[De La Salle University] Promoting Networked Learning and Computational Thinking with Canvas' Mastery Paths
Good afternoon, everyone. So I will be the final speaker speaker for this session. So I hope, you still have the energy to listen to my presentation. So it's always been my dream to share my passion for teaching. And thank you, Candace, for, giving me this opportunity to share my passion to everyone. So, for today so let me just see.
Yeah. So for today, I will be discussing, promoting network learning and computational thinking with Canvas Mastery Paths. So I decided to go into the pedagogical side of education because, in conferences like this, we always talk about the technological side, the technical side. So that's why I would like to, just go to the other side of education. K? So AI has been the top of the town.
Obviously, ever since, it was launched, ChatChippity was launched, so everyone is talking about it. Even in this conference, we are hearing about this a lot. So can you please raise your hand if you have used AI yourself, Whether in education context or maybe personal, use cases. So or have you heard of AI in trainings that you attended? So yeah. So a lot of us have heard about this.
So educators are on debate whether to use it or not. So universities are putting their best effort in crafting policies, in crafting guidelines and principles in using AI. But beyond that, we must focus on our pedagogy as well. Because it will not be enough to just have guidelines, but it would it will always be best if we will equip ourselves as educators with these pedagogical skills. Okay.
So I would like to touch first on the neural networks. So neural networks are the key component in making AI intelligent. So who among you have heard of this term, neural networks? So if you are from computer science, you are you definitely know about this. So neural, networks is the computational model that enables AI to recognize patterns, process information, learn from data, and handle nonlinear relationships. So, when, developers, develop AI, they feed information to teach it patterns so that it could become intelligent or what we call now AI.
But we all know that, neural networks of AI is based from the neural networks of the human brain. So definitely, our brain is more capable than AI. But the problem is we don't know how to unlock it. We don't know how to, to, to to use it to teach our students. Now, because of that, it might be beneficial if we approach education nowadays in the lens of network learning.
Have you heard of this, educational theory network learning? Sim meron po ba, kadiring natinong network learning. So I'm seeing a few hands. So network learning came from, the theory of connectivism, wherein, it encourages students to connect with all the learning nodes that it that he or she has around him. So to define it for you, according to Goodyear et al, he, he described network learning in which ICT is used to promote connections between one learner to the other learner, between learners and tutors, or maybe, you can change that into teachers, between a learning community and and its learning resources. So this is the definition that we have for network learning, and it is it is not something or it is something that is not really, widely used, especially in the Philippine context.
K. So to to make it more, familiar to you, who among you is a member in the FB group of homebodies? Homebodies. Homebodies or maybe kas kasan bodies, gong alam niyapu yun. Okay? Yay. Digital banks groups or something, similar groups like that.
So, we join groups like that so that we can learn about the tips, the experience of others, or, the hacks that others use in homes. And it makes us more interested in that subject area because we we find a connection with that material. So nobody, nobody is required to, nobody is requiring us to learn those things, but we try to learn this thing because we are we feel some connection to that. So that is an example of network learning because there isn't a connection learner who's trying to learn about credit cards or something like that. So that is an example of network learning.
Now, in the context of classroom, so we can do this by, making our classes something that, students can connect with, whether personally or maybe, professionally if you are in a higher education department. Now the question is, how can we do this? So it's it seems like it's a complicated process. So what we need to have as teachers computational thinking, because it originated in the computer science. So in computational thinking, you have there a decomposition process, pattern recognition, abstraction, and algorithm. So in the decomposition process, you break down a big problem into small manageable parts.
And then in the pattern recognition, you observe the similarities of these patterns. And then for the abstraction, you identify the important parts of the problem and of what they do is, they identify the difficult parts of a system and they try to break it down into, manageable manageable parts and then they create step step by step processes to, to help the user of the system achieve a goal. K? Upload or send email. So step by step process. Now in the education context, you might, you might be using this, unconsciously already.
So when you are teaching students, like for example, addition. Addition techniques and patterns so that the students might learn the the equation better. So that is an example of computational thinking. But right now, medyo nagiba na siya because of the existence of AI. Right? Especially, chat based AI where the students can now chat, the the the tech, the chat GPT, and they can learn a lot of takes.
