Thoughts about Adaptive Learning

Adaptive Learning leverages many other fields, including Machine Learning and Predictive Analytics, to create customized learning experiences for individual learners. These experiences tend to be semi-automated, meaning that while each learner’s path through the course is determined by an algorithm, an instructor remains present. Freed from the role of blanket lecturer, and equipped with the detailed analytics about each learner that adaptive platforms tend to offer, instructors can focus on one-on-one mentorship and tutelage. Adaptive Learning platforms are designed around four theories: Metacognition (thinking about what we know enhances retention), Deliberate Practice (focusing our practice on the weakest areas facilitates development), Fun in Gaming (assessments should be challenging but not too challenging), and the Ebbinghaus Forgetting Curve (knowledge can be more effectively transitioned from short- to long-term memory if it is recharged at specific points in time, especially right before the learner forgets).

Adaptive Learning platforms have a ton of potential to benefit learners in all stages of education. As we’ve seen in this unit, paths of mastery can be helpful for allowing math students to pace themselves and get all the remediation they need while remaining engaged. This unit didn’t go into humanities very much, but there’s much potential there as well! Despite fields like literary criticism lacking “right” or “wrong” answers, learners’ interests and they topics they wish to tackle can wildly diverge. Adaptive Learning could help humanities instructors cater the genres and texts they assign to individual learners’ preferences, enabling students to break out of the traditional canon (the concept of which is becoming increasingly challenged) or eschew the traditional essay response.

For my signature assignment, I’ll be outlining a training on How to Remediate a PDF in Adobe Acrobat DC that would be housed in a Canvas course. To incorporate Adaptive Learning, I would use the “Mastery Paths” feature within a series of Canvas modules. Mastery Paths allow instructors to customize learners’ paths through modules based on learners’ score ranges in “source” assignments given to everyone. Mastery Paths also provides detailed analytics the instructor can use to customize their mentorship to each learner. I would create three or four broad paths in the form of series of modules locked behind requirements. I would then create several smaller paths (through assignments with more granular learning objectives) within each module, determined by Mastery paths. This would allow learners who were more challenged by various parts of the remediation process to get bite-sized pieces of extra help as needed, then move on in an appropriate path to a new topic. In conjunction with this, I would use Canvas’ Outcomes tool to set clear learning goals for the training and measure each learner’s individual progress toward these goals.

On top of this, if anyone knows of a free Adaptive Learning tool that integrates with Canvas, I’d love to hear about it!

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