  Response process data collected from human-computer interactive items contain
rich information about respondents' behavioral patterns and cognitive
processes. Their irregular formats as well as their large sizes make standard
statistical tools difficult to apply. This paper develops a computationally
efficient method for exploratory analysis of such process data. The new
approach segments a lengthy individual process into a sequence of short
subprocesses to achieve complexity reduction, easy clustering and meaningful
interpretation. Each subprocess is considered a subtask. The segmentation is
based on sequential action predictability using a parsimonious predictive model
combined with the Shannon entropy. Simulation studies are conducted to assess
performance of the new methods. We use the process data from PIAAC 2012 to
demonstrate how exploratory analysis of process data can be done with the new
approach.
