Computational Conversations with Eli Sheldon
Welcome to Computational Conversations, an interview series where we talk to educators introducing computational thinking into classrooms and curricula. Whether they teach at a high school or develop programs at a university, each interviewee brings new ideas to the table on how to get students and teachers thinking computationally.
Today’s interview is with Eli Sheldon, founding ninth-grade computer science teacher at Rainier Valley Leadership Academy High School. He helps run the Green Dot Computational Thinking Center, a center in the Green Dot Public Schools school system that helps to ideate and implement computational thinking programs.
With his B.A. in mechanical engineering, he’s gone beyond building systems to help teach concepts such as teamwork, social justice and more through his innovative programs and partnerships, some of which have been featured on the education blogs Edutopia and EdSurge. He presented at ISTE 2018, and is now entering his fourth year in the field of education and his first year in the classroom.
What’s your personal definition of computational thinking?
[It’s] a toolbox of problem-solving skills that draw from computer science and can be applied across all disciplines.
What sparked your interest in computational thinking?
While I was working at Microsoft and considering a move into education, a brand-new public middle school opened up a position for a computational thinking program manager to come design and implement a school-wide computational thinking (CT) initiative, in which I’d be working with all teachers on staff to integrate CT projects and units aligned with their learning objectives and standards into their classrooms on a regular basis. The types of thinking and real-world learning I presumed that would entail aligned super closely with my undergrad education at Olin College, and I was super excited to bring that same style of hands-on, learn-by-doing learning to middle-school students.
What value does computational thinking hold within your discipline?
In my role as CT specialist with Green Dot, my mission is to help teachers across all disciplines answer this very question. I work with teachers across math, science, English Language arts, social studies, music, art and PE to help them see the power of CT in helping students access their content and objectives through a new lens. Across all subjects, I see students approach problems more confidently and fail more comfortably when looking at them through that CT lens.
Typically, failure is seen as a devastating event, something to impede progress rather than encourage growth. How would you encourage educators (or students) to learn to “fail comfortably”?
Start with super-low stakes! And make sure everyone fails, and fails frequently. On our first day this year, students built aluminum foil boats to support pennies before sinking. The most important facet of this lesson was that every single boat sank. Everyone “failed,” but everyone got to learn from their failures (and those of their peers) to work on a second iteration, and then a third iteration, so their boats could hold more and more pennies each time. Once everyone accepts failure as both a common occurrence and a learning opportunity, slowly raise the stakes a bit, remembering to loudly shout out each mistake or failure for how we can grow from it.
How have students responded to your computational thinking–focused lessons and activities?
At our schools, “CT” lessons and projects are most often hands-on, real-world activities that students know are relevant to their lives and are challenging, but not inaccessible. As such, students are frequently excited when we announce a lesson’s connection to CT, and they’re eager to stretch a little farther with their work.
What’s your biggest computational thinking success story?
My favorite project to date has been our seventh-grade criminal justice reform unit (blog, lesson plan and materials). In collaboration with our humanities teacher, we took a unit originally centered around reading Monster by Walter Dean Myers and made it a personal, interactive political experience in which students used decomposition to break down the complex beast that is the American criminal justice system, used abstraction and pattern recognition to find trends and systemic issues across multiple real-world cases, and used algorithmic thinking to better understand the processes criminal suspects encounter. Students got to design their own criminal justice systems and apply their new policies and laws to real criminal cases, defending and adjusting their choices with each new wrinkle.
I’m also really proud of our MoneyKickball unit in PE class. Instead of a traditional kickball unit where the most athletic students thrive and others dread being picked last or even going up to kick, we led a rich data-driven unit in which students captured game stats in real time and used anonymized data to draft new teams each class and make in-the-moment strategic shifts to their lineup or defense. This framing of the unit allowed all students not only to participate, but also to understand the value they brought to their team and how they could contribute to that shared success.
Your MoneyKickball unit is interesting in that it focuses more on the social aspects of team sports, facilitated by data. Did the social connections fostered during this project appear to continue on into “regular” gym lessons, with students cooperating more or showing stronger teamwork even without their actions being quantified?
