GVU Technical Report Number:
GIT-GVU-94-04
Title:
Automatic Chunk Detection in Human-Computer Interaction
Authors:
Paulo J. Santos
Albert N. Badre
Abstract:
This paper describes an algorithm to detect users' 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 an 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.
Keywords:
Human-computer interaction, novice/expert differences, chunking, chunk
detection, models of the user, event logging, user study
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