I’ve been trying to come up with ways to explain to teachers taking my course that there is creativity and opportunity in failure. Many teachers have been tasked to “teach to tests,” which entails instruction that is devoid of passion and meaning as students are taught information from a stripped-down curriculum. I’ve encountered students who respond negatively to this method and at one time I was one of them.
The example I gave was a MIDI controller that high school students created and used to perform (see second video below). This project points to constructionism that supports culturally responsive STEAM learning:
- Project space (project, problem or case)
- Related (robotics) cases (ex. Robotic Musicianship)
- Information resources (ex. Adafruit where students found a tutorial for their project)
- Cognitive tools (ex. attention, sensation, perception, memory)
- Conversation/collaboration tools (ex. Google Jamboard)
- Social/contextual tools (ex. meaning making, concept mapping)
Students spent their spring semester in a STEAM Lab working on their MIDI devices and returned in the fall to complete the project. The students failed frequently and threatened to quit each time but the project kept them coming back. This student programmed his device (his first time coding) and this is the moment when he realized his effort had worked:
This experience had a profound effect on me, not just as a teacher but also as an artist. I started using Deep Dream Generator, a computer vision platform that allows users to input photos and transform them through an artificial intelligence algorithm. I had initially used it to teach high school students about data science. It would take almost two years to get to a point where I wanted to share the results I was getting with the world.
This image is the result of failure. I had to generate three versions to get to one that I was satisfied with. The style images came from the Happy Color mobile coloring app and an abstract painting. Watching students in the STEAM Lab keep at something because they can envision the outcome is/was inspiration for what I’m doing with machine learning. Many see the outcome and do not realize how many images had to be created in order to produce the final version. It took students over six months to finish their MIDI controllers. It took me two years.