669 research outputs found

    Concert: Virginia Tech Faculty Chamber Players

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    The Design of Learning Objects by Learners

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    Abstract: A new instructional design paradigm has recently emerged due to the technological innovation known as learning objects (LO). Educational professionals anticipate that learning objects can realize the goals of technology-based learning: adaptivity, generativity, and scalability. Reflecting the popularity of the learning objects movement, many development initiatives have focused on the use of this design approach for the creation of instructional programs. However, these efforts have been criticized for a lack of theoretical background to facilitate learning. In response to this criticism, researchers have worked to apply learning theories and rigorous instructional design processes to develop and design learning objects. These efforts toward adding a theoretical background have provided good support for instructional designers and developers to create pedagogically effective learning materials. But, current research needs to pay more attention to what learners can bring to the design and development of learning objects. A learner-centered design strategy could allow learners the potential to create and repurpose learning objects and share them with others, in addition to using existing learning objects for their own educational enhancement. In view of such a possible design strategy, it seems necessary to investigate the feasibility and effectiveness of the design of learning objects by learners. Investigative inquiry could examine the application of a "learners-as-designers" approach, whereby learners design and develop learning objects using a learning object development tool. To explore the effectiveness of this approach, this future research proposes to find how various learners' characteristics interact with the ability to design learning objects

    Adaptive Life-Long Learning for an Inclusive Knowledge Economy

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    This report addresses the globalized knowledge economy in the 21st century; not only as it exists today, but the knowledge economy needed to meet the demands of tomorrow. This report proposes that in order for our knowledge economy to grow and be sustainable, it must be inclusive in ways that enable it to adapt to—and incorporate within it—the personal and professional growth of a large and diverse body of lifelong learners. In this introduction, we first define what we mean by inclusive knowledge and explain how our proposed definition expands some of the traditional understandings. We then show that an expansive and dynamic conceptualization of knowledge increases inclusion and promotes lifelong adaptive learning as a mindset and a practice

    Statistically significant forecasting improvements: How much out-ofsample data is likely necessary

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    abstract Testing the out-of-sample forecasting superiority of one model over another requires an a priori partitioning of the data into a model specification/estimation ("training") period and a model comparison/evaluation ("out-of-sample" or "validation") period. How large a validation period is necessary for a given mean square forecasting error (MSFE) improvement to be statistically significant at the 5% level? If the forecast errors from each model are NIID and these errors are independent of one another, then the 5% critical points for the F distribution provide the answer to this question. But even optimal forecast errors from well-specified models can be serially correlated. And forecast errors are typically substantially crosscorrelated. For such errors, a validation period in excess of 100 observations long is typically necessary in order for a 20% MSFE reduction to be statistically significant at the 5% level. Illustrative applications using actual economic data are given
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