[De La Salle University] How to Make Sense of New Analytics in Canvas Courses

Thank you, Canvas, for having me in this, space recitation. Okay. Thank Thank you. So my session is titled How to Make Sense of New Analytics in Canvas Courses. Can I get a raise of hands? Who here has used the new analytics functions in their Canvas courses? Okay. So, very few.

Because it's not actually one of the main features we use when we teach. Right? And sometimes, there is not actually very visible in the, the course itself. And it's, it kind of gives off a little bit of intimidation because it's, it delves into data analytics. But today, I'm going to show you how easy it is to use without actually going to, use, data analytics software. And we're going to actually just use the data the new analytics in itself.

How has the practice of education changed? I'm sorry if I'm just going to ask about your age. Who here was born during my generation? Millennials? Can I get a raise of hands of Millennials? They're legit Millennials. Not the one who are pretending to be Millennials. Okay. The generation before the what do you call the generation before Millennials? Gen x.

Okay. Raise your hand if you're Gen x. Okay. Raise your hand if you're the generation before Gen x. Boomers.

Okay. Silent generation. Okay. So, my generation, when we grew up, when we went into school, we actually first, experienced a lot of technological changes when it came to education. K? And one big, change is that we, during our grade school days, actually found ourselves using still using Manila paper.

Did you guys still use Manila paper in your classes? Okay. Okay. So Manila paper. After Manila paper, we slightly transition to OHP. Remember OHP? The overhead projector, the one you would use to make shadow puppets.

Yeah. And then when you would print stuff, when you would have to write stuff on the acetate, sometimes you have to pay a lot of money just to print one page for the OHP. That was twenty pesos back then. Now just basically free when you're going to use your PowerPoint. So after OHP, we went through the slides.

I remember in Zobel, I'm not from Zobel, we're from the enemy of Zobel, I'm from San Beda, Alabang. Is anyone here from Zobel? Okay. Alabang. Okay. So, from Sovel, I when we were in high school, I remember the teachers our teachers in Sandeira Alabang talking about the teachers in Sovel.

And they were using the slides pa. And for some reason, in Sovel, they had this technology that when you were going to use the next slide instead of using a clicker or mouse like a normal person, what Zobel teachers had to do was to step on the floor. So they went they went they went like that every time. And sometimes, they didn't actually it didn't actually work. They had to stomp and stomp and stomp.

But the slides just didn't work. So whatever. Let's go. Okay. Sorry.

And then, we went, of course, we also use notebooks. But after notebooks, we actually transitioned to laptops. I remember in our high school, we were not allowed to use laptops because we they were primarily used for games. But, when we went into college, we were encouraged to use laptops now. Now our students always use and bring laptop for classes.

And for for me, it's fine as long as they're not, they're using it for productive purposes. And now, after laptops, they're now using their phones. It's now rare, to see, laptops in classes because a lot of students just use their phones. Because phones are so strong right now. There are so our phones are technically stronger than the first, manned missions, the computers, the computers used by the manned missions that went to the moon.

K? Our phones can do a lot of things now. So that's how, education has changed. It has been primarily affected by the technological development over the decades. And I'm sure, if you're been a if you've been a teacher for a long time now, you have seen these changes. K.

So and one big change that we have also experienced especially during the pandemic are the learning management systems, especially Canvas. Okay? Who here during the pandemic was their first time to use a learning management system? Can I get a raise of hands? Okay. So, some people, it was their first time to use a learning management system. For me, it wasn't the first time because I enrolled in MOOCs before the pandemic. So at least I had some experience when we transitioned into the LMS era of education.

But for a lot of teachers, it was their first time to see an LMS. It was their first time to see, the Canvas. And it was their first time to see if they were not using Canvas. It was their their first time to see an insert brand name here that I cannot say. Okay? So, and in LaSalle, our Canvas instance is called Animospace.

Right? And here in Animospace where we put a lot of modules, we put a lot of quizzes and assignments, attendance and participation, the functions and features that you would normally use, in the integrations, the Google app, the Microsoft integrations. When we look at these service level, they are just that. They are functions. However, beneath all of that, beneath all of that, when we look at the usage of a student and we look at the usage of a church, what we actually see is data. All of that is data.

