A Deep Dive into Computational Thinking with the Wolfram Emerging Leaders Program
Computational thinking is an increasingly relevant and important skill for young people to develop. The ability to break down problems into their component parts, and to piece together a solution quickly and accurately, is important for a variety of careers and pursuits in the 21st century. Even more important, perhaps, is the ability to express ideas clearly enough so that even a computer can understand them.
My role at Wolfram Research focuses on building programs for high-school and college students to explore and learn computational thinking and coding using Wolfram technologies. After acting as a mentor at the Wolfram High School Summer Camp in 2019, I worked to build the Wolfram Emerging Leaders Program, a semester-long experience for Summer Camp alumni to dive deep into computational thinking.
Wolfram High School Summer Camp
The Wolfram High School Summer Camp is an intense, two-week, project-based program aimed at talented high schoolers from around the world. Our students come from a variety of educational backgrounds. Some have taken computer science classes at school. Some have learned to code on their own. Others have never coded at all before applying to the camp.
The first few days of the program are devoted to getting to know the Wolfram Language. Through a combination of traditional classroom lessons, active learning exercises and coding challenges, students quickly learn how to translate their ideas into something computable — the core skill of computational thinking.
Once students have grasped the fundamentals of the Wolfram Language and developed their computational thinking skills in a controlled environment, they are unleashed on an independent project for the duration of the camp. With the support of an expert mentor, students take their projects from a short description, worked out and agreed upon with Stephen Wolfram, to a finished product.
For many students, it’s the first time they’ve had an opportunity to immerse themselves in a project for any length of time. Oftentimes, it’s also the first chance they’ve had to control project outcomes. The students, for the most part, thrive on the independence. They create surprisingly high-quality projects at the intersection of their own passions and their rapidly expanding Wolfram Language skills. In 2019, projects ranged from automatically identifying and displaying meter in Latin poetry, to tracking movement in a squash game, to tricking neural networks into identifying images incorrectly. The variety in projects was real evidence to me of the extraordinary talent pool we had found in these teenagers.
So the camp came to an end, and we waved the students onto their buses, trains and planes, sending them back to their real lives. They had learned a remarkable amount in the two weeks, both in hard technical skills and in broader thinking competencies. It struck me that there was a lot left to learn, and that, perhaps, this could be the beginning of a longer learning journey.
Wolfram Emerging Leaders Program
From there, the Wolfram Emerging Leaders Program, affectionately nicknamed WELP, was born. We took a selection of the Summer Camp students and asked them to join us for 14 weeks to complete a remote group project. If the Wolfram High School Summer Camp is a crash course in computational thinking, then the Wolfram Emerging Leaders Program is a deep dive into project work, team management and long-term development.
I spoke to all of the students and organized them into five groups, each consisting of three to five students, split by interest. I gave them deliberately vague descriptions of why they had been placed together — the “data” group questioned if they had to use machine learning in their project, while the “biology and education” group wondered if they would need to make lesson plans. But the goal was simply to get them thinking, and to focus the program entirely on the students.
WELP was broken into three stages, with each roughly correlating to IDEO’s design thinking steps: ideation, iteration and implementation.
Ideation
In the ideation phase, students focus on finding a problem they want to solve. During WELP, they worked together in their groups to find the intersections of the things they’re interested in, the problems they saw in the real world and the capabilities of Wolfram technologies.
The objective of this stage was for students to come up with a core goal for their project, and to figure out what they wanted to achieve.
At the end of this phase, the groups had a concise problem statement: a sentence or two describing the problem they wanted to solve.
Iteration
After their project statements were approved, the teams moved on to iteration. Iteration is all about generating ideas for solutions to the problem they identified in the previous phase. The goal of this phase was for students to use divergent thinking skills to come up with dozens of potential solutions, and then for them to work their way down to the one solution they wanted to carry through to the end of the program. Students were encouraged to do “quick and dirty” coding, creating short-form, experimental solutions for several of their ideas.
By the end of the iteration phase, students had converged on a single solution and created a minimum viable product (MVP), a draft version of what would become their final product.
