From Artifacts to Analytics: Teaching with Real-World Data

Students enjoy working with real research because it gives them a sense of authenticity—they like knowing the data they’re analyzing is out there in published studies. I often pull examples from my own fieldwork, but I’ve also found great datasets in dissertations or appendices from academic publications. Even if you’re not involved in original research, there are plenty of public datasets available that can be adapted for coursework. And students love critiquing published work! One of my favorite exercises is having them evaluate charts from academic articles using a list of best practices—when they realize even experienced researchers make unclear graphics, it builds their confidence in analyzing data critically. 

Real-world data rarely fits into a neat bell curve, and that can be intimidating for students who are used to textbook examples. To help them get comfortable, I scaffold assignments by providing structured spreadsheets with key formulas already embedded. That way, they can see how calculations work before applying them on their own. When students understand that real data is often messy and that part of research is making sense of complexity, they become more resilient problem-solvers.

I’ve learned that it’s important to test out activities before introducing them in class. If you’re developing a new data-driven exercise, try running through it yourself or asking a colleague to test it. I also involve my students in the refinement process—when I introduce a new activity, I tell them upfront, “This is the first time I’m doing this, so let’s see how it goes.” Framing it as a collaborative experiment keeps students engaged, and their feedback helps me improve the exercise for the next time.