We saw students, who prior to the MoneyKickball unit might struggle to articulate their value to their teams or how they could succeed in PE class, more willing to take athletic risks and to take leadership roles within their teams. By expanding the definition of success within PE to include core academic skills like data analysis and collaboration, many of those students’ grades shot up, as they now identified as students who truly belonged in PE.
Given how hands-on your school’s computational thinking–based activities are, it seems that you’re a fan of project-based learning. Do you think project-based learning is an effective way for educators to begin introducing computational thinking into their classrooms, and if so, how do you suggest educators go about setting up their own projects?
My college experience was almost 100% project-based learning, and it’s personally the way I learn best, which is why I rely on it so much in teaching others.
It may seem like cheating or like you’re not really changing much, but one of the best ways to start with computational thinking is to look at your existing projects and materials and begin to identify the CT skills you’re already asking your students to use. If you’re a science teacher, it’s very likely your students are already using pattern recognition pretty frequently. In English, you’re definitely talking about abstraction, even if you haven’t named it that. Once you find what CT skills are naturally supported by your curriculum, build on those!
I created a CT teacher rubric last year to help out our teachers with this very process. When it comes time to design a new project, I usually hunt around for inspiration or starting points and then identify one or two CT skills I really want to reinforce, adding rigor or extension activities that really challenge students to flex those muscles while tackling the assignment.
What’s been the most challenging part of introducing computational thinking into your curriculum?
Defining it! We made a choice early on to define CT solely through the thinking and problem-solving skills we were asking students to focus on. To that end, they have a good sense of what an algorithm is, and what abstraction looks like, but to this day asking them “What is computational thinking?” leads to a wide array of examples and rambling without anyone agreeing on a concise definition.
You mentioned that defining computational thinking as a concept has been a challenge, and it appears that you all at Green Dot have leaned on the idea of “problem solving” as one of the core definitions. How does problem solving relate to computer science, and do you think that problem solving in computer science has a different nuance than, say, problem solving as a means of understanding literature?
Problem solving is at the heart of computer science. We as humans created computers to help us solve problems, and almost any computer-based activity can be framed as a way to help us solve a problem, be it staying in communication with distant friends, or documenting memories, or entertaining ourselves, or learning a new subject, or anything. Those activities are all about consuming the products of computer science, but it is equally true when creating the products of computer science.
To help people solve problems, we as computer scientists need to think even deeper about how to solve those problems — and that’s where computational thinking comes in, in helping us approach the problem in a confident, structured way. Many of those same CT skills can be equally applied in an English classroom, or an art classroom, or a math classroom — the power of CT is in how it takes those problem-solving skills like decomposition and abstraction and generalizes them to allow students to form cross-disciplinary connections in all of their classes.
Any advice for educators looking to add more computational thinking into their lessons?
I started out just by seeing what was out there — looking at engaging projects that either directly referenced CT skills, or those where injecting some CT skills felt like a natural fit. Identify what it is that you’re already doing that aligns with skills like algorithmic thinking and pattern recognition, and get in the habit of using that vocabulary and pushing students even further in those directions. If your school has a computer science department, chat with your CS teacher(s) about potential cross-disciplinary collaborations!
Thanks to Eli for sharing his experiences and advice! He can be reached via e-mail or on Twitter. The Green Dot Computational Thinking page shares computational thinking–focused lesson plans, including lessons he talked about in this interview.
If you’d like to learn more about computational thinking and explore some browser-based activities from a variety of disciplines, you can also check out the Computational Thinking Initiatives page, or explore previous posts from our archives.
Doing something cool with computational thinking in your classes and want to be featured in the next installment of Computational Conversations? Want to share your story via a post on the Tech-Based Teaching blog? Get in touch at email@example.com.
(Correction: Eli’s B.A. is in mechanical engineering, not computer science, and it’s his first year in the classroom.)
About the blogger:
Jesika Brooks is an editor and bookworm with a Master of Library and Information Science degree. She works in the field of higher education as an educational technology librarian, assisting with everything from setting up Learning Management Systems to teaching students how to use edtech tools. A lifelong learner herself, she has always been fascinated by the intersection of education and technology. She edits the Tech-Based Teaching blog (and always wants to hear from new voices!).