An interview with Dr. Steve Ritter about his new book
Have you heard of “learning engineering?”
Even if you haven’t, you’re probably already using some of its principles in your classroom.
Our Chief Scientist and Founder, Dr. Steve Ritter, is a contributing author of the new book, Learning Engineering Toolkit: Evidence-Based Practices from the Learning Sciences, Instructional Design, and Beyond. It’s one of the first to pull together the many threads of expertise needed to transform educational processes so they’re more effective and equitable for everyone.
While Learning Engineering Toolkit is intended for fellow educational researchers, it contains many takeaways for classroom educators. We interviewed Dr. Ritter about what all teachers should know about learning engineering and how you can apply it to your teaching practice.
How would you describe learning engineering to a classroom educator?
Learning engineering is about understanding what works for which students and when and turning that knowledge into classroom practice. It’s also about developing educational materials and processes that are proven effective and that teachers can use in the classroom.
One example in the book is that learning science has shown us that students build on their prior knowledge when they learn. Learning engineering is trying to find out how to give each student the experiences they need to use that prior knowledge to excel.
Why is learning engineering an emerging trend in education?
One reason is that advances in educational technology are allowing us to gain greater insight into how students learn, how teachers teach, and the overall impact of educational experiences.
Another reason is the rising awareness of educational inequality has led researchers to try to understand contextual factors that lead students in some classrooms to do better than students in other classrooms. In learning engineering, we ask how we can structure education differently to ensure that each student is learning under the conditions they need to succeed at high levels.
One aspect of learning engineering your new book talks about is human-centered engineering design methodology. What is this and what might it look like in a classroom?
What it means to be human-centered is, first of all, to put the students’ needs first. And sadly, this still isn’t the norm.
In education, we often ask questions like, “How do we arrange grades within buildings?” “What materials do we give to teachers?” “How do we train teachers?” But if we start from the student’s perspective, we’d start with different questions, like, “How do we create a change in students’ brains?” Because that’s what learning is.
One thing we’re trying to understand is what different techniques each student needs to learn. Even though everyone in education believes at some level that personalization is important, many people are surprised by how little we know about how student characteristics relate to that personalization. For example, how might personalization connect to students' interests or their identity? That’s something my research team is currently studying.
What does learning engineering offer educators that is new and helpful to their teaching practice?
Teachers using learning engineering principles will enter the classroom with some healthy scientific skepticism and be willing to question everything they do. That’s one reason continuous formative assessments are so important. If we want kids to understand what we’re teaching, we must constantly check and ensure that this is happening. And if it isn’t, teachers must consider how they can modify their approaches. And they’re so good at doing this because teachers are some of the most creative people in the world.
On a larger scale, what learning engineering does is aggregate teacher knowledge. One thing that doesn’t happen well in education is widescale data sharing. Teachers find what works for their kids, but the teacher in the next school district over doesn’t benefit from that knowledge. Learning engineering analyzes data from large numbers of schools and districts to discover what’s working for which students and when. And when this data is shared, teachers can start to make meaningful changes to their teaching practices.
We’ve talked about how learning engineering seeks to personalize student learning and promotes formative assessment. Does your book mention other ways learning engineering benefits student learning?
I would add motivation. We talk about student outcomes primarily as academic outcomes, but perhaps even more important in the long term is instilling in students the confidence that they can learn and the desire to continue learning. If you don’t have these things, there are substantial limits to how far you’ll get when it comes to student knowledge.
Learning engineering focuses on motivational factors as much as it focuses on academic outcomes. For example, we just completed an experiment focused on localizing the names found in word problems to match names common in each student's community. We think there may be academic benefits, but we're mostly looking to increase students’ sense of belonging in math class.
How do we at Carnegie Learning use a learning engineering approach in our practices and product designs?
I like to say that Carnegie Learning was using learning engineering before people knew the phrase.
Our success with MATHia has been about taking learning science seriously and finding practical ways to apply it. What that means is setting up our software so that it makes predictions about what students are going to find challenging and what they’re going to find easy. We then continuously evaluate those predictions, just as a good teacher would. You try something, ask yourself, ‘will it work?’, see if it does, and if it doesn’t, you change it and try again.
If educators wanted to start using learning engineering in their classrooms tomorrow, what are a few things you would tell them to do?
The first thing is to listen to students.
Engaged and active listening is an incredibly powerful tool, especially during formative assessments. When I say formative assessment, I don’t just mean a test given in the middle of a unit instead of at the end. Formative assessment can just be a conversation with a student. It’s about understanding what the student knows.
One of the techniques we use in MATHia that is supported by cognitive science research and integral to learning engineering is called the Think-Aloud Protocol. When teachers use this protocol, they ask students to solve a problem and think out loud while solving it. And it’s a technique that can be used in all subjects, not just math. When a student arrives at an incorrect answer, chances are that most of their reasoning was correct, or they took a different route to get to their answer, and the Think-Aloud Protocol allows teachers to take all that into account when responding.
Congratulations to Dr. Ritter on his contributions to Learning Engineering Toolkit, which will be published July 25, 2022 by Routledge.
As learning engineering continues to develop, we’re excited to partner with more educators as we collectively move towards an effective, equitable educational system that gives every student the tools and experiences they need to embark on a lifetime of successful learning.
Carnegie Learning is helping students learn why, not just what. Born from more than 30 years of learning science research at Carnegie Mellon University, the company has become a recognized leader in the ed tech space, using artificial intelligence, formative assessment, and adaptive learning to deliver groundbreaking solutions to education’s toughest challenges. With the highest quality offerings for K-12 math, ELA, literacy, world languages, professional learning and more, Carnegie Learning is changing the way we think about education, fostering learning that lasts.Explore more related to this author
But if we start from the student’s perspective, we’d start with different questions, like, “How do we create a change in students’ brains?” Because that’s what learning is.
Dr. Steve Ritter, Chief Scientist and Founder, Carnegie Learning