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Have you ever thought about having your courses adapt to students based on their current skills and knowledge? Do you want to automatically serve customized content to learners that are beginners, proficient, or at any other level in between?
Some people implement this by having separate courses for each level. They let users choose the level they want to take. What if you could automate this process to enroll your student at an appropriate level? How about creating a single course that adapts to a student’s level automatically?
In this post, we’ll discuss an educational method called Adaptive Learning. We’ll also explore ways to implement it using Uncanny Automator with your favourite WordPress based LMS. It’s a way to tailor a course to different groups of users based on their existing abilities.
What is Adaptive Learning?
Practically, Adaptive Learning starts with a way to estimate a learner’s current understanding. To do that, we could use a form/survey to find out what they are consciously aware of. A better idea is to ask them questions about the content on which we want to check their confidence/competence. This can be done through one or more quizzes to find out what they are unconsciously aware of.
Why create Adaptive Learning?
- It’s like 1-on-1 instruction.
Contrasted with a standard, one-size-fits-all approach to learning, an adaptive method is closer to one on one supervision. Here, a computer program (Automator) acts as a personal facilitator.
- Reduce learners’ time and effort while increasing their productivity. Efficiency FTW.
If courses adapt to a learner’s current knowledge, they don’t spend time on things they already know. They also spend less unproductive time on things that they might find too difficult to grasp. By estimating their skills, you have the opportunity to present alternative material that ensures their success. Especially if they are particularly weak or strong in a topic.
- Indirect personalized feedback and learning.
Testing a learner regularly and adapting their learning accordingly is an indirect way of providing personalized feedback to a learner.
- Individualized learning paths.
Since each learner’s test results could be completely unique, the learning path presented to them could also be absolutely unique down to an individual learner!
- Cater to different types of users in mixed groups.
When working with groups that mix students at various levels of competence, it’s easier to present a single adaptive program than look at creating different courses for different members of a group. In fact, assigning different courses to different members of a group isn’t even possible with WordPress based LMS’s. Adaptive Learning is a way to work around this limitation.
- Provide focused remediation.
When students are faced with learning material presented to them, they might still struggle. On the other hand, they may be extraordinarily successful. It would indicate that the material wasn’t challenging enough for them. Both these cases become less probable if there are different variations of the material. This way students can focus on their weak areas while skimming or skipping the ones they’re strong in.
- Individual intervention.
A specific learner may struggle with something even when the material is supposed to work for them. With adaptive learning, it becomes easier for instructors to identify and isolate such cases and personally intervene. This can also help design materials for a lower level if a lot of students struggle with the lowest level material. Conversely, it can help design higher levels of material if too many students perform very well at the existing highest level.
Adaptive Learning with Automator
Uncanny Automator is a plugin that allows you to automate aspects of WordPress and its numerous plugins. With Automator, you can create Recipes that contain Triggers that run on user activities and Actions that are run automatically when triggers are completed.
Uncanny Automator contains multiple automation triggers and actions for LMS’s like LearnDash, LifterLMS, TutorLMS, LearnPress, WP Courseware, WP LMS, and MasterStudy LMS. With the actions, you can automate enrolment, un-enrolment, completion, and incompletion of learning material. Most of them have triggers so that you can run automations when a user passes or fails a quiz (among others). Some of them even have a percentage based quiz completion trigger that opens up further possibilities.
If we create a quiz on all the concepts covered in a topic, whether a user passes or fails a quiz tells us whether the user has a good grasp on the concepts or doesn’t. If you create quizzes for each topic in a course, you can estimate whether a user needs to complete a step (topic, lesson, section, a whole course). So, if our understanding is that a user already knows everything we’re going to present in a step, we might as well auto-complete it for them using the relevant Automator action!
Let’s explore this in detail using two examples that illustrate what’s possible. This way, you could use the same principle on your e-learning site to solve your own adaptive learning needs. Although we’ve illustrated the following examples with LearnDash, you could do identical/almost similar things with your favourite LMS too.
Example 1: Basic and Pro Levels
Imagine you’ve created two courses on a subject, a basic level, and a pro-level. To implement adaptive learning in its simplest form, we create a new course that we’ll enrol a new student in instead of enrolling in either of the other two existing ones.
In this qualifying course, we add a new quiz that contains questions that test all the concepts covered in the basic course. (Do note that some LMS’s will need you to add a lesson to be able to add a quiz.)
Next, we create two recipes in Automator. The first recipe enrols a student in the basic course if they fail the qualifying quiz.
The second recipe enrols a student in the pro course if they pass the qualifying quiz.
So, this is a new learner’s full learning pathway:
This model could easily be extended to multiple levels:
Example 2: Employee Training Course
Now imagine you are catering to a mixed group of students, maybe because you resell courses to organizations. In this case, it probably makes more sense to create a single course that contains content for all the levels instead of creating separate courses.
In such a case, you could organize the same three courses as above into a single course with three separate sections.
The qualifying course could also become a section that contains the qualifying quizzes.
Now you can create recipes in Automator to auto-complete sections or lessons (depending on the LMS) based on a student’s performance in the quizzes.
Additionally, you could redirect the user directly to the lesson that they need to start with:
You can pretty much figure out the other recipes for this scenario.
Using Surveys/Forms and other Automator goodness
Just like we’ve used quizzes in the examples above, you could use any of the available form integrations (https://automatorplugin.com/all-triggers-and-actions/) to create a questionnaire or a survey and use the users’ responses as a cue to the course they should be ideally enrolled in.
It’s slightly less reliable because users may consciously over-estimate or under-estimate their current level. However, it might be the only feasible solution in some cases. You might find numerous other triggers and actions that might help you add an even more personal touch to a student’s journey.
In a similar vein, a student may struggle even at the lowest levels (they fail a quiz that tests their learning at the end of a module/course). In such cases, you could trigger a private message or an email to the concerned instructor.
In the message, you could maybe include a link to your appointment calendar for a one-on-one conversation, enrol them into a remedial course or register them in a Zoom/GoToWebinar meeting/webinar or use webhooks to run actions on third-party services or even integrate with Zapier or Integromat.
A similar process could be implemented for high performing students who ace all the available material. You could provide them extra resources or schedule a one-on-one intervention to chart out a special path for them.
I hope that this post has given you enough concepts and practical building blocks to make your next program adaptive and personalized. We’ve just about scratched the surface and looked at really simple examples.
Based on these, you should be able to create really complex pathways; pathways that use multiple recipes to create a unique experience for every single student of yours.
If you have any questions or would like to share how you implemented Adaptive Learning, let us know in the comments.