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

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  • 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
    Shanghai Institute of Applied Mathematics and Mechanics, 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)

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.

Suggested Citation

  • Syed Muhammad Raza Abidi & Wu Zhang & Saqib Ali Haidery & Sanam Shahla Rizvi & Rabia Riaz & Hu Ding & Se Jin Kwon, 2020. "Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6074-:d:391160
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    References listed on IDEAS

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    1. Syed Muhammad Raza Abidi & Mushtaq Hussain & Yonglin Xu & Wu Zhang, 2018. "Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development," Sustainability, MDPI, vol. 11(1), pages 1-21, December.
    2. Tran, Bach Xuan & Nguyen, Long Hoang & Vu, Giang Thu & Le, Huong Thi & Nguyen, Hinh Duc & Hoang, Vuong Quan & La, Phuong Viet & Hoang, Duc Anh & Van Dam, Nhue & Vuong, Thu Trang & Nguyen, Huong Lan Th, 2019. "Online peer influences are associated with receptiveness of youths: The case of Shisha in Vietnam," Children and Youth Services Review, Elsevier, vol. 99(C), pages 18-22.
    3. Eric P. Bettinger & Lindsay Fox & Susanna Loeb & Eric S. Taylor, 2017. "Virtual Classrooms: How Online College Courses Affect Student Success," American Economic Review, American Economic Association, vol. 107(9), pages 2855-2875, September.
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    1. Jingran Zhang & Feiyue Qiu & Wei Wu & Jiayue Wang & Rongqiