Student-Driven Datasets

My Story

I have been teaching SDS 320E Elements of Statistics for over 10 years, and I’ve made a lot of updates and changes to it as course coordinator. Although I am passionate about the content, I understand that most students will not be excited about taking a statistics class. Because I teach a large introductory service course, nearly all of my students are there because they have to be and not because they want to. So I tend to struggle with convincing them to care about what we are learning and to appreciate that learning how to analyze data will help them in their other coursework, future careers, and their lives outside of school. 

Even though I’ve dedicated a lot of time to picking interesting and relevant real-world example datasets to explore in this class, I wanted to add more context about the data the students analyze so that they can focus their efforts on answering specific research questions of interest to them. My hope is that highlighting topic relevance will help motivate students to take a greater interest in what they are learning and to retain their data literacy and applied statistics skills after they leave my class.

What does this practice look like?

I first added a question to my “welcome” survey that students fill out the first week of class to better understand which datasets would be most relevant to them:

After reviewing their responses, I found the topics that overlapped with examples I could include during class and in assignments. I didn’t have to update much of my course materials, as many of their responses aligned with topics I already have. But having this survey question allowed me to tell them about the connection of their interests to what they are learning.

I typically did this by starting lectures with a research question from a topic of interest. Then, we learn the method (e.g. t-test, correlation, calculating center/spread) to answer that question, and apply it using a real dataset. This is also how their weekly lab assignments are structured, and I added this focus to all of the problems they see in class as well.

For example, when starting a lecture on correlation, I would tell the class:

How did it impact students?

Based on the results of a student survey a few weeks into the term, it appears that several remembered and appreciated the applicability of what they learned to real-world problems. For example, students wrote:

Implementation Tips and Resources

I think it is really important to ask students at the start of the semester what their interests are and then to explicitly refer back to their responses during activities so that they know that you were listening. I also have found that pictures can really help keep students engaged during class, so finding a visual that is related to the problem they are working on is a good idea.