In educational technology and learning sciences, there are multiple uses for a predictive model of whether a student will perform a task correctly or not. For example, an intelligent tutoring system may use such a model to estimate whether or not a student has mastered a skill. We analyze the significance of data recency in making such…

Researchers use many different metrics for evaluation of performance of student models. The aim of this paper is to provide an overview of commonly used metrics, to discuss properties, advantages, and disadvantages of different metrics, to summarize current practice in educational data mining, and to provide guidance for evaluation of student…

The growth in the availability of longitudinal data--data collected over time on the same individuals--as part of program evaluations has opened up exciting possibilities for evaluators to ask more nuanced questions about how individuals' outcomes change over time. However, in order to leverage longitudinal data to glean these important insights,…

In the modern world, higher education is transitioning from enrollment mode to recruitment mode. This shift paved the way for institutional research and policy making from historical data perspective. More and more universities in the U.S. are implementing and using enterprise resource planning (ERP) systems, which collect vast amounts of data.…