How Project Tomorrow Is Helping Teachers Incorporate Computational Thinking Into Their Instructional Practice
Computational thinking is often associated with computer science. After all, computer scientists draw on decomposition, pattern recognition, abstraction, algorithm design, and other elements of computational thinking in their day-to-work. But those problem-solving strategies are also important in a variety of other fields and subjects.
That concept is the driver behind Project Tomorrow’s computational thinking (CT) programs. The nonprofit educational research and professional learning organization uses a proprietary evaluation tool to identify a teacher’s readiness and proficiency level with CT, and then develops an individualized professional learning plan tailored to their unique growth and development. By working across entire grade levels within schools, Project Tomorrow seeks to help teachers prepare students to solve challenging problems, regardless of whether those students one day become computer scientists.
We sat down with Project Tomorrow CEO Julie Evans to discuss why computational thinking skills are important for all learners to develop; how Project Tomorrow reaches teachers where they are; how a personalized and customized approach to professional learning can scale without losing quality; and why a “campfire approach” to innovation won’t change systems.
Over the last several years, Project Tomorrow has built a cadre of projects around computational thinking. Why did you feel it was important to focus on computational thinking?
The vision of Project Tomorrow is to ensure that today’s students are well prepared to be tomorrow’s innovators, leaders, and engaged citizens of the world. Computational thinking emerged as an important dimension of that work.
At Project Tomorrow, we are focused on using research to inform practice. In our literature reviews, we found one camp of scholars who identified computational thinking as a beginning pathway for students to enter computer science courses. There was another school of thought that argued that we also need to look at computational thinking as an important literacy for all students to acquire. Karen Brennan at Harvard Graduate School of Education has written and spoken very eloquently about that.
We’re in that second camp. We want all students to be good problem solvers. So how do we help them develop those muscles as good problem solvers from a very early age? If that takes them into computer science, that’s great, but we don’t need every student to become a computer scientist. But every student should be a good problem solver.
We also wanted to understand the practices that teachers can use to help students develop these problem solving skills. Some time ago, we polled teachers to figure out how skills like computational thinking become part of the DNA of teacher practice, rather than a one-off lesson. Without that type of coordinated effort, we end up with a bunch of “campfires” of innovation spread out over the place and nothing that is sustainably transforming the education process. That led us to focus on building capacity among all teachers in a grade or school, working with schools and districts to coordinate those efforts and sustain the innovations.
Once you decided that computational thinking was an important area of focus, how did you develop your programs? What elements of computational thinking did you decide to concentrate on?
We based our approach in the translation of research into practice, focusing on elements that have shown to work in driving innovation in the classroom and transforming the learning process for students. What actually needs to happen from a mechanics standpoint?
We began by identifying the elements of computational thinking that were important and settled on four pillars: decomposition, pattern recognition, abstraction, and algorithm design. Those skills came up again and again in the literature. There were other related concepts like debugging, but we saw those more as applications rather than core concepts.
Importantly, one of the things that came out of our previous work was the importance of making teachers feel comfortable. That is, if we can seed new instructional practices through the prism of what teachers are currently doing, they are more likely to feel comfortable about experimenting, even though it may be a new direction for them or a new tool. We try to build on and enhance things that they’re doing already. We’re not coming in and saying, “Everything that you’re doing currently is substandard or insufficient.” We’re actually saying, “Everything you’re doing is great. We just want to help you get to the next level.”
Elementary educators already saw themselves and their instructional practice within those four pillars. They would say, “I do pattern recognition. I didn’t know it was computational thinking. But when I’m teaching reading or when we’re doing a science lab, we’re doing pattern recognition.” We wanted to have a doorway for teachers that was comfortable, where they could say, “I’m already doing those things.” That gave us a good starting point for building sustainability and persistence.
Project Tomorrow decided to focus those efforts on elementary school educators. Why?
Most of the computer science conversations happening at the time were workforce-related. How do we develop a group of future employees for different industries? That was largely a high school-oriented conversation.
We were also seeing that districts’ computer science offerings were sometimes disjointed. There wasn’t a clear articulation from elementary school into high school. They were doing things like Hour of Code or a coding project here or there. They might give teachers little stipends to do things with robotics. But there wasn’t a clear learning arc that went all the way through K-12 education.
