Model-Driven Research in Human-Computer Interaction

Abstract

How can we build systems that enable users to mix and match tools together? How can people share the results of their explorations with each other, and for innovative tools to be remixed? Widely-used tools such as Web Browsers, Wikis, spreadsheets, and analytics environments like R all contain models of how people mix and combine operators and functionalities. In my own research, system developments are very much informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide-variety of user behaviors in human-computer interaction (HCI), from individuals interacting with a search system like MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behavior. In this talk, I will illustrate how a model-driven approach to answering the above questions should help to illuminate the path forward for HCI research.