Process Mining is a famous technique which is frequently applied to Software Development Processes, while being neglected in Human-Computer Interaction (HCI) recommendation applications. Organizations usually train employees to interact with required IT systems. Often, employees, or users in general, develop their own strategies for solving repetitive tasks and processes. However, organizations find it hard to detect whether employees interact efficiently with IT systems or not. Hence, we have developed a method which detects inefficient behavior assuming that at least one optimal HCI strategy is known. This method provides recommendations to gradually adapt users' behavior towards the optimal way of interaction considering satisfaction of users. Based on users' behavior logs tracked by a Java application suitable for multi-application and multi-instance environments, we demonstrate the applicability for a specific task in a common Windows environment utilizing realistic simulated behaviors of users.
As user interfaces become more and more complex and feature laden, usability tends to decrease. One possibility to counter this effect are intelligent support mechanisms. In this paper, we present AUGUR, a system that provides context-aware interaction support for navigating and entering data in arbitrary form-based web applications. We further report the results of an initial user study we performed to evaluate the usability of such context-aware interaction support. AUGUR combines several novel approaches: (i) it considers various context sources for providing interaction support, and (ii) it contains a context store that mimics the user's short-term memory to keep track of the context information that currently influences the user's interactions. AUGUR thereby combines the advantages of the three main approaches for supporting the user's interactions, i.e. knowledge-based systems, learning agents, and end-user programming.