CogQN: A Queueing Model that Captures Human Learning of the User Interfaces of Session-Based Systems

Abstract

A session-based system provides various services to its end users through user interfaces. A novice user of a service’s user interface takes more think time—the time to comprehend the content, and the layout of graphical elements, on the interface—in comparison to expert users. The think time gradually decreases, as she repeatedly comprehends the same interface, over time. This decrease in think time is the user learning phenomenon. Owing to this learning behavior, the proportion of users—at various learning levels for different services—changes dynamically leading to a difference in the workload. Traditionally though, workload specifications (required for system performance evaluation) never accounted for user learning behavior. They generally assumed a global mean think time, instead. In this work, we propose a novel queueing network (QN) model called CogQN that accounts for user learning. It is a multi-class QN model where each service and its learning level constitute a class of users for the service. The model predicts overall mean response times across different learning modes within 10% error in comparison to empirical data.