9:50am Room A - Subtask Analysis of Process Data Through a Predictive Model

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 case study of PIAAC 2012 to demonstrate how exploratory analysis for process data can be done with the new approach.


Dr. Xueying Tang

Assistant Professor
Department of Mathematics
University of Arizona
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I got my PhD in Statistics from the University of Florida. Prior joining UA, I was a Postdoctoral Research Scientist at Columbia University. My research interest in high dimensional Bayesian statistics and analyzing large and complex data from education and psychology.