SOAR: an architecture for general intelligence
Artificial Intelligence
A programming language basis for user interface
CHI '89 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The keystroke-level model for user performance time with interactive systems
Communications of the ACM
The Architecture of Cognition
Approaches to Adaptivity in User Interface Technology: Survey and Taxonomy
Proceedings of the IFIP TC2/WG2.7 Working Conference on Engineering for Human-Computer Interaction
Extracting usability information from user interface events
ACM Computing Surveys (CSUR)
A survey of software learnability: metrics, methodologies and guidelines
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic detection of users' skill levels using high-frequency user interface events
User Modeling and User-Adapted Interaction
International Journal of Cognitive Performance Support
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This paper describes an algorithm to detect user's mental chunks by analysis of pause lengths in goal-directed human-computer interaction. Identifying and characterizing users' chunks can help in gauging the users' level of expertise. The algorithm described in this paper works with information collected by an automatic logging mechanism. Therefore, it is applicable to situations in which no human intervention is required to perform the analysis, such as adaptive interfaces. An empirical study was conducted to validate the algorithm, showing that mental chunks and their characteristics can indeed be inferred from analysis of human-computer interaction logs. Users performing a variety of goal-directed tasks were monitored. Using an automated logging tool, every command invoked, every operation performed with the input devices, as well as all system responses were recorded. Analysis of the interaction logs was performed by a program that implements a chunk detection algorithm that looks at command sequences and timings. The results support the hypothesis that a significant number of user mental chunks can be detected by our algorithm.