Abstract—Ubiquitous learning is a new educational
paradigm partly created by the affordance of digital media. This
trend has continued to expand over time. The emergence of
ubiquitous computing has created unique conditions for people
working as education professionals and learning as students.
Procrastination is one of the characteristics that has been seen in
students that forces them to set back and sit back without
achieving their goals. It has been estimated that almost 70% of
college students or even school students engage in frequent
academic procrastination and purposive delays in the beginning
or completing tasks. Throughout this study, we concentrated on
different predictive measures that can be used to identify
procrastination behaviour among students. These measures
include the usage of ensemble classification models such as
Logistic Regression, Stochastic Gradient Descent, K-Nearest
Neighbours, Decision Tree and Random Forest. Of these, the
random forest model achieved the best predictive outcome with
an accuracy of almost 85%. Moreover, earlier prediction of such
procrastination behaviours would assist tutors in classifying
students before completing any task or homework which is a
useful path for developing sustainability in the learning process.
A strength of this study is that the parameters discussed can be
well defined in both virtual and traditional learning
environments. However, the parameters defining students’
cognitive or emotional states were not explored in this study.
Index Terms—Procrastination, learning analytics, virtual learning environment, educational data mining, learning patterns.
The authors are with the School of Engineering, Cochin University of Science and Technology, Kerala, India.
Cite: Nisha S. Raj* and Renumol V. G., "An Approach for Early Prediction of Academic Procrastination in e-Learning Environment," International Journal of Information and Education Technology vol. 13, no. 1, pp. 73-81, 2023.Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).