Build Computational Literacy in a Collaborative Game
More Details Coming Soon
Science museums often reward visitors who have already engaged with the traditional modes of science learning. However, this dynamic only pushes people from underrepresented communities farther away from the learning opportunities. Rainbow Agents was designed to encourage and promote computational literacy through collaborative play and share sense-making. In our research , we bring in and focus on perspectives from groups typically underrepresented in computer sciences to help further understand how to better design and engage a broader group of audience in informal learning environments.
Zhang, E., Kumar, V. (2019). Designing Collaborative Museum Games for Engaging Computational Thinking Practices. Presented as a poster at the Learning Sciences Graduate Student Conference (LSGSC) 2019. Northwestern University, Evanston, IL, USA. (Poster Presentation)
Pellicone, A., Lyons, L., Kumar, V., Zhang, E., & Berland, M. (2019, October). Rainbow Agents: A Collaborative Game For Computational Literacy. In Extended Abstracts of the Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts (pp. 597-604).
Visualize Learning in the Game
This is a work-in-progress of my dissertation. On the right side, I plotted 115 players’ gameplay. Each row represents a unique player’s entire play trajectory. I used conjecture mapping to align players’ in-game behavior with the underlying learning notions.
Co-play with AI
The goal is to train an AI model that can support and encourage more collaborative play between the two players through popping up just-in-time prompts. The model is trained on multi-modal data including conversation between the players as well as the game states of each player. On the right side, I outline the data flow pipeline.
