DataShopping for Performance Predictions

机译：数据用于性能预测

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Mathematical models of learning have been created to capitalize on the regularities that are seen when individuals acquire new skills, which could be useful if implemented in learning management systems. One such mathematical model is the Predictive Performance Equation (PPE). It is the intent that PPE will be used to predict the performance of individuals to inform real-world education and training decisions. However, in order to improve mathematical models of learning, data from multiple samples are needed. Online data repositories, such as Carnegie Mellon University's DataShop, provide data from multiple studies at fine levels of granularity. In this paper, we describe results from a set of analyses ranging across levels of granularity in order to assess the predictive validity of PPE in educational contexts available in the repository.

机译：已经创建了学习的数学模型，以利用当个人获取新技能时所见的规律性，如果在学习管理系统中实施可能是有用的。一种这样的数学模型是预测性能方程（PPE）。 PPE的意图将用于预测个人的表现，以告知现实世界教育和培训决策。然而，为了改善学习的数学模型，需要来自多个样本的数据。在线数据存储库，如Carnegie Mellon University的数据库，提供来自粒度细水平的多项研究的数据。在本文中，我们描述了一组分析的结果，这些分析在粒度水平范围内，以评估存储库中可用的教育环境中PPE的预测有效性。

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《International Conference on Augmented Cognition》|2015年||共12页
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作者
Michael Collins; Kevin A. Gluck; Tiffany S. Jastrzembski;
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中图分类 TP11-53;
关键词
Performance prediction; Datashop; Repository; Learning optimization; Mathematical models;

机译：性能预测;数据库;存储库;学习优化;数学模型;

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4. DataShopping for Performance Predictions [C] . Michael Collins, Kevin A. Gluck, Tiffany S. Jastrzembski International conference on human-computer interaction;International conference on augmented cognition . 2015

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8. Prediction des Performances des Moteurs a Turbine a Gaz en Regimes Etabli etTransitoire (Steady and Transient Performance Prediction of Gas Turbine Engines) [R] . 1992

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DataShopping for Performance Predictions

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