Playful Learning in Robotics: A Case Study with Smart Motors Workshops

In this innovative practice paper, we present case studies of participants using a trainable robotics tool called Smart Motors from two workshops: one with participants from elementary schools in a robotics camp and the second with students from a high school. We designed the workshops to help us observe two key aspects: how students engage with the tool as new users from different age groups and the variability in levels of enjoyment and iterative thinking with an engineering design task while using the tool. Learning robotics can be complex as it can involve learning programming, mechanical design, and electrical circuits simultaneously. Some existing robotics kits support beginners with mechanical design tasks via easy-to-integrate sensor kits, motors, and control hubs, and some support beginners with alternative programming methods like web-based block coding languages and QR codes. However, cost, availability, and ease of use make integrating robotics into the curriculum difficult for some classes. Smart Motors are easy-to-use, low-cost alternatives to lower those barriers to bringing robotics into the classroom. Smart Motors simplifies mechanical “set-up” by packaging the user interface, motor, and sensor in one unit. They use training and a machine learning algorithm called the nearest neighbor to make decisions. Since they do not require coding to generate desired outcomes, students can use them in classrooms without computers or internet access. We allowed first-time Smart Motor users to learn the tool while participating in a play-based design activity with three sessions. The first session of the workshop was a discussion of Artificial Intelligence and Machine Learning. The second session was an introduction to Smart Motors, in which the participants built a “hello-world” waving contraption using Smart Motors and LEGO® pieces. The third session consisted of an activity derived from Tufts University's Novel Engineering curriculum, where the participants listened to a story, chose a character, and designed solutions for them. We analyzed their work with the help of field notes and video data. Using the Learning through Play Experience Tool, we looked for evidence of two of the five characteristics of playful learning: Joyful and Iterative. We transcribed the video data in detail and coded for the states of play in two-minute chunks, where we looked at and analyzed the play trends. We found that Smart Motors supports playful learning in engineering design workshops, allowing participants from different ages and experience levels to engage creatively to design products of various complexity.