A cognitive task analysis (CTA) and a laboratory study examined the cognitive and communicative processes technical support workers use when providing remote, Internet-based technical support, with a particular focus on the role of the end user who is receiving technical support. In the CTA, 6 experienced technical support employees communicated with end users to solve 4 technical support problems while providing the task analyst with a verbal protocol of their thoughts and actions. The results of the CTA include procedural diagrams illustrating the technical support process and the concept of “user diagnosis.” The subsequent laboratory study examined how two characteristics of technical support end users, their emotional state and the level of detail in their problem descriptions, affected stress levels and performance of technical support personnel. Angry end users caused more subjective stress to technical support workers than happy users, and technical support employees perceived problems to be more difficult when they were interacting with vague users or angry users. When end users were vague or angry, technical support workers' problem-solving time and performance ratings were significantly worse. Taken together, the results from these studies can be applied to improve the technical support process by providing interfaces that scaffold the communicative aspects of the technical support process and adding training and evaluation for support workers in communication and interpersonal skills.

We describe the second phase of an ongoing study whose aims are to explore how computer science students solve problems and to understand the relationship between metacognition and schemata. This research uses verbal protocols from computer science students, and based on the analysis of their transcripts, empirical evidence supports metacognition as a significant role in computer science problem solving. For the problem solved in this research, the students reveal a pattern in their interactions between metacognitive processes and the domain-specific strategies used to develop a program.

“AWE” is a programmable “Animated Work Environment” supporting everyday human activities, at home, work and school, in an increasingly digital society. AWE features a novel robotic “wall,” three horizontal, reconfigurable work surfaces, and embedded information technologies. The video shows AWE as a digital simulation moving through six standard wall-desk configurations, interspersed with still photos and video clips of people interacting with the physical, full-scale, working prototype. The video also shows AWE beginning to behave intelligently as well as users fine-tuning AWE's configurations by gesturing proximity sensors mounted at the hinges between wall panels. Usability testing suggests that AWE clearly adapts to variations in complex activities involving users working or playing in a single physical space with both physical and digital tools and artifacts.

Subpixel addressing is a font-rendering technology that triples the apparent horizontal resolution of liquid crystal displays. Four experiments measured the effects of subpixel addressing (Microsoft's ClearType) relative to standard (aliased) font-rendering techniques. Participants preferred, and gave higher readability ratings to, text that had been rendered using subpixel addressing. Subpixel addressing also significantly improved the accuracy of lexical decisions and the accuracy and speed of sentence comprehension. Subpixel addressing did not affect word-naming performance or reading speed during pleasure reading. Taken together, these findings suggest that subpixel addressing provides substantial benefits to users while adding no costs to display hardware.

Two experiments investigated expert-novice differences in debugging computer programs. Debugging was done on programs provided to the subject, and were run on a microcomputer. The programs were in LOGO in Exp. 1 and Pascal in Exp. 2. Experts debugged more quickly and accurately, largely because they generated high quality hypotheses on the basis of less study of the code than novices. Further, novices frequently added bugs to the program during the course of trying to find the original one. At least for these simple programs, experts superior debugging performance seemed to be due primarily to their superior ability to comprehend the program.