🇨🇿Monotonicity in Bayesian Networks for Computerized Adaptive Testing

Presented at the 10th Annual BayesiaLab Conference on Friday, October 28, 2022.

Abstract

Testing of human skills and abilities is a task that is being repeated frequently in the modern world. In this talk, we will explore an approach to Computerized Adaptive Testing using Bayesian Networks. This concept aims at modeling a student and measuring his/her skills. This effort allows us to create a shorter and more precise test as we are able to ask questions suiting the particular student better. We will also present the effect of a special condition of Bayesian Networks used for this task, monotonicity. The monotonicity condition requires a model to satisfy special conditions placed on its parameters. This condition is especially helpful in cases where the learning dataset is small. We will present a new method for learning monotone parameters. Based on our experiments these models provide better results than non-monotone methods and competitive monotone methods. Monotonicity is an important concept that helps to learn models and allows us to learn more reliable parameters. Monotone models are more likely to be accepted by final users in areas where monotonicity is to be expected.

Presentation Video

Presentation Slides

About the Presenter

Martin Planjer is the Director of the Research and Development department in the consultancy company Logio. This department's goal is to keep the company at the technological edge and to provide new methods and methodology. This is done by seeking for new approaches, prototyping, and defining new products.

Martin is also a junior researcher at the Institute of Information Theory and Automation (UTIA) in the field of decision-making theory with mathematical modeling background from Ph.D. studies and the Czech Technical University.

These two parts provide an opportunity to combine the business and the academic world and to challenge both theoretical concepts as well as established practices.