And when we look at, the data and we just look at, we just leave it there, it's wasted. Because data can, tell us about a lot of things. About our students, about our teaching and learning practices, and even about our, usage of Canvas and how we can improve it. So, what is data anyway? I'm pretty sure that if you, graduated, undergrad, you would say, I know what data is. But the problem is when it comes to data literacy, there's actually a very different, definition for data, formation, knowledge, and stuff.

It's the D I K W pyramid that we call it. So data, I'm sorry for the long code, is in in the information age are large sets of bits. Encoding numbers, text, images, sounds, videos, and so on. Unless we add information to data, they are meaningless. So remember that.

Data is meaningless. When we look at a spreadsheet, it's just that. We are not understanding what it actually means. When we look at a grade a grading sheet, it's just data. When we look at a CSV file, an excel excel file, it doesn't mean anything.

However, when we go up the d I k w, pyramid, that's when, when we apply meaning to the data that we have. So first, here at the bottom, we have data, meaningless and unstructured. It's just all over the place. K? However, when we go up to the next level, information, it now has something, it now means something. It's organized and now conveys something.

So when we look at the data, we have to add context. Who, what, where, when? What, when we look at spreadsheet, six what? Six days what? What is that six days? But when we actually apply meaning into it, that's when we have important. And we can now slightly use it. What is six days? Six days, meaning six days absent, the student, the student was absent for? Okay. Six points.

What does that mean? In itself, it doesn't mean anything. But when we, put meaning into it, six points out of one hundred. Well, that basically means that they failed. Right? But if it's six points out of five points and, given by a student by who? By who? Gotten by a student, that means they're really excelling with the quiz. K? Now we're look, adding information.

We're adding context. The next level is knowledge. Usable in professional in our professional and personal lives. How can we apply it now? If we already know that this student got six out of one hundred in the quiz, how can we use it? If we already know that this particular student got six out of five in the quiz, how can we use it? It's already why and how, the level of why and how. Okay? So we already had meaning, now we're already putting the usage and at the top of the pyramid is wisdom.

Being able to intuitively determine relationships. We're creating a framework. Like, when we collect all of the data, we don't, we don't need to run the data again. We already know what's happening. We already know what's going to happen when we present, when we encounter a similar situation.

And that's why we need to review and review and look at the data that we have collected over the years in our campus instances. Looking at the data, we already know what will happen and how we can help our students achieve better in their classes. Why are students, not going to classes? Why are why are students not engaging in discussion forums? If you look at the data, it might tell you something. If you have not looked at it, look at the data and still have these questions, well, look at the data and maybe you will actually see the primary reason. I'm going to give you an example.

So how do they make sense of all the data? In data analytics, it's basically the science of analyzing true data into, to extract useful knowledge patterns. Okay? The problem here is we're so used from a positivistic lens. Okay? We're used to having a theory first and then getting, a story out of the theory. In data analytics, that's kind of the opposite. K? From data analytics, our practice is creating data, creating a story using the data.

K? I know I know this is going to be, raising a lot of question marks from, social scientists in the in the, room. No? Especially me. My training was in psychology. So I was really based on social, social, theories. And then I had to data which fit into the theory.

And then I had to test the theory using the data. In data analytics, in the information age, it kind of flipped for me. So what I had to do now is to look for stories within the data. So my job was to look at the data and what, and ask ourselves what was happening in our school based on the data? What was be what was the story being told using the data? K? So what about in education? Data analytics can be seen in the, in education in two ways. First is what we call academic analytics.

So academic analytics is very macro. We take data from the registrar, we we take data from the student information system, national records, individual records from previous schools, and we correlate all of these and then generate a story for the whole, student. K? What does the grade of the student, from their high school tell about the grade of the student to be, in the future of their college journey? A lot of, research has been done here looking at the correlation, which is actually a big issue right now. Some universities in the West are actually using student grades and student grades demographics and, national dem national and individual demographics to determine if there's two if they're going to accept a student. Because if they're going to accept a student, they might as well make sure that the student is going to be successful.

And according to the models that they develop over the years using their data, what it, what they see that is certain students from certain demographics with certain grades will achieve better than other students that may be affected by, again, data analytics in part b a. I remember that, by the bias, by the model. K? Some racism happens in the data. You have to be aware of that. K? Another thing another way we can use data analytics is learning analytics.

This is very micro. This looks, only into your course, your classes. So learning analytics in Canvas can be done via the new analytics for your courses. K. It's very simple.

It's actually very fun to use because you'll be able to see the story that happened in your previous course. And then you might want to apply the lessons from your story in your next courses. So for example, this is a course from, one of the faculty members in LSEU. I'm not going to show who it is. Okay.

Okay. So in the main page, you will be able to see the new analytics button on the right side. If you don't see it there, you might need to enable it in the navigation. K? But usually, it's already visible. When you click on the new analytics, this is what you're going to see.

Okay? So right now, we're just going to see a lot of data. It means nothing. Okay? These are the average course grades per assignment for the course. Okay? So I'm going to put a little bit more data to evolve it into information. I'm going to put dates into the courses.

These were manually added. Okay. So these are now assignments, the average grades of the assignments with okay. If we're going to put, where was I? These are the average grades of the assignments over the course of the course. Right? So now, looking at the data without using any data analytics programs, what trend can you see? It took me about thirty minutes to, looking at this data just to make a story.

And it makes sense. I actually asked the faculty member if it makes sense for their course. Yeah. It makes sense. So what did I put the dates.

Okay. Here's the trend. Look at the group assignments. Look at the dates. Five thirty, five thirty one.

Six nineteen, six nineteen. Seven seventeen, seven seventeen, seven seventeen, seven seventeen, seven twenty seven, seven twenty seven, seven twenty seven, seven twenty seven. The assignments that were grouped into closer dates tended to, have a lower, trend going down. K? Tapos, I actually asked the faculty member, sir, I noticed that the assignments of your students, were graded lower if you, bundle them up into, smaller dates. And then he said, actually, that, yeah, my students actually complained.

My students actually told me, can you distribute the assignments? Because we're actually having a hard time, submitting all of the assignments. And that is why the faculty member, in their next course, when I actually asked them, told them this, said that they will, separate the assignments a little bit. And this does only happen in this course. This is another course by the same faculty member and see the trends again. So seven seventeen seven seventeen, look at the look at the, trend here.

Seven twenty seven, seven twenty seven, seven thirty one. And if you look at the, assignments that were actually, divide separated by a good, number of days, you can actually see that the grades actually went up. Of course, there are some exemptions, but the general trend is there. K? So this is just a mini story that you can create using data analytics, using your courses. There are actually we actually conducted a study using, academic grades and all of the functions in Canvas, with the whole university.

What? There there's a weird correlation, actually. I'm not sure if this is going to be true for your university. The number of quizzes, the number of assignments and quizzes, graded assignments and quizzes that were given by faculty members were negatively correlated with the final grade of students. So para sai nyo ba? Mas marami bing bini begin assignment mas mababa yung grade? What what does that mean? What's the story behind that? That's the only that's only a correlation. We haven't made a story for that.

Me, on my end, maybe it's because they were so stressed. There were so many assignments given by the faculty members, so it really bogged their grades down. Maybe it's a different reason. We don't know. But that's the one part of story, of data analytics.

Using, storytelling, you can generate a story from your data. Now the hard part is justifying that story. Here, we have data to back up what I was just saying that if you grade, make your assignments, consecutively, from the student's feedback, your grades are going to go low. Your students' grades are going to get low. Okay? So okay.

With the given data, if you're going to look at the new analytics of your data in your course, what story or stories can you tell? K? What will you see when you just look at the general trends in your data? What will the numbers tell? K? You have to immerse yourself in the reality of the numbers because numbers don't lie. What lies is what, your interpretation. Okay? Will your interpretation be justified? Or did I defend you? K? So going back, the d I k w pyramid, at the end, what we want to generate is wisdom. We want you to get an intuitive feeling on how to deliver education the best way you can. K? From the data, from the information, from the knowledge, you we want you to develop wisdom. Okay? So, that's my session. Thank you very much.