Tracking ability in adaptive learning systems with the Urnings algorithm: From theory to practice.

Adaptive learning systems are computerised platforms that provide optimal content for students. To fulfil this, one needs an algorithm capable of dynamically updating the estimates of changing student abilities. The Urnings algorithm is a computationally inexpensive method, which provides unbiased estimates and known standard errors and, can track the change without formulating an explicit growth model. However, it is not straightforward how to set up a system to match the characteristics of the learning context and provide optimal tracking and parameter estimation. First, we studied the convergence properties of Urnings when the ability of the students is constant. Second, we tested how well the algorithm can track different types and amounts of change in the ability. Urnings algorithm proved to be successful at adapting to the various simulated learning contexts and capabilities to track changing abilities. Based on the results we provided recommendations for implementing the Urnings algorithm to adaptive learning systems.