Again, computational thinking is really about how we solve problems. We can’t suddenly take an eighth grader and say, “We want to make you into a problem solver. Let me teach you all the problem solving strategies.”
We wanted to start that process in elementary school, when students are younger and when there’s more fluidity across the curriculum for the integration of the computational thinking concepts. That way, with reinforcement and continual articulation, by the time the students get to middle school or high school, they’re already fluent in problem solving. That would then help their own self-efficacy as a learner, and also help them succeed in middle school and high school—both in computer science classes and in all of their learning.
Teachers are crucial partners in Project Tomorrow’s work. What does that partnership look like when it comes to computational thinking? What role does teacher readiness play in your programming?
In the traditional model for rolling out an innovation, schools look for volunteers. Teachers and staff raise their hand and say, “I’d like to volunteer for this,” or “I’d like to get that professional development.” We specifically avoided that because that approach doesn’t get innovation into the culture of the school. Instead, we focused on entire grade levels. Those teachers might include innovators, early adopters, laggards—the whole gambit. We wanted to start building capacity at the school level, not only to build individual teacher capacity in the classroom.
Elementary school principals bought into that approach because they are thinking about skill development of students, not just content knowledge measured on assessment tests. Realizing that vision is challenging. How do you build that capacity for student skill development en masse as a principal? How can you lift up all teachers, to serve all students? Our approach of working with entire grade levels, and now working school-wide really resonated with those principals.
In a prior NSF grant, we found that personalizing professional learning had a greater impact on student learning than what was taught in that professional learning. The key elements were understanding teachers’ prior knowledge, values, and belief systems around the intervention. We developed a way to quantify that to develop a personalized professional learning plan that was effective in moving teachers along a journey of increased competency. That was far more effective than a “one-size-fits-all” approach.
The readiness spectrum that we developed for our computational thinking projects looks at skills, mindset, valuation, teachers’ relationship with technology, and concerns. We evaluate responses across a spectrum of 16 teacher profiles. We have a diagnostic tool that teachers complete, and then we use that feedback to identify where teachers are. We then position professional learning for them that’s highly customized and personalized to exactly where they are.
What does that look like in practice?
Most teachers are not really familiar with computational thinking. They may be doing the practices, but they’re not familiar with the concepts. If they’re not familiar with the concepts, they don’t value it. They don’t see it as an equity imperative for their students. They don’t see computational thinking as problem solving because they don’t have the familiarity. Similarly, we have found that if teachers are not using technology in a sophisticated or advanced way in their classroom, they often also don’t have an appreciation of computational thinking.
For those teachers, we begin by helping them understand the valuation of digital tools in their classroom and help them with the implementation of some of those digital tools. That in turn helps them learn how they can incorporate those tools into their instructional practice. In addition, it opens the door for them to understand the purpose around computational thinking as a problem-solving strategy using those tools.
On the other side, we have teachers who indicate that they have had some prior training on computational thinking, who are strong technology users, and who understand the value of the tech. But maybe their concern is about classroom management. Maybe their question is: “How do I change my position from being the answer teacher where I’m telling you how to solve the problem to letting the students solve the problem on their own?” That teacher is going to require a different type of mentoring and coaching.
How are you working to scale that model, while preserving the customized approach that you described?
In our work in New York City, we realized that we could scale our approach by working with instructional coaches who are already in schools, rather than just bringing in our own instructional coaches to work with teachers at each site. The school instructional coaches could be literacy coaches, math coaches, technology coaches, special education coaches, or others. We’re training them in computational thinking and also in how to personalize their coaching and mentoring about computational thinking with teachers.
For example, the literacy coach is working with the second grade teachers, helping their Tier Two readers become more proficient. At the same time, they’re bringing in computational thinking so that they are literacy coaches enhanced by computational thinking. Or they’re math coaches that have this added set of tools. In that way, it’s highly personalized. The capacity also is now with staff who are already resident in the school.
