On the role of individual human abilities in the design of adaptive user interfaces for scientific problem solving environments

A scientific problem solving environment should be built in such a way that users (scientists) might exploit underlying technologies without a specialised knowledge about available tools and resources. An adaptive user interface can be considered as an opportunity in addressing this challenge. This paper explores the importance of individual human abilities in the design of adaptive user interfaces for scientific problem solving environments. In total, seven human factors (gender, learning abilities, locus of control, attention focus, cognitive strategy and verbal and nonverbal IQs) have been evaluated regarding their impact on interface adjustments done manually by users. People’s preferences for different interface configurations have been investigated. The experimental study suggests criteria for the inclusion of human factors into the user model guiding and controlling the adaptation process. To provide automatic means of adaptation, the Intelligent System for User Modelling has been developed. Elena Zudilova-Seinstra is a Senior Researcher at the Scientific Visualisation and Virtual Reality Group of the University of Amsterdam. Previously, she worked for the Corning Scientific Centre. Apart from being a researcher, in 1999–2002 she was a part-time Assistant Professor at the St. Petersburg Academy of Management Methods and Techniques. She received her M.S. degree in technical engineering in 1993 and Ph.D. in computer science in 1998 from the St. Petersburg State Technical University. In 1996, she received an award for R&D from the Welles-Johnson Foundation of Maryland. She is a Program Committee Member of several International Conferences and Workshops. Her current research interests include multi-modal and adaptive interaction, scientific visualisation, virtual and augmented reality, ambient intelligence and usability studies. She has more than 40 research publications and three editorials in these areas. Also, she has been an INTAS evaluator since February 2005.     

Numerical uncertainty management in user and student modeling: An overview of systems and issues

A rapidly growing number of user and student modeling systems have employed numerical techniques for uncertainty management. The three major paradigms are those of Bayesian networks, the Dempster-Shafer theory of evidence, and fuzzy logic. In this overview, each of the first three main sections focuses on one of these paradigms. It first introduces the basic concepts by showing how they can be applied to a relatively simple user modeling problem. It then surveys systems that have applied techniques from the paradigm to user or student modeling, characterizing each system within a common framework. The final main section discusses several aspects of the usability of these techniques for user or student modeling, such as their knowledge engineering requirements, their need for computational resources, and the communicability of their results.