LELAND STANFORD JUNIOR UNIVERSITY, THE

New and exciting technologies have created opportunities for developing rich open-ended learning environments that combine a number of different learning paradigms and resources. Students can complete quests in game environments, engage in inquiry, interact with virtual agents, run science simulations, take quizzes, access the web, and more generally make choices about different learning activities. These choices can be extremely informative about student learning and diagnostic of what students can learn once they leave highly scripted curricula. We are using machine learning methods to analyze student choices in relatively open learning environments and determine whether students are showing (sub) optimal behavior patterns. The environment can then adapt intelligently by encouraging (alternative) choices and better learning behaviors. Our primary hypothesis is that helping students develop the abilities to make learning choices will have strong effects on their subsequent abilities to learn in the future in unstructured and unsupervised but resource-rich environments.

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