Now what na isan po ba na isop? No? Bakit bamas busunile yung ChatGPT more than just listening to their teacher. Now we will go back to the definition of network learning. It is because they feel connected with ChatGPT. Because with ChatGPT, they could, contextualize a conversation based on how they want to learn or their personal interest so that they could understand a specific, concept better. K? Because they feel that connection.
Now with Canvas MasterPaths, it could help us, provide this connection for our students. So for those who haven't used MasterPaths, so MasterPaths allows us educators to create personalized learning for our students depending on their needs and interest. With this tool, you can set conditional assignments automatically. So you can identify the different interests of your students, their different difficulty, their different preferred learning modes, and you can provide different paths so that they could feel more connected with your material or with with the content and skills that you are trying to teach your students. So let's try to, use the computational thinking to, in using mastery path.
So first, in the decomposition, we design a course. So we decide, the assessments, the projects, the learning content that we want to give our students. And then and the pattern recognition, so most of the time we conduct, like, a pre assessment in our classes. Right? So this might be contrary to the other instructional design models kasi parang paliktad no? So not so monod yung, leads in interest before the design. We are talking about, design na natin.
Course natin. But for some reasons, like, we don't expect, we didn't expect that there's two there are students who are not feeling connected with the the course that we have designed. So upon the pre assessment, it turns out that your expected, differences of your student is differences of your student is different with what you have imagined or what we what you have researched especially that Gen Z and Gen Alpha student is very diverse. It's really hard to, confine them in one, one subject area. And then after we recognize their, needs and interest, we set up the conditional mastery paths depend depending on those needs and interest.
And then after that, the mastery path automatically directs students on what to study and activity to do based on their individuality. K. So let's try to do this in a math subject. Kasi ite fin na kah madale kasi objective siya. Right? So maybe sa movie, Hindi madale sa niyo pero, pag LM naman po yung ginabit mo example.
Ayan. So we can create a mastery path based on mastery level. So you can create a quiz, and then based on the score they got, they will be receiving different, different, content or different version of the content depending on their mastery level. So in this way, those who are already, advanced, they will not be bored because they will get a different version of your lesson. And then for those who needs more attention, they will get, like, a lighter version of the explanation of your content.
So that no one will be left behind. So done. Another use case of Mastery Path is we can create a Mastery Path for their preferred mode of learning. So for example, there are students who likes to watch a video. There are students who prefers listening to audio only.
And then there are students who prefers reading over the other format. So you can use the mastery path. So what you can do lang po is to assign the points. So ko nari po zero points for the text pero. Hindi yung account na sa grades.
And then, they will receive, audio version of that lesson, video version of that, lesson, or the text version of that lesson. Or if you are, masipag, like ma'am, earlier, you can they some students can receive gamified version of that lesson. Alright? So you can do that with mastery path. And last is you can use master paths based on their interest. So, in math, it is extra difficult to understand math when the scenarios in the problem solving activity doesn't make sense.
Like for example, what will happen if, Juan eats ten thousand apples? So, makes sense. So, we can use mastery paths to align the problems to the interest of the students. Like, for example, here, I created the three versions of, the fraction lesson. So for the first option, it's about anime. And then the second version is about planting.
And then for the third version, it's about baking. So you can do that with Mastery Path so that the students will feel more connected with the content that you are teaching. Now this seems very difficult and very, time consuming. But with the existence of AI, so very much convert one lesson to another format. So you can convert a math problem into a baking type of problem, into a planting type of problem.
So that is very, very quick na po. So this is one of the best ways we could collaborate with AI without sacrificing the quality of content and AI over reliance. So one of the worries of, educators is to have AI over reliance because it can cause laziness, their motivation, and some, it can also lessen their critical thinking skills. But if we will use AI this way to provide personalized learning to help students connect more with you and with the learning material, then we can hit two stones at once. And at the same time, it offers a solution to the pervasive learning poverty among Filipino learners.
So we have we all have heard, I'm sure, about the problem in learning poverty, about that, problem with math, English, and science. So in MasteryPaths where you can provide, personalized learning with students, we can, somehow help our students improve on these areas. So how can we further enhance this? So we can use the learning outcomes in Canvas so that we could have a more informed, decision in addressing the mastery of the students. So you will know, okay. So this type of mastery path, indeed increase their mastery or this kind of assessment increase the mastery of the students.
