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This study explored the Idea Wall, a collaborative knowledge-building tool to support students’ collaboration in small groups during a plant biology science curriculum. We examined the affordances and challenges of the Idea Wall and found the effective use of the tool's spatial organization capabilities by students, particularly the Yup Zone and the intermediary neutral spaces, for collaboratively organizing notes. But there's also a need for improvements in some features of the tool’s design and instructional guidance. 

Computer-aided simulation-based platforms have been shown to be effective tools for teaching STEM concepts. At the same time, Computer Supported Collaborative Learning (CSCL) platforms encourage different viewpoints and approaches from the learners which can enrich the learning experience in STEM classrooms. The deployment in recent years of networked personal devices such as Chromebooks in classrooms has motivated educators to design collaborative learning tools for these devices. However, prior work has shown that using one-on-one devices may discourage students from talking among each other, which hinders collaboration. To understand the affordances of personal devices for CSCL tools within Biology curricula, we designed a collaborative plant growth simulation application that provides mirrored plant growth simulation views for every group member to facilitate a common visualization. In this paper, we present our findings from an in-the-wild study that evaluated the affordance and usability of the plant growth simulation application and investigated the nature of collaboration and engagement aided through the simulation mirroring feature. Our study results showed that the plant simulation application had high usability and acceptance. Moreover, mirroring the plant growth simulation improved collaboration, generated excitement, and stimulated conversation. We also identified episodes where collaboration was hindered due to off-task activities, troubleshooting, group dynamics, and lack of understanding that led us to outline some potential guidelines to improve the collaborative learning experience for the students in Biology classroom. 

Recent algorithms have achieved superhuman performance at a number of twoplayer zero-sum games such as poker and go. However, many real-world situations are multi-player games. Zero-sum two-team games, such as bridge and football, involve two teams where each member of the team shares the same reward with every other member of that team, and each team has the negative of the reward of the other team. A popular solution concept in this setting, called TMECor, assumes that teams can jointly correlate their strategies before play, but are not able to communicate during play. This setting is harder than two-player zerosum games because each player on a team has different information and must use their public actions to signal to other members of the team. Prior works either have game-theoretic guarantees but only work in very small games, or are able to scale to large games but do not have game-theoretic guarantees. In this paper we introduce two algorithms: Team-PSRO, an extension of PSRO from twoplayer games to team games, and Team-PSRO Mix-and-Match which improves upon Team PSRO by better using population policies. In Team-PSRO, in every iteration both teams learn a joint best response to the opponent’s meta-strategy via reinforcement learning. As the reinforcement learning joint best response approaches the optimal best response, Team-PSRO is guaranteed to converge to a TMECor. In experiments on Kuhn poker and Liar’s Dice, we show that a tabular version of Team-PSRO converges to TMECor, and a version of Team PSRO using deep cooperative reinforcement learning beats self-play reinforcement learning in the large game of Google Research Football.