeing a question in the chat here.

So query around time measures how accurate can this be. So that's a great question. It will depend on the type of metric that you're looking at, really. Okay? So if we're looking at something like, the the deltas in the grading time, right, this is accurate, you know, to the second, basically, because we're looking at two time stamps. We're looking at the submission time stamp and the posting time stamp.

Of those grades. Okay. So taking the delta, that one's pretty straightforward. Thing with quizzes time spent, there is a quiz time log effectively of when you've started it versus when you've completed it. When we're looking at average activity by accounts, k.

This one's a little bit different. So this is based on your enrollments. You will there there's a particular that is tracking time. But there are barriers in place so that if a user, a learner is sitting on a browser and just has a page open for forty eight hours, it's not counting that forty eight hours. As, sort of, active time.

Okay? So there are some some sort of further nuances in terms of the the way that that is approached. But, I think that the the one thing that I would say is that we always try to look at this from a balanced perspective of not just time spent in the platform, but also the activity levels things that are actually being done, right, the engagement levels, page views, participation. So that's why we saw those at the, at the platform analytics level. Okay. So with that, I'm happy to stop and take any other questions, if the if anyone does have any, Otherwise, I can pass it over to Joe, and I know Joe has a couple of things to to talk about before we wrap up for the day as well.

As per usual, I always won the last vote. Yeah, thanks everybody for joining us. There's been three really good septum. I think you've also all enjoyed them. We are planning on doing these more in the future as well also based off recommendations from from current customers and things we hear in the market.

So, you know, some webinars already plan on doing in the future, so it will be around AI and and other new topics coming up. But more but in more recent news, we do have an event coming up on the first of November and Liverpool. Our cameras connect event there where we're going to be diving into topics like AI, lots of different areas and bring lots of customers and prospects together. So really more of a session where people can connect with each other discuss what they're doing at their institutions. It's a free event all in Liverpool on the day, reading through to see, you know, if anybody here is planning on joining us there as well.

You can see Alex has put up the web page for that as well. So you can go there and register if you'd like to, like I said, completely free and you'll get to see us in person. You can actually verify that we do have legs as well, not just two floating heads. But yeah, if there's no other questions coming in, Sarah has just graciously brought the link in the chat as well. So if you haven't been to the link or registered yet, you can do that there.

Otherwise, thank you for joining us. We'll hope to see you guys either at the event or at future webinars. And thank you to Sammy for joining just in your comments come in. Yes. The resources will be shared fairly sought, fairly shortly.

After this webinar, we've been waiting to have all three of them ready to go so we can share that with everyone that's been joining. Anybody who's might miss one or two of them. So yeah, that those resources will be with you very soon. Don't you worry, where our marketing team will be hotly on that one as well. My pleasure.

Thank you everybody for joining. It's been a good one. It's been a great one. We will see you all very soon and have a great rest of the week. Thanks, everyone.