Organized by the International Working Group on Educational Data Mining.


Université du Québec à Montréal (UQAM), Canada

Machine Learning Department at the School of Computer Science, Carnegie Melon University

Brian W. Junker: Assessment Modeling in Educational Data Mining

Cecil and Ida Green Professor of Physics, Department of Statsitics, Carnegie Mellon University (Pittsburgh, PA)

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Educational data mining can be defined as the process of converting raw data from educational systems into information that can be used to inform design decisions and answer research and policy questions. Much of the raw data comes from assessments embedded in educational
systems - from task performance data in online tutoring systems to standardized state tests at the end of the school year.  Psychometrics has grown from a few rules of thumb about point-biserial correlations, Cronbach's alpha and scree plots to a modern statistical toolkit for modeling assessment data and deriving inferences about students, tasks and educational systems from them.  In this talk I will sketch the development of psychometrics into a statistical modeling toolkit,
indicate some of the ways in which statistical psychometrics can help with educational data mining tasks, and survey some research at the interface between psychometrics and EDM.