Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers

Syed Muhammad Raza Abidi, Wu Zhang, Saqib Ali Haidery, Sanam Shahla Rizvi, Rabia Riaz, Hu Ding and Se Jin Kwon
Additional contact information
Syed Muhammad Raza Abidi: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Wu Zhang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Saqib Ali Haidery: School of Communications and Information Engineering, Shanghai University, Shanghai 200444, China
Sanam Shahla Rizvi: Raptor Interactive (Pty) Ltd., Eco Boulevard, Witch Hazel Ave, Centurion 0157, South Africa
Rabia Riaz: Department of CS IT, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
Hu Ding: Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai 200444, China
Se Jin Kwon: Department of Computer Engineering, Kangwon National University, Samcheok 25806, Korea

Sustainability, 2020, vol. 12, issue 15, 1-23

Abstract: Ubiquitous online learning is continuing to expand, and the factors affecting success and educational sustainability need to be quantified. Procrastination is one of the compelling characteristics that students observe as a failure to achieve the weaker outcomes. Past studies have mainly assessed the behaviors of procrastination by describing explanatory work. Throughout this research, we concentrate on predictive measures to identify and forecast procrastinator students by using ensemble machine learning models (i.e., Logistic Regression, Decision Tree, Gradient Boosting, and Forest). Our results indicate that the Gradient Boosting autotuned is a predictive champion model of high precision compared to the other default and hyper-parameterized tuned models in the pipeline. The accuracy we enumerated for the VALIDATION partition dataset is 91.77 percent, based on the Kolmogorov–Smirnov statistics. Additionally, our model allows teachers to monitor each procrastinator student who interacts with the web-based e-learning platform and take corrective action on the next day of the class. The earlier prediction of such procrastination behaviors would assist teachers in classifying students before completing the task, homework, or mastery of a skill, which is useful and a path to developing a sustainable atmosphere for education or education for sustainable development.

Keywords: academic procrastination; tree ensemble classifiers; machine learning; education for sustainable development; intelligent tutoring system; SAS Visual data mining and machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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