Abstract
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.
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Collins, M., Gluck, K. A., & Jastrzembski, T. S. (2015). Datashopping for performance predictions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9183, pp. 12–23). Springer Verlag. https://doi.org/10.1007/978-3-319-20816-9_2
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