Adaptive learning and Open education: A Match made in heaven?

What is adaptive learning? When reading about the subject, many definitions appear that also determine most of the pros and cons. You can see these in these three examples of definitions from different points of view: 

• Adaptive learning is an educational technology that uses algorithms to adjust the presentation of material to an individual student's needs, based on their performance and other data.
• Adaptive learning is a method of instruction that personalizes the learning experience for each student, by adjusting the content, pace, and difficulty level of the material to meet their individual needs.
• Adaptive learning is a process by which learners actively participate in the design and delivery of their own learning experience, by providing feedback to the system and adjusting their own learning goals and strategies.

We believe understanding and looking at adaptive learning from these varied viewpoints is key for its success and will strike the right balance between what the technology does, together with the lecturer and learner. Our vision of adaptive learning is focused on increasing the accessibility of learning, while also make it easier for teachers and students to make conscious decisions about their learning journeys based on feedback and insights. Adaptive learning should not just be a tool or AI telling you what to do. Rather, in complex learning, like with maths and statistics, adaptive learning should be a helpful tool that gives you insights based on a combination of massive data.

At Grasple, we've based our adaptive learning on the following design principles:

1. It should enhance open education and increase accessibility 
2. It is a tool to help learners on their personal learning path
3. It should provide a high level of autonomy. Teachers and students are in charge of the content and choices, which helps them stay motivated and grasp the subject matter 

We believe the combination of open educational resources (i.e. open mathematics/statistics exercises) and adaptive learning has a high potential for creating a learning impact for students. We strive to make learning mathematics and statistics more accessible for everyone around the world and improving equity in STEM education. To do so, we already ensure that content is openly licensed so that everyone can always access, use, copy, and/or improve it, and that no single party is in control of the (copyrighted) content. In future, we also want to make sure you can interact with the content via an adaptive learning method as an individual without having to pay for that feature (or for the access of the content).

To enhance the potential of adaptive learning and open education, we will focus on the next level of cooperation within the community and enhancing content quality. These next steps will involve creating a method for teachers to be able to improve, update, maintain and extend knowledge component graphs (KCG) within the community so that openly licensed exercises can easily be used in adaptive learning.

This approach will tackle some of the challenges most commonly associated with the development of adaptive learning like:

• The requirement for a high volume of quality content
• The low level of autonomy for teachers and students
• The absence of transparency and insights into the algorithm (i.e. black box)
• The increase in content learning material costs for students 

How does Grasple apply adaptive learning in practice and tackle some of the difficulties and concerns?

When focusing on technology, major concerns are often: Who is in control? How does the algorithm make decisions? Should the student fail or pass? At Grasple we believe that the technology's power lies in collecting data and combining it with expert knowledge from the teacher and student to provide insights and advice while still letting the students and teachers make the final decisions.

In line with our approach to adaptive learning, Grasple has developed three key features for our platform:

• Knowledge component graphs (KCGs)
• Diagnostic testing 
• Easy content creation, sharing and editing moving towards interoperability

The knowledge component graph (KCG) is a type of graphical representation used to model the relationships between different knowledge components in a given domain. Knowledge components are the basic building blocks of knowledge that a learner needs to acquire in order to become proficient in a particular subject area.

Grasple developed the graphs with teachers and learning experts. We can create KCGs for a course and manually use them in tests. We are working towards enabling teachers to do this themselves based on a base KCG. The learnings and insights from these can be openly shared, just like how Grasple facilitates the sharing of open learning resources. The KCG is not just meant to force a learner into a path. Rather, it provides insights and advice to teachers and students regarding what learning steps can be taken next. Teachers have a say in the design of the KCGs for their subjects and create the model with their expertise, learning audience, and learning goals for their courses in mind. The system adds data and enhances the expertise of the lecturers. 

The subjects and learning goals connect in the graph via two relations: Hierarchy or prior knowledge. The hierarchical relationships show how a broader topic is split into its subtopics and indicate how users' knowledge about the subtopics will feed into their knowledge about the overarching topic. Prior knowledge relations indicate a certain level of knowledge needed before someone can continue with the next subject.

The relationships between the knowledge components can estimate the mastery of a subject while simultaneously revealing gaps in knowledge.

A diagnostic test is developed for courses based on the subjects covered in the course. Again, these can be shared and leveraged within the community of users (and even exported) as Grasple is an open platform. The diagnostic test provides insights to the student and teacher regarding where we think the student could benefit from more practice. The information is updated based on the exercises a student completes and the percentage of those exercises they answer correctly. The exercises are curated by the lecturers responsible for the course, providing the teacher with autonomy over their course. 

The student in the course is not forced to learn within a fixed path set by an algorithm. The system collects information and creates insights. The student can still choose where to spend their time, giving the student autonomy and ownership of their learning journey. 

In the end, the benefit of Grasple's adaptive learning for educators is seeing your students learn and grow based on insights provided by the platform, retaining the power to make conscious decisions about how to structure offline learning, and balancing online and offline activities in a way that engages students and generates learning value.

N.B. Detailed insights only available in the institution account not for individual accounts due to high privacy and security demands

Furthermore, working with existing community content saves time on content creation. One challenge for adaptive learning is having a lot of data and but not enough detailed content available. With the creation of an open resources platform, the Grasple community creates a lot of content with highly detailed feedback embedded in the exercise. The statement “many hands make light work” counts in Open Education. Teachers' workloads are heavy and the effort they put into creating content is not always seen in their appraisals. Being able to make use of a community and cooperate within a user-friendly editor enhanced the adoption of open resources within institutions and by individual teachers. Individual teachers can use Grasple for free when creating content for the community and when using materials on the platform in their courses. 

Combining these benefits with an easy to use platform for teachers will positively impact countless learners around the world, since they will have free access to openly licensed interactive math/statistics exercises. These quality materials are maintained and improved by a large, global group of teachers. These materials will be optimized for interactive and adaptive learning, such that it facilitates learning best suited for a student's pace and level of mastery. 

At Grasple we continue to work on the following; 

• Use expert driven KC graphs together with AI research on student answer data to continuously determine which open exercises are best to use in the adaptive learning method 
• Allow teachers to easily create/adapt KC Graphs for their courses/materials
• Make the KC Graphs part of the open content in terms of collaboration and open licensing
• Have community KC graphs (like we have community materials now) 
• Have an adaptive way of interacting with the open exercises using those KC graphs and the open exercises for individual users (instead of the linear way of interacting with them now)
• Create a community for sharing open pedagogic strategies, a.o. balancing on- and offline learning. 

Please send us your insights and/or comments on adaptive learning. We'd love to hear more ideas and concepts to sharpen our and others' visions of making knowledge available for all.