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Creators/Authors contains: "Mu, Tong"

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  1. ; ; ; (, Work in Progress, Learning at Scale 2018)
    Learning at scale (LAS) systems like Massive Open Online Classes (MOOCs) have hugely expanded access to high quality educational materials however, such materials are frequently time and resource expensive to create. In this work we propose a new approach for automatically and adaptively sequencing practice activities for a particular learner and explore its application for foreign language learning. We evaluate our system through simulation and are in the process of running an experiment. Our simulation results suggest that such an approach may be significantly better than an expert system when there is high variability in the rate of learning among the students and if mastering prerequisites before advancing is important. They also suggest it is likely to be no worse than an expert system if our generated curriculum approximately describes the necessary structure of learning in students. 
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  2. ; ; ; ; ; ; ; ; ; ; et al (, Science)
    null (Ed.)
    The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature. 
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