"Data Cubes" for Large-Scale Psychometric Data

"The use of OLAP data cube models for psychometrics opens the door to complex and dynamic uses of that data. This paper asserts that data cube modelling would allow larger, aligned, and integrated datasets to be constructed that could be used to build knowledge graphs or feed machine learning systems". Consider what this means and list some opportunities afforded by applying psychometric criteria to classifying content in e-learning systems that could improve your education.

The Argument for a "Data Cube" for Large-Scale Psychometric Data

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.


Source: Alina A.von Davier, Pak Chung Wong, Steve Polyak, and Michael Yudelson, https://www.frontiersin.org/articles/10.3389/feduc.2019.00071/full
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 License.