Campus Technology Insider Podcast July 2021
Listen: The Science of Studying Student Learning at Scale
00:13
Rhea Kelly: Hello and welcome to the Campus Technology Insider podcast! I'm Rhea Kelly, executive editor for Campus Technology, and your host.
Imagine you're a professor teaching a course, and you want to test what teaching practices work best for your students. For example, is it better to give immediate feedback on assignments, or delay the feedback for a few days and give students an extra opportunity to process the concepts in their work? Even after testing each method and determining a result, it's impossible to know if your findings are unique to your own particular course or if they could apply to all students in general. To solve that problem, a team from Indiana University set out to expand the scope of pedagogical research by creating ManyClasses, a model for studying how students learn not just in a single classroom, but in a variety of different classes across multiple universities. For this episode of the podcast, I spoke with researchers Emily Fyfe and Ben Motz about how ManyClasses works, the challenges of using a learning management system to conduct research, what they learned from the first ManyClasses experiment, and more. Here's our chat.
01:30
Hi, Emily and Ben, welcome to the podcast. So I think I'd like to start by having you each introduce yourself and just talk a little bit about the work you do. Emily, would you want to start?
01:42
Emily Fyfe: Sure. So my name is Emily Fyfe. I'm currently an assistant professor at Indiana University in the Psychological and Brain Sciences department. I conduct research on the science of learning. I have an emphasis on STEM learning, so how children think about problems in mathematics and how the errors they make sort of give insights into their cognition. But I also think critically about this across development. So not just with children, but with adolescents and adults. And the goal is to think about how people learn and how we can use that information from science to inform educational practice.
Kelly: Great. And Ben?
02:22
Ben Motz: Yeah, I'm kind of similar to Emily. So my name is Ben Motz, and I'm a research scientist in Emily's department, in the Department of Psychological and Brain Sciences. And I also direct the eLearning Research and Practice Lab, which is a research unit that's kind of within our IT division, where I build bridges between faculty research and the student data infrastructure and student data warehouses that a big university like Indiana University maintains.
02:50
Kelly And I know you've recently published a paper about a new model for studying how particular teaching practices can improve student learning, called ManyClasses. And there's so much to dive into, but maybe first, you could give me kind of a brief overview of what ManyClasses is.
03:08
Motz: So as you might guess from the name ManyClasses, it's a study that takes place across many different classes. And that's relevant because when researchers go out to study how people learn, or even teachers go out to seek how different instructional tactics or strategies affect student performance in their class, they're usually doing it in very isolated situations. So usually just in one class. That creates problems: For example, it's unclear whether the results are due to the intervention, whether they're due to whatever the experimental manipulation might be, or if it's just because of the random idiosyncrasies of that class. So ManyClasses is really an attempt to try and expand the scope of research so that what we're doing in asking a question of how people learn, is expanding beyond the boundaries of any single classroom, really aiming at developing inferences that could generalize beyond that narrow scope, but also that might be able to identify where a practice might have benefits. If it's not so hetero, I'm sorry, if it's not so homogenous across the student population, maybe it's the case that some things work, not everywhere, but only in specific settings. So ManyClasses is also pretty well equipped to be able to answer those types of questions as well.