We present a new technology-based paradigm to support embodied mathematics educational games, using wearable devices in the form of SmartPhones and SmartWatches for math learning, for full classes of students in formal in- school education settings. The Wearable Learning Games Engine is web based infrastructure that enables students to carry one mobile device per child, as they embark on math team-based activities that require physical engagement with the environment. These Wearable Tutors serve as guides and assistants while students manipulate, measure, estimate, discern, discard and find mathematical objects that satisfy specified constraints. Multi-player math games that use this infrastructure have yielded both cognitive and affective benefits. Beyond math game play, the Wearable Games Engine Authoring Tool enables students to create games themselves for other students to play; in this process, students engage in computational thinking and learn about finite-state machines. We present the infrastructure, games, and results for a series of experiments on both game play and game creation.
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Experiments with sensorimotor games in dynamic human/machine interaction
While interacting with a machine, humans will naturally formulate beliefs about the machine's behavior, and these beliefs will affect the interaction. Since humans and machines have imperfect information about each other and their environment, a natural model for their interaction is a game. Such games have been investigated from the perspective of economic game theory, and some results on discrete decision-making have been translated to the neuromechanical setting, but there is little work on continuous sensorimotor games that arise when humans interact in a dynamic closed loop with machines. We study these games both theoretically and experimentally, deriving predictive models for steady-state (i.e. equilibrium) and transient (i.e. learning) behaviors of humans interacting with other agents (humans and machines). Specifically, we consider experiments wherein agents are instructed to control a linear system so as to minimize a given quadratic cost functional, i.e. the agents play a Linear-Quadratic game. Using our recent results on gradient-based learning in continuous games, we derive predictions regarding steady-state and transient play. These predictions are compared with empirical observations of human sensorimotor learning using a teleoperation testbed.
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- Award ID(s):
- 1836819
- NSF-PAR ID:
- 10111544
- Date Published:
- Journal Name:
- Micro- and Nanotechnology Sensors, Systems, and Applications XI
- Page Range / eLocation ID:
- 81
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2519258
