A significant amount of educational data mining (EDM) research consider students’ past performance or non-academic factors to build predictive models, paying less attention to students’ activity data. While procrastination has been found as a crucial indicator which negatively affects performance of students, no research has investigated this underlying factor in predicting achievement of students in online courses. In this study, we aim to predict students’ course achievement in Moodle through their procrastination behaviour using their homework submission data. We first build feature vectors of students’ procrastination tendencies by considering their active, inactive, and spare time for homework, along with homework grades. Accordingly, we then use clustering and classification methods to optimally sort and put students into various categories of course achievement. We use a Moodle course from the University of Tartu in Estonia which includes 242 students to assess the efficacy of our proposed approach. Our findings show that our approach successfully predicts course achievement for students through their procrastination behaviour with precision and accuracy of 87% and 84% with L-SVM outperforming other classification methods. Furthermore, we found that students who procrastinate more are less successful and are potentially going to do poorly in a course, leading to lower achievement in courses. Finally, our results show that it is viable to use a less complex approach that is easy to implement, interpret, and use by practitioners to predict students’ course achievement with a high accuracy, and possibly take remedial actions in the semester.
Bibliographical noteFunding Information:
This research was partly supported by the European Regional Development Fund through the University of Tartu project ASTRA per ASPERA; the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01405) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation); Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques); Eastern Scholar Chair Professorship Fund from Shanghai Municipal Education Commission of China (No. JZ2017005); National Natural Science Foundation of China (No. 61977023).
- Educational data mining
- Higher education
- Online learning
- Predication of course achievement
- Prediction of grade
- Procrastination behavior
ASJC Scopus subject areas
- Theoretical Computer Science
- Geometry and Topology