That’s the approach we’ve taken in our computational thinking professional programs in Michigan, where CT learning is now included in the state budget. We’ve been working with the Michigan State Department of Education on pilot programs, and they were very excited to see that we had a way of measuring outcomes. That is, we could see teachers’ readiness coming into the program, and then see where teachers moved on our proficiency spectrum over the course of the year. That helped to demonstrate a return on investment. Measuring teacher readiness on a pre- and post-evaluation was one of the things that was included in the legislation in Michigan. And individual teachers have a way of assessing their own effectiveness. Instructional coaches know how they can best support individual teachers.
The teachers are very receptive to that approach. For example, I recently spoke to a fourth grade math teacher who told me, “I’ve always taught multiplying fractions the same way, for 16-plus years.” He started changing it up by incorporating a little bit of computational thinking. He gave students the opportunity to think about what an algorithmic design would be and had them create the steps for multiplying fractions. Or he talked about abstraction with multiplying fractions, by asking, “What are the things we don’t need to worry about here?” He began to realize that the lesson was better this way. He was able to see positive outcomes in the classroom as a result of these instructional practices.
What impact is this work having across schools and on students?
We’re seeing big changes in school culture. One principal said that she used to find that teachers weren’t really understanding either their standards or good pedagogical practices, but were instead using tricks that would get their students to a certain place of understanding. The infusion of computational thinking has caused teachers to slow down, to think about their standards, to think about their pedagogical practices, to think about their role within the instructional model. The principal believes that the focus on computational thinking is helping the teachers be more effective overall.
In addition, we’re seeing schools focus more on student skill development and to be able to actualize what equity means in terms of learning experiences. That’s because we see computational thinking as important for all students and all teachers. We’re deploying our model across entire grades, across entire schools.
There are big impacts on teachers, too. They’re telling us that as they get more proficient with computational thinking, they recognize the value of that growth. Now they want to do the same for their students. That pushed us to think about student assessments on computational thinking, since we could not find much in the literature on this. We wanted something where we could see the students using the four concepts across different curricula, even though maybe in their class, they had only done it in a single subject. Could they apply a strategy that they had learned in Math to ELA, for example?
We created a student assessment that mirrors our student spectrum of competencies in computational thinking, and a student skills inventory diagnostic that the teachers can use as a formative tool. We also developed a student self-efficacy survey to understand what students felt was the long-tail impact of computational thinking learning. We’re seeing students say that they believe they are better learners, that they persevere more through problems than they did before, that they are better set up for success in their next grade level. They say that they are collaborating more with their classmates. They feel that they are self-directing their own learning in very significant ways. That self-efficacy of the learner is huge, particularly in the high poverty, under-resourced schools that we’re serving. It’s magnified even more when that gets articulated, grade-level to grade-level to grade-level, not only in a single grade.
You have been at this work for many years. Are there any final learnings you’d like to share?
I believe very strongly in two key things. First, every student should learn computational thinking strategies. It’s an equity imperative. I don’t care whether that’s an urban school, a suburban school, or a rural school—every student should have those strategies as part of their learning portfolio.
Second, every teacher can incorporate computational thinking across their curriculum. They just need to be introduced to it. They need to be mentored and coached along the way, but they can transform and persist with that transformation of their instructional practice with computational thinking if provided with the opportunity. That’s what we are advocating for in all the projects that we’re working on at Project Tomorrow.
###
Dr. Julie A. Evans is the CEO of Project Tomorrow and is the founder of the heralded Speak Up Research Project which annually collects and reports on the authentic views of K-12 students, parents, and educators nationwide on key education issues. Dr. Evans serves as the chief researcher on the Speak Up Project as well as leading research efforts on the impact of innovative learning models and interventions in both K-12 and higher education.
Dr. Evans is a graduate of Brown University and earned her doctorate in educational leadership from the University of California, San Diego and California State University San Marcos. She serves on several boards and is a frequent speaker and writer on new learning models within education, most notably around digital learning. Among her many accolades and awards, Dr. Evans was named in April 2023 as the winner of EdTech Digest’s National Leader award. Dr. Evans is the author of the newly published book, Free Agent Learning: Leveraging Students’ Self-Directed Learning to Transform K-12 Education.
