The Argument for a “Data Cube” for Large-Scale Psychometric Data - von Davier, Alina A.; Wong, Pak Chung; Polyak, Steve; Yudelson, Michael (Frontiers in Education, 2019)
In recent years, work with educational testing data has changed due to the affordances provided by technology, the availability of large data sets, and by the advances made in data mining and machine learning. Consequently, data analysis has moved from traditional psychometrics to computational psychometrics. Despite advances in the methodology and the availability of the large data sets collected at each administration, the way assessment data is collected, stored, and analyzed by testing organizations is not conducive to these real-time, data intensive computational methods that can reveal new patterns and information about students. In this paper, we propose a new way to label, collect, and store data from large scale educational learning and assessment systems (LAS) using the concept of the “data cube.” This paradigm will make the application of machine-learning, learning analytics, and complex analyses possible. It will also allow for storing the content for tests (items) and instruction (videos, simulations, items with scaffolds) as data, which opens up new avenues for personalized learning. This data paradigm will allow us to innovate at a scale far beyond the hypothesis-driven, small-scale research that has characterized educational research in the past.