TY - GEN
T1 - Understand and assess people’s procrastination by mining computer usage log
AU - He, Ming
AU - Chen, Yan
AU - Liu, Qi
AU - Ge, Yong
AU - Chen, Enhong
AU - Liu, Guiquan
AU - Liu, Lichao
AU - Li, Xin
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85052234359&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052234359&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-99365-2_17
DO - 10.1007/978-3-319-99365-2_17
M3 - Conference contribution
AN - SCOPUS:85052234359
SN - 9783319993645
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 187
EP - 199
BT - Knowledge Science, Engineering and Management - 11th International Conference, KSEM 2018, Proceedings
A2 - Liu, Weiru
A2 - Yang, Bo
A2 - Giunchiglia, Fausto
PB - Springer-Verlag
T2 - 11th International Conference on Knowledge Science, Engineering and Management, KSEM 2018
Y2 - 17 August 2018 through 19 August 2018
ER -