The main aims of this chapter are to:
The evaluation methods described so far in this book have involved interaction with, or direct observation of, users. In this chapter we introduce methods that are based on understanding users through knowledge codified in heuristics, or data collected remotely, or models that predict users' performance. None of these methods require users to be present during the evaluation. Inspection methods typically involve an expert role-playing the users for whom the product is designed, analyzing aspects of an interface, and identifying any potential usability problems by using a set of guidelines. The most well known are heuristic evaluation and walkthroughs. Analytics involves user interaction logging, which is often done remotely. Predictive models involve analyzing the various physical and mental operations that are needed to perform particular tasks at the interface and operationalizing them as quantitative measures. Two of the most commonly used predictive models are GOMS and Fitts' Law.