Understand and Assess People’s Procrastination by Mining Computer Usage Log
Although the computer and Internet largely improve the convenience of life, they also result in various problems to our work, such as procrastination. Especially, today’s easy access to Internet makes procrastination more pervasive for many people. However, how to accurately assess user procrastination is a challenging problem. Traditional approaches are mainly based on questionnaires, where a list of questions are often created by experts and presented to users to answer. But these approaches are often inaccurate, costly and time-consuming, and thus can not work well for a large number of ordinary people. In this paper, to the best of our knowledge, we are the first to propose to understand and assess people’s procrastination by mining user’s behavioral log on computer. Specifically, as the user’s behavior log is time-series, we first propose a simple procrastination identification model based on the Markov Chain to assess user procrastination. While the simple model can not directly depict reasons of user procrastination, we extract some features from computer logs, which successfully bridge the gap between user behaviors on computer and psychological theories. Based on the extracted features, we design a more sophisticated model, which can accurately identify user procrastination and reveal factors that may cause user’s procrastination. The revealed factors could be used to further develop programs to mitigate user’s procrastination. To validate the effectiveness of our model, we conduct experiments on a real-world dataset and procrastination questionnaires with 115 volunteers. The results are consistent with psychological findings and validate the effectiveness of the proposed model. We believe this work could provide valuable insights for researchers to further exploring procrastination.
This research was partially supported by grants from the National Natural Science Foundation of China (Grants No. 61727809, U1605251, 61672483, 61602234 and 61572032), and the Science Foundation of Ministry of Education of China & China Mobile (No. MCM20170507).
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