ETEC 580 - Reflections

September 30th, 2025

This directed study is officially underway! To begin, my focus has been on orientating myself within the landscape of adaptive learning research. Through my professional experience and the ETEC 543: Understanding Learning Analytics course, I’m starting with a rough understanding of adaptive learning and what it entails. Though adaptive learning feels like a reasonably focused topic, compiling the draft reading-list for this course proposal made me aware of the wildly different directions I could take this study.

The technical foundations of adaptive learning are complex, seeped in algorithms and statistical models, such as Bayesian Knowledge Tracing. There is substantial research comparing the effectiveness of adaptive learning systems, student’s perceptions of it, and frameworks for its integration. And of course, no field is safe from the effects of AI, especially not one as seemingly suitable for its strengths as adaptive learning. What impact AI has had on the field, I have yet to dive into. Any of these avenues could be a course unto themselves.

My takeaway from the research I’ve done thus far has me leaning away from getting lost in the technical foundations of adaptive learning. Certainly my literature review and subsequent explorations will discuss be informed by the algorithms that power these tools, but to dive to deeply into the math and code would quickly exceed the scope of this study.

I have also been reflecting a lot on the questions I want to pose in this study:

• “What is adaptive learning”
• “How does it work?”
• “Is it effective?”
• “How is it practically implemented?”
• “Why isn’t it more widespread?”
• “How is AI affecting adaptive learning?”
• “Should I be capitalizing ‘Adaptive Learning’” (Just kidding! I had to ask Claude though…)

As I progress, I hope to narrow these questions down.

Duncan