669,840 research outputs found

    Rich environments for active learning: a definition

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    Rich Environments for Active Learning, or REALs, are comprehensive instructional systems that evolve from and are consistent with constructivist philosophies and theories. To embody a constructivist view of learning, REALs: promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making, and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher-order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning-to-learn within authentic contexts using realistic tasks and performances. REALs provide learning activities that engage students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, REALs are a response to educational practices that promote the development of inert knowledge, such as conventional teacher-to-student knowledge-transfer activities. In this article, we describe and organize the shared elements of REALs, including the theoretical foundations and instructional strategies to provide a common ground for discussion. We compare existing assumptions underlying education with new assumptions that promote problem-solving and higher-level thinking. Next, we examine the theoretical foundation that supports these new assumptions. Finally, we describe how REALs promote these new assumptions within a constructivist framework, defining each REAL attribute and providing supporting examples of REAL strategies in action

    Searching for a Modernized Voice: Economics, Institutions, and Predictability in European Competition Law

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    Public Catholicism: An American Prospect

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    Ensemble evaluation of hydrological model hypotheses

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    It is demonstrated for the first time how model parameter, structural and data uncertainties can be accounted for explicitly and simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. As an example application, 72 variants of a single soil moisture accounting store are tested as simplified hypotheses of runoff generation at six experimental grassland field-scale lysimeters through model rejection and a novel diagnostic scheme. The fields, designed as replicates, exhibit different hydrological behaviors which yield different model performances. For fields with low initial discharge levels at the beginning of events, the conceptual stores considered reach their limit of applicability. Conversely, one of the fields yielding more discharge than the others, but having larger data gaps, allows for greater flexibility in the choice of model structures. As a model learning exercise, the study points to a “leaking” of the fields not evident from previous field experiments. It is discussed how understanding observational uncertainties and incorporating these into model diagnostics can help appreciate the scale of model structural error
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