Computer-supported form design using keystroke-level modeling with reinforcement learning

Abstract

The Keystroke-Level Model (KLM) is commonly used to predict a user's task completion times with graphical user interfaces. With KLM, the user's behavior is modeled with a linear function of independent, elementary operators. Each task can be completed with a sequence of operators. The policy, or the assumed sequence that the user executes, is typically pre-specified by the analyst. Using Reinforcement Learning (RL), RL-KLM [4] proposes an algorithmic method to obtain this policy automatically. This approach yields user-like policies in simple but realistic interaction tasks, and offers a quick way to obtain an upper bound for user performance. In this demonstration, we show how a policy is automatically learned by RL-KLM in form-filling tasks. A user can interact with the system by placing form fields onto a UI canvas. The system learns the fastest filling order for the form template according to Fitts' Law operators, and computes estimates the time required to complete the form. Attendees are able to iterate over their designs to see how the changes in designs affect user's policy and the task completion time.