Exploring the Procrastination of College Students: A Data-Driven Behavioral Perspective

  • Yan Zhu
  • Hengshu Zhu
  • Qi Liu
  • Enhong ChenEmail author
  • Hong Li
  • Hongke Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9642)


Procrastination refers to the practice of putting off impending tasks due to the habitual carelessness or laziness. The understanding of procrastination plays an important role in educational psychology, which can help track and evaluate the comprehensive quality of students. However, traditional methods for procrastination analysis largely rely on the knowledge and experiences from domain experts. Fortunately, with the rapid development of college information systems, a large amount of student behavior records are captured, which enables us to analyze the behaviors of students in a quantitative way. To this end, in this paper, we provide a data-driven study from a behavioral perspective to understand the procrastination of college students. Specifically, we propose an unsupervised approach to quantitatively estimate the procrastination level of students by the analysis of their borrowing records in library. Along this line, we first propose a naive Reading-Procrastination (naive RP) model, which considers the behavioral similarity between students for procrastination discovery. Furthermore, to improve the discovery performance, we develop a dynamic Reading-Procrastination (dynamic RP) model by integrating more comprehensive characteristics of student behaviors, such as semester-awareness and month-regularity. Finally, we conduct extensive experiments on several real-world data sets. The experimental results clearly demonstrate the effectiveness of our approach, and verify several key findings from psychological fields.


Root Mean Square Error Hold Time Procrastination Behavior Educational Data Mining Academic Procrastination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was partially supported by grants from the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), the Science and Technology Program for Public Wellbeing (Grant No. 2013GS340302). Qi Liu gratefully acknowledges the support of the Youth Innovation Promotion Association of CAS and acknowledges the support of the CCF-Intel Young Faculty Researcher Program (YFRP).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yan Zhu
    • 1
  • Hengshu Zhu
    • 2
  • Qi Liu
    • 1
  • Enhong Chen
    • 1
    Email author
  • Hong Li
    • 3
  • Hongke Zhao
    • 1
  1. 1.University of Science and Technology of ChinaHefeiChina
  2. 2.Baidu Research-Big Data LabBeijingChina
  3. 3.Hefei UniversityHefeiChina

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