Statistical tools to track a multitude of abilities as they develop

Maria Bolsinova

With education's ambition to move towards personalized learning systems, it is crucially important to in digital learning systems be able to accurately track learners' development of skills and abilities as they develop during the learning process. While traditional statistical methods are not well-equipped to address this challenge, this project will develop novel flexible and efficient statistical methods that allow for dynamically tracking a large number of interrelated abilities and skills in learners as they develop over time, where the assessment of the learner's ability levels is updated directly after every new response. The envisioned statistical machinery allows for balancing of the accuracy of the assessment and the speed with which a change in ability is tracked, and can form the statistical basis for the development of a wide range of large-scale online adaptive learning systems. The concrete outcome of the project will be well-tested and open-source statistical software that make these multidimensional analyses possible. By developing the statistical foundations, this project will function as a key stepping stone for the development of personalized learning systems, which requires the statistical machinery for accurately and efficiently tracking abilities to be in place before they can fully be developed, tested, and optimized.

About Maria Bolsinova

Maria Bolsinova is a research scientist at ACTNext based in Amsterdam, the Netherlands. Maria obtained her Ph.D. from Utrecht University in 2016 after which she worked as a postdoctoral researcher at the University of Amsterdam. Her dissertation entitled ?Simple models and complex reality: Contributions to item response theory in educational measurement? has been recognized by the dissertation awards from the Psychometric Society and from the National Council on Measurement in Education. Maria has also received the New Assessment Research Award from the Association of Educational Assessment Europe. Her research has been devoted to developing advanced psychometric models for the analysis of product and process data from educational tests and building statistical foundations for measurement in adaptive learning systems. With her research, she aims to develop statistical tools that will improve the quality of educational assessment in practice and help deliver innovative learning and assessment systems.