Can failure be a learning tool? What’s the learning value of “trial and error?” From discussions of teaching “persistence” and “grit” to current educational trends such as making and tinkering, Design Thinking, and hands-on STEM learning, many teachers and schools are embracing opportunities for students to learn through doing and to use inquiry and discovery as a iterative process. Physics teacher Moses Rifkin gave his students a program called Sodaplay which allows them to build simulated robots, and asked them to build robots which could accomplish a set of challenges. The students then had to experiment with multiple designs, learn what worked and what didn’t, and continue to revise them until they had a final product. The student projects and subsequent write-ups showed both their engineering development, as well as their personal growth in moving through a fuzzy challenge that could only be solved by creative and persistent thinking.
It took many cycles of trial + error to get the timing of the legs right. — Student write-up
While Rifkin has run this project in the past, putting it at the beginning of the year was an intentional change this year. “In previous years, I’ve used the Sodaplay modeling website as a tool later in the year, once we’ve talked about what makes structures stand and the complex internal forces within them; this year, as I’ve shifted Physics towards a more project-based approach, I wanted to use Sodaplay to kick off the year,” Rifkin explains. “Having used it for many years, I thought it set a nice tone: in my experience, all students struggle with the project at first, and all students end up creating something that they feel good (and even delighted) about, something they didn’t think was possible a few days earlier.” One of his core objectives was to make them “go through many cycles of experiment-fail-learn-revise-repeat” and develop what he says is “the skill of persistence and patient confidence in the face of initial failure. Some call this an ‘engineering mindset,’ but, again, I think it’s a useful way of approaching even non-scientific problems.”
I was growing very frustrated that I couldn’t get my robot to go quickly that I tried something drastically different… — Student write-up
Students were tasked to document all of their various experiments, and to describe what worked and didn’t about each. They also named each file in a way which indicated a) the type of model (for example, “Star”), and which version (e.g., “Star_2,” “Star_3”). As students shared their process, they began with varied approaches before naturally settling into a promising design which required multiple revisions. The designs range from simple to wildly complex, although students observed that this affected their ability to continue to iterate their designs. “My best solution was maintaining the simplicity of my robot so that when I encountered a problem, there were few variables I could change,” one observed. Even when lines of inquiry “failed,” students noted that there were useful elements which could be taken from each.
After Oracle Bot, I started looking into more organic bot composition. Drawing influence from animals, such as Crabs. Crabs consisted of a walk which could easily be translated to 2-D representation. […] From this idea, came multiple representations of a series of Bots properly under the name of Mudcrab. While ultimately unsuccessful, they enhanced my knowledge. — Student write-up
Because Rifkin wanted students to develop their mindset and approach through the project as well as their Physics knowledge, students filled out a form at 20-minute intervals throughout the project in which they were asked to rate their current levels of Frustration, Engagement, Procrastination and Progress Made. As part of their final write-ups, students had to analyze this data as well as their robot data, and reflect on the process and how these variables interacted. “The more I got into the project, the more frustrated I felt. This was really hard!” wrote one student. “It was not until I actually got a version of my robot to work that my frustration level went down to five. Of course, when I couldn’t get my robot to walk both directions my frustration level went back up. But, I noticed that while I was frustrated I was still pretty engaged in the process, with ranges of typically around several on the graph. I think this was because I wanted to complete my challenge. I couldn’t let this beat me so I continued to try, which is why my procrastination is always below four on the graph and my progress continues to go up.”
“Not going to lie, I was stoked” – “Accomplishment” – “Triumph” – “Achievement” – “Proud” – “Excited” — Students describing their successful models.