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.
What methods can we use to help understand why users adopt certain use strategies, and how can we evaluate designs to anticipate and perhaps positively modify how users are likely to behave? This paper proposes taking advantage of social network analysis (SNA) to identify features of interaction. There are plausible reasons why SNA should be relevant to interaction programming and design, but we also show that SNA has promise, identifies and explains interesting use phenomena, and can be used effectively on conventionally-programmed interactive devices. Social network analysis is a very rich field, practically and theoretically, and many further forms of application and analysis beyond the promising examples explored in this paper are possible.