Unfolding Students' Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics

Kokoc M., AKÇAPINAR G., Hasnine M. N.

EDUCATIONAL TECHNOLOGY & SOCIETY, vol.24, no.1, pp.223-235, 2021 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 1
  • Publication Date: 2021
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, EBSCO Education Source, Education Abstracts, Educational research abstracts (ERA), ERIC (Education Resources Information Center), INSPEC, Psycinfo, Directory of Open Access Journals
  • Page Numbers: pp.223-235
  • Hacettepe University Affiliated: Yes


This study analyzed students' online assignment submission behaviors from the perspectives of temporal learning analytics. This study aimed to model the time-dependent changes in the assignment submission behavior of university students by employing various machine learning methods. Precisely, clustering, Markov Chains, and association rule mining analysis were used to analyze students' assignment submission behaviors in an online learning environment. The results revealed that students displayed similar patterns in terms of assignment submission behavior. Moreover, it was observed that students' assignment submission behavior did not change much across the semester. When these results are analyzed together with the students' academic performance at the end of the semester, it was observed that students' end-of-term academic performance can be predicted from their assignment submission behaviors at the beginning of the semester. Our results, within the scope of precision education, can be used to diagnose and predict students who are not going to submit the next assignments as the semester progresses as well as students who are going to fail at the end of the semester. Therefore, learning analytics interventions can be designed based on these results to prevent possible academic failures. Furthermore, the findings of the study are discussed considering the development of early-warning intervention systems for at-risk students and precision education.