Productive Persistence: Part One of Our Interview with the Carnegie Foundation's Rachel Beattie

Hello readers! We hope you had a great weekend! This week, we are running our two-part interview with the Carnegie Foundation's Director of Productive Persistence: Rachel Beattie. Dr. Nolting has shared a great interest in productive persistence in recent years, so this conversation was a real treat for us. Among many topics discussed, we spoke about the main variables that affect math learning, the application of science and research to the classroom, and the differences between positive persistence and negative ​persistence. Enjoy! Check back on Wednesday for the conclusion of our conversation, 

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ASP Blog: Do you want to tell us a little about your background?
 
Beattie: Sure! My background is in psychology. When studying for my Ph.D., I specifically focused on the etiology of learning differences in mathematics [with regards to] reading and language—with a heavy focus on dyslexia and specific language impairments. I wanted to understand why these learning differences came to be and what the actual differences were between typical and atypical reading and language processing in mathematics. While I was doing that, I was also an adjunct professor at Occidental College and USC. After I finished my Ph.D., I taught research methods, statistics, learning and memory courses, and I helped TA a course on the science of happiness. I then went to Ohio State University where I did my postdoctoral fellowship in neuroscience, which involved conducting neuroimaging on children and adults related to their mathematics reading and language processes. This gave us great insight into the development of these differences.
 
After my postdoc, what I really wanted to do was get away from research about the basic processes, and get more into applied research—actually helping students and teachers in their classrooms during the school year. So, I started working at the Carnegie Foundation. I’ve been here for a year and a half now, and it has been really wonderful to actually make a practical difference on a daily basis.
 
ASP Blog: Can you talk a little bit about the variables that predict math success?
 
Beattie: Sure. I’ll speak mostly on developmental mathematics because that is the data I am working with right now. Obviously, one of the biggest variables is the foundational mathematics skills that students come to a course with—we actually measure these when students come in with a conceptual knowledge quiz. This is a pretty strong predictor, though it is not necessarily deterministic. There are other factors that we see that are really important as well. We see that students’ mindsets about their ability to perform and learn in a classroom are also predictive. We also often see that a student’s certainty of his or her belonging in the classroom is important. If he or she has high learning uncertainties, then we see that they are much more likely to withdraw—and if these same students finish a course, they usually have a lower score at the end.
 
Anxiety is another interesting variable in the data. Research is showing us more and more that very low and very high anxiety are maladaptive. Very low anxiety is actually negatively predictive of success. It is very linear too, as we see that high anxiety is actually a predictor of success—we think this is because these students are putting in more effort outside of the classroom. That is our current working theory, though we are still puzzled a bit about that.
 
Also, in the pathways, we do a lot of intervention around students mindsets and beliefs. Intriguingly, we see that their week one beliefs are no longer predictive of course outcomes. When we started we saw that, yes, they were very predictive to these mindsets, uncertainties, stereotypes, and anxiety. However, now we see that week four is much more predictive of success than week one. This suggests that we have shifted students’ mindsets. It is really nice to see. We always want to create a learning environment, a culture, where your belief sets at the beginning are irrelevant. It is how you perform throughout the class that is what is ultimately important to your success. 

ASP Blog: Interesting! How much of this is the result of students better understanding what is expected in a class and having a better grasp on whether or not they have what it takes to find success in the course?
 
Beattie: I’m sure that is part of it. We see that students are more comfortable asking questions by [week four]. A lot of it, though, is the actual interventions we conduct to help students see themselves as capable mathematics learners. It is important to shift student thinking and self-perceptions in that way.
 
ASP Blog: Which is a perfect segue into your personal expertise: productive persistence—the ability for a student to persevere through difficult times or even learn how to handle success. Do you want to talk a little bit about this behavior? 
 
Beattie: Of course! My favorite subject! People always ask me what “productive persistence” means. We think of it as how students continue to put forth effort during challenges. When they do so we hope they are using affective strategies. We focus on productive persistence because in our data we are seeing a lot of students being unproductively persistent. We don’t want students coming back to the same courses over and over again. They know the course is important and that they need it in order to pursue their dreams. But they aren’t succeeding; they aren’t being consistently productive.
 
We want to help create environments that allow students to be productively persistent in mathematics. When we develop frameworks, we introduce students to faculty, we consult with psychological literature, as well as with general education reform literature, and we form a network of faculty members and researchers to create frameworks based on all of these sources of information. [During this process] we came up with nearly `100 possible constructs that might contribute to productive persistence, and we narrowed these down to five high-leverage factors. If we make real movement on these five, we will help students become more productively persistent.
 
While there are many things that affect productive persistence, we focused on the following five because they are within our locus of control:
 
1. That students believe they are capable of learning. They believe it is possible to succeed in a course. 
 
2. It is really important for students to develop social ties to peers, faculty, and the course. If anything happens that makes students wonder if they belong in a classroom setting, they often take this on as their identity within the classroom.
 
3. That students see a course has value. When we talk about value, we mean that students are able to relate material to their short-term and long-term interests. 
 
4. We want students to know how to succeed in a college setting. This involves traditional study skills, cognitive study skills, but also emotional recognition skills and general college-know-how skills: how to navigate a syllabus, knowing how to navigate course progression, etc. 
 
5. We want to help support faculty to help students to develop these mindsets and skills. We believe that we are in the business of shifting student perceptions, but also that it is important to change the learning environment. There needs to be a congruency between the mindsets we are helping students develop and the environment in which they are learning.
 
We also want to create an effective pathway through college math. We actually created a curriculum that engages students in math that matters—so it involves real life problems, so that students can see the relevance of mathematics in their lives. To support all of that, we have a lot of support for faculty built into the classrooms, we have faculty mentorship, we have online resources, and a lot of this focuses on productive persistence. The pedagogy again supports productive persistence because it promotes collaborative learning. We focus on activities that involve students working with one another to better understand the concepts of mathematics. We don’t just want them to have the procedural skills; we want them to have the conceptual knowledge too.
 
I think productive persistence works really well as a part of this whole structure. We want the whole environment, the pathways of developmental mathematics, to support students to develop the right mindsets and skills. 

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That wraps up Part One! Part Two continued, here.