Implementation
The students took their MVPs and ran toward the finish line. Their MVPs were intended to go a long way toward addressing the problem or goal that they set in the iteration phase, but this phase was intended for them to refine and implement their projects. By the end of this phase, the groups would have fully realized projects.
In some ways, this was the easiest step for the students. This was a workflow they were familiar with — taking a set task and working toward a set product. Certainly, the students had fewer questions; they were surer of their ability to implement than they had been of their ability to come up with ideas.
In my opinion, there’s a strong fear of being wrong perpetuated in traditional classroom settings, which can stifle students in their attempts to quickly come up with ideas. I encouraged students in WELP not to worry that they were going to be judged on their ideas, and most of the teams settled on an idea that they’d had late in the process. This implies to me that without having a specified time for ideation, the groups would have plowed forward with projects that might not have suited them or worked as well as they did.
The Projects
By the end of the program, four out of the five groups had produced high-quality computational essays, and the fifth group had created a series of science communication videos in which they explored a variety of topics in a live-coding format.
Detecting Gerrymandering with Visualization and Analysis
One group identified gerrymandering, the deliberate redistricting of voting zones to advantage one political party over another, as an issue they wanted to explore. By generating hundreds of randomly districted graphs, this group used several established methods of detecting gerrymandering to attempt to find a baseline for what maps could reasonably be achieved by randomness, and what should be looked at more closely as an attempt to gerrymander.
Exploring and Predicting Unemployment in the USA
Another group decided that they wanted to learn new data science skills, working their way through the data science pipeline of gathering, cleaning, analyzing and predicting with data.
I think that the students learned a lot from this project, not only about the theory behind data analysis and prediction but also about the challenges of applying theory to a real problem.
Non-Deterministic Localized Model of Disease Spread
This group decided to study the spread of disease in a closed population. The traditional way of addressing this problem is setting up a series of coupled equations that model the number of susceptible, infected and recovered individuals in a population.
The group decided to use non-deterministic cellular automata to model their spread of disease. They also made the interesting leap of using parameters for distances, vaccination rates and other measures found in the real world.
Visualizing Topic Progression in a Text
This group wrote some very nifty code to produce visualizations of how topics progress through a text. Originally intended to track lectures or podcasts, the group successfully extrapolated into more generalized text documents. The program takes a text and outputs a series of visualizations showing how the topics progress.
With this fairly unique use of natural language processing, the students managed to create useful and interesting visualizations that allow the user to see the story play out.
Wrapping Up
These projects show the strength and diversity of our students. Over the course of the program, they learned several important skills. First, and most obviously, their coding skills and content knowledge improved dramatically. There was a noticeable difference in the quality of the students’ work from the end of Summer Camp to the end of WELP, and a just-as-noticeable improvement from their MVPs to their final products.
Second, they learned how to work in a remote team. This is a skill that they will use with increasing regularity in the workplace and in projects they’ll do as they pursue further education.
Third, I like to think that their computational thinking skills improved dramatically. Several of the WELP students will be attending the Summer Camp in 2020 as teaching assistants, so it will be interesting to see this improvement in action, especially as they will be helping to deliver casual learning opportunities specifically designed to improve computational thinking skills.
Personally, I really enjoyed mentoring the WELP students as part of this program. Seeing them work hard to deliver a project was gratifying, and watching them develop both personally and academically is a point of personal pride. I am very much looking forward to seeing what these students achieve in the coming years.
For high-school students who are interested in starting or continuing a journey into computational thinking, I advise applying to the Wolfram High School Summer Camp, which takes place near Boston each July. A variety of financial aid options are available, and it’s a remarkable opportunity to develop core skills, create an interesting project and make friends with like-minded high-school students from around the world.
About the blogger:
Rory Foulger
Rory Foulger is an Instructional Designer and Technologist at Wolfram Research. They graduated in 2019 as part of the first graduating class at Minerva Schools, where they studied computational sciences alongside a broad, problem-based curriculum. Rory is passionate about teaching and learning computational thinking, as well as decision science, problem solving and technology.
