Assessing User Competence for Multiattribute Data Cognition

While computers provide access to a multitude of data, the comprehension of that data for decision making and communication purposes relies on humans. What characteristics emerge as important to the skill of data interpretation and is training possible to develop this skill? This paper assesses user competence for the perception and comprehension of graphical computer displays using a knowledge, skills and abilities framework. The basic research questions are: how do characteristics that are inherent to an individual (abilities) affect the use of moving graphical displays, how do characteristics that may be altered through training (skills) affect such use, and how does knowledge related to the task affect use? Following a model of skill acquisition, the possible characteristics of importance are presented based on an experiment conducted using an animated, graphical computer screen. Results indicate that age and domain knowledge are correlated with task performance. Younger subjects performed better, as did those subjects already familiar with the data domain of the states in the United States.