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.

预测学生的表现是一项至关重要的任务，它使教育工作者能够采取必要的行动来改善学生的学习。大量研究得出的结论是，与那些拖延倾向较高的学生相比，拖延倾向较低的学生归档的更多。在这项研究中，提出了一种新方法来预测学生的拖延倾向，从他们在在线学习中的服从行为模式可以看出。在这种方法中，首先绘制了表示学生作业方式的特征向量。接下来，采用集成聚类方法将学生最佳地分为拖延的各种类别：拖延者，拖延者候选人和非拖延者。最后，评估了各种分类方法以辨别哪种方法可以最好地预测学生的拖延倾向。该方法的有效性通过爱沙尼亚塔尔图大学的242名学生的课程数据进行评估。我们的研究发现，我们的方法可以从提交模式数据中正确地识别出学生拖延症，准确率达到97％，并且表现最好的分类器是线性支持向量机。调查不同数量的特征（作业）对聚类和分类方法的性能的影响表明，找到在聚类和分类方法中使用的最佳特征数是一项至关重要的任务，因为这可能会影响我们方法的预测能力。更具体地说，结果表明，在我们提出的方法中，