Prediction of students’ procrastination behaviour through their submission behavioural pattern in online learning

Yeongwook Yang, Danial Hooshyar, Margus Pedaste, Minhong Wang, Yueh Min Huang, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Prediction of students' performance has been reported as a vital task which enables educators to take necessary actions to improve students’ learning. Numerous studies have concluded that students with lower procrastination tendencies archive more compared to those with higher procrastination tendencies. In this study, a new method is proposed to predict students’ procrastination tendencies discerned from their submission behavioural patterns in online learning. In this method, feature vectors signifying students’ submission patterns on homework are firstly drafted. Next, an ensemble clustering method is employed to optimally sort students into various categories of procrastination: procrastinator, procrastinator candidate, and non-procrastinator. Lastly, various classification methods are assessed to discern which one best predicts students’ procrastination tendencies. The efficacy of this approach is assessed through the data from a course comprised of 242 students at the University of Tartu in Estonia. Our study found that our method correctly identifies student procrastination from submission pattern data with 97% accuracy, and that the best performing classifier is linear support vector machine. Investigating the effect of different number of features (homework) on performance of clustering and classification methods indicate that finding the optimal number of feature to use in both clustering and classification methods is a vital task as it could potentially affect prediction power of our approach. More specifically, the results show that in our proposed approach, unlike clustering methods that show a better performance with lower number of features, classification methods mostly tend to show a better performance with larger number of features.

Original languageEnglish
JournalJournal of Ambient Intelligence and Humanized Computing
Publication statusAccepted/In press - 2020

Bibliographical note

Funding Information:
This research was partly supported by the European Regional Development Fund through the University of Tartu project ASTRA per ASPERA.

Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.


  • Educational data mining
  • Higher education
  • Online learning
  • Predication of students’ performance
  • Procrastination behaviour
  • Submission behavioural pattern

ASJC Scopus subject areas

  • General Computer Science


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