Substantive Post #2 – Models of Active Learning

This module helped me understand that active learning is not just about adding group work or projects; it is about designing learning experiences that push students to actively engage with ideas, apply them, and reflect on their understanding. This became especially clear to me when I thought about a data sampling course I took, where I genuinely felt like I learned something useful.

In that course, learning was built around real tasks rather than passive exposure to content. I learned how to design surveys, collect data, clean and format datasets, analyze real data, and visualize information. For our final group project, we looked at tourism trends before and after COVID using Statistics Canada data. Because I had to actually work through each step myself, the concepts did not feel abstract. Instead, they felt practical and connected, which made the learning stick. Looking back, this course closely aligns with models of active learning because it required me to apply concepts continuously rather than just understand them theoretically.

This experience contrasts sharply with another statistics course I took that relied almost entirely on long, monotone lectures and reading slides. There were no pauses to process information, no practice questions during class, and no opportunities to apply ideas as they were introduced. Even though I was physically present, I often found myself mentally disengaged. Learning was treated as something students were responsible for doing on their own later, rather than something supported during class time. 

What pulls me out of this sub-conscious state is not always noticing that I have zoned out, but being required to respond in some way. Writing down something important, being asked a question, or even reacting to the energy of the room can bring me back into the learning experience. Active learning models intentionally interrupt this disengagement by requiring participation, which helps explain why even small interactive moments can significantly improve focus and understanding.

Merrill’s First Principles of Instruction helped me better understand why the data sampling course was so effective. The learning was problem centred and grounded in an authentic task. Prior knowledge was activated through reminders of earlier techniques, new ideas were demonstrated through examples, and then immediately applied through hands-on work. Integration also played a role, especially in the final project, where we had to interpret and communicate our findings. Seeing this structure made me realize that successful learning experiences are usually the result of intentional design, not coincidence.

If I were designing a lesson using Merrill’s principles, I would focus on the issue of misleading data in research and media. As a statistics student, this is something I personally care about. I would include a short video showing how data can be misrepresented through graphs, followed by an interactive activity where learners manipulate visualizations themselves. This approach would allow learners to actively test their assumptions and receive immediate feedback, rather than passively observing examples.

Compared to my K–12 experience, particularly in India, this course feels much more engaging. Much of my earlier schooling focused on memorization and exam performance rather than understanding, and active learning was rare, likely because it required more time, resources, and effort from teachers. In contrast, this course encourages reflection and connection between theory and personal experience, which makes learning feel more meaningful. Overall, this module reinforced that learning happens when students are cognitively engaged, not just exposed to information.

References

Tse, K. (2023). What is Knowledge? Active vs. Passive. Medium. https://keithtse.medium.com/what-is-knowledge-active-vs-passive-cef2c50c34c6