automatic chunk detection in human-computer interaction
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.
automation chunk detection chunking detection human human-computer interaction interaction paper algorithm user user analysis help expertise informal log situated adaptable interfaces empirical studies tool input devices systems sequence timing support direct novice/expert differences event logging users user study models of the user