And last but not the least, you can also award progress with Canvas credentials. So that after all the hard work of the students in mastering a learning outcome, they will feel rewarded because of Canvas credentials. Session. Thank you so much, everyone.
Yeah. So for today, I will be discussing, promoting network learning and computational thinking with Canvas Mastery Paths. So I decided to go into the pedagogical side of education because, in conferences like this, we always talk about the technological side, the technical side. So that's why I would like to, just go to the other side of education. K? So AI has been the top of the town.
Obviously, ever since, it was launched, ChatChippity was launched, so everyone is talking about it. Even in this conference, we are hearing about this a lot. So can you please raise your hand if you have used AI yourself, Whether in education context or maybe personal, use cases. So or have you heard of AI in trainings that you attended? So yeah. So a lot of us have heard about this.
So educators are on debate whether to use it or not. So universities are putting their best effort in crafting policies, in crafting guidelines and principles in using AI. But beyond that, we must focus on our pedagogy as well. Because it will not be enough to just have guidelines, but it would it will always be best if we will equip ourselves as educators with these pedagogical skills. Okay.
So I would like to touch first on the neural networks. So neural networks are the key component in making AI intelligent. So who among you have heard of this term, neural networks? So if you are from computer science, you are you definitely know about this. So neural, networks is the computational model that enables AI to recognize patterns, process information, learn from data, and handle nonlinear relationships. So, when, developers, develop AI, they feed information to teach it patterns so that it could become intelligent or what we call now AI.
But we all know that, neural networks of AI is based from the neural networks of the human brain. So definitely, our brain is more capable than AI. But the problem is we don't know how to unlock it. We don't know how to, to, to to use it to teach our students. Now, because of that, it might be beneficial if we approach education nowadays in the lens of network learning.
Have you heard of this, educational theory network learning? Sim meron po ba, kadiring natinong network learning. So I'm seeing a few hands. So network learning came from, the theory of connectivism, wherein, it encourages students to connect with all the learning nodes that it that he or she has around him. So to define it for you, according to Goodyear et al, he, he described network learning in which ICT is used to promote connections between one learner to the other learner, between learners and tutors, or maybe, you can change that into teachers, between a learning community and and its learning resources. So this is the definition that we have for network learning, and it is it is not something or it is something that is not really, widely used, especially in the Philippine context.
K. So to to make it more, familiar to you, who among you is a member in the FB group of homebodies? Homebodies. Homebodies or maybe kas kasan bodies, gong alam niyapu yun. Okay? Yay. Digital banks groups or something, similar groups like that.
So, we join groups like that so that we can learn about the tips, the experience of others, or, the hacks that others use in homes. And it makes us more interested in that subject area because we we find a connection with that material. So nobody, nobody is required to, nobody is requiring us to learn those things, but we try to learn this thing because we are we feel some connection to that. So that is an example of network learning because there isn't a connection learner who's trying to learn about credit cards or something like that. So that is an example of network learning.
Now, in the context of classroom, so we can do this by, making our classes something that, students can connect with, whether personally or maybe, professionally if you are in a higher education department. Now the question is, how can we do this? So it's it seems like it's a complicated process. So what we need to have as teachers computational thinking, because it originated in the computer science. So in computational thinking, you have there a decomposition process, pattern recognition, abstraction, and algorithm. So in the decomposition process, you break down a big problem into small manageable parts.
And then in the pattern recognition, you observe the similarities of these patterns. And then for the abstraction, you identify the important parts of the problem and of what they do is, they identify the difficult parts of a system and they try to break it down into, manageable manageable parts and then they create step step by step processes to, to help the user of the system achieve a goal. K? Upload or send email. So step by step process. Now in the education context, you might, you might be using this, unconsciously already.
So when you are teaching students, like for example, addition. Addition techniques and patterns so that the students might learn the the equation better. So that is an example of computational thinking. But right now, medyo nagiba na siya because of the existence of AI. Right? Especially, chat based AI where the students can now chat, the the the tech, the chat GPT, and they can learn a lot of takes.
Now what na isan po ba na isop? No? Bakit bamas busunile yung ChatGPT more than just listening to their teacher. Now we will go back to the definition of network learning. It is because they feel connected with ChatGPT. Because with ChatGPT, they could, contextualize a conversation based on how they want to learn or their personal interest so that they could understand a specific, concept better. K? Because they feel that connection.
Now with Canvas MasterPaths, it could help us, provide this connection for our students. So for those who haven't used MasterPaths, so MasterPaths allows us educators to create personalized learning for our students depending on their needs and interest. With this tool, you can set conditional assignments automatically. So you can identify the different interests of your students, their different difficulty, their different preferred learning modes, and you can provide different paths so that they could feel more connected with your material or with with the content and skills that you are trying to teach your students. So let's try to, use the computational thinking to, in using mastery path.
So first, in the decomposition, we design a course. So we decide, the assessments, the projects, the learning content that we want to give our students. And then and the pattern recognition, so most of the time we conduct, like, a pre assessment in our classes. Right? So this might be contrary to the other instructional design models kasi parang paliktad no? So not so monod yung, leads in interest before the design. We are talking about, design na natin.
Course natin. But for some reasons, like, we don't expect, we didn't expect that there's two there are students who are not feeling connected with the the course that we have designed. So upon the pre assessment, it turns out that your expected, differences of your student is differences of your student is different with what you have imagined or what we what you have researched especially that Gen Z and Gen Alpha student is very diverse. It's really hard to, confine them in one, one subject area. And then after we recognize their, needs and interest, we set up the conditional mastery paths depend depending on those needs and interest.
And then after that, the mastery path automatically directs students on what to study and activity to do based on their individuality. K. So let's try to do this in a math subject. Kasi ite fin na kah madale kasi objective siya. Right? So maybe sa movie, Hindi madale sa niyo pero, pag LM naman po yung ginabit mo example.
Ayan. So we can create a mastery path based on mastery level. So you can create a quiz, and then based on the score they got, they will be receiving different, different, content or different version of the content depending on their mastery level. So in this way, those who are already, advanced, they will not be bored because they will get a different version of your lesson. And then for those who needs more attention, they will get, like, a lighter version of the explanation of your content.
So that no one will be left behind. So done. Another use case of Mastery Path is we can create a Mastery Path for their preferred mode of learning. So for example, there are students who likes to watch a video. There are students who prefers listening to audio only.
And then there are students who prefers reading over the other format. So you can use the mastery path. So what you can do lang po is to assign the points. So ko nari po zero points for the text pero. Hindi yung account na sa grades.
And then, they will receive, audio version of that lesson, video version of that, lesson, or the text version of that lesson. Or if you are, masipag, like ma'am, earlier, you can they some students can receive gamified version of that lesson. Alright? So you can do that with mastery path. And last is you can use master paths based on their interest. So, in math, it is extra difficult to understand math when the scenarios in the problem solving activity doesn't make sense.
Like for example, what will happen if, Juan eats ten thousand apples? So, makes sense. So, we can use mastery paths to align the problems to the interest of the students. Like, for example, here, I created the three versions of, the fraction lesson. So for the first option, it's about anime. And then the second version is about planting.
And then for the third version, it's about baking. So you can do that with Mastery Path so that the students will feel more connected with the content that you are teaching. Now this seems very difficult and very, time consuming. But with the existence of AI, so very much convert one lesson to another format. So you can convert a math problem into a baking type of problem, into a planting type of problem.
So that is very, very quick na po. So this is one of the best ways we could collaborate with AI without sacrificing the quality of content and AI over reliance. So one of the worries of, educators is to have AI over reliance because it can cause laziness, their motivation, and some, it can also lessen their critical thinking skills. But if we will use AI this way to provide personalized learning to help students connect more with you and with the learning material, then we can hit two stones at once. And at the same time, it offers a solution to the pervasive learning poverty among Filipino learners.
So we have we all have heard, I'm sure, about the problem in learning poverty, about that, problem with math, English, and science. So in MasteryPaths where you can provide, personalized learning with students, we can, somehow help our students improve on these areas. So how can we further enhance this? So we can use the learning outcomes in Canvas so that we could have a more informed, decision in addressing the mastery of the students. So you will know, okay. So this type of mastery path, indeed increase their mastery or this kind of assessment increase the mastery of the students.
And last but not the least, you can also award progress with Canvas credentials. So that after all the hard work of the students in mastering a learning outcome, they will feel rewarded because of Canvas credentials. Session. Thank you so much, everyone.