Reflection #1

I joined the lab really late, so unfortunately I didn't have a chance to talk through the readings with the class. At the same time, I did have some extra time to sit with the readings on my own, which gave me more space to think about how they connect to my own learning experience.

One key idea from the readings that stayed with me is that becoming an expert is not simply about knowing more. It is also about learning to recognize what information matters and how different pieces of knowledge connect. The authors describe how novices tend to approach problems through individual facts, formulas, or rules, while experts gradually develop more organized knowledge that helps them recognize meaningful patterns and connections. This made me think about my own experience learning statistics. Before graduate school, I often thought of statistics as a collection of methods: learn the formula, understand what each number means, and apply the method to the right problem. When I started taking more advanced statistics, however, I realized that knowing how to calculate something is very different from knowing why I should use it, what assumptions I am making, and how to interpret the result in context. For example, when I first started working with linear regression, I was very focused on getting the coefficient and standard error correct. But I gradually became more interested in the larger argument behind the model: What relationship am I actually trying to understand? Which variables are relevant? What does the coefficient tell me, and what does it not tell me? These questions require more than procedural knowledge. This is something I am spending a lot of time working on right now, since I am also taking a statistics course with Andrew, who constantly asks us to explain concepts in words that even our uncle could understand.

I also see a connection to how I think about learning more generally. With AI tools becoming increasingly accessible, it is becoming easier to obtain an answer without necessarily developing the underlying understanding. This makes me wonder whether an important part of learning in the age of AI is becoming better at identifying what questions to ask, what information is meaningful, and when an answer should be questioned. In some ways, AI makes the distinction between knowing information and knowing how to use information even more important. If I can ask AI to calculate a regression coefficient for me in a few seconds, then the more valuable skill may be understanding what I am actually trying to learn from that regression and whether the result makes sense.

The second reading by Peggy A. Ertmer and Timothy J. Newby pushed me to think about this not only from the perspective of the learner, but also from the perspective of a designer. They argue that instructional design should begin with understanding the learning problem and the context, rather than simply applying a particular teaching strategy. They emphasize that different theories of learning lead to different approaches to instruction, and that designers need to think about when and why a particular strategy is appropriate. This made me realize that becoming a better learner and becoming a better designer may require a similar kind of thinking: we both need to understand the situation before deciding what to do. Just as I am learning to ask why a statistical method is appropriate instead of simply applying it, an instructional designer needs to ask why a particular learning activity or strategy is appropriate for a particular learner.

Putting the two readings together, I am starting to think about expertise less as an endpoint and more as a way of thinking. It is about being able to recognize patterns, understand context, make decisions about what matters, and reflect on whether your approach is working.