Subtask analysis of process data through a predictive model |
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Authors: | Zhi Wang Xueying Tang Jingchen Liu Zhiliang Ying |
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Affiliation: | 1. Department of Statistics, Columbia University, New York City, NY, USA;2. Department of Mathematics, University of Arizona, Tucson, Arizona, USA |
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Abstract: | 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. |
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Keywords: | action prediction entropy process data sequence segmentation |
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