Subtask analysis of process data through a predictive model.

Response process data collected from human–computer interactive items contain detailed information about respondents' behavioural patterns and cognitive processes. Such data are valuable sources for analysing respondents' problem‐solving strategies. However, the irregular data format and the complex structure make standard statistical tools difficult to apply. This article 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 the performance of the new method. We use a case study of PIAAC 2012 to demonstrate how exploratory analysis for process data can be carried out with the new approach. [ABSTRACT FROM AUTHOR]