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DataShopping for Performance Predictions

机译：通过DataShopping进行性能预测

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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，以利用个人获得新技能时看到的规律性，如果在学习管理系统中实施，这可能会很有用。一种这样的数学模型是预测性能方程（PPE）。 PPE的目的是用来预测个人的表现，从而为现实世界中的教育和培训决策提供依据。但是，为了改善学习的数学模型，需要来自多个样本的数据。卡内基·梅隆大学（Carnegie Mellon University）的DataShop等在线数据存储库以精细的级别提供了来自多个研究的数据。在本文中，我们将描述一系列粒度范围内的分析结果，以便评估存储库中可用的教育背景下PPE的预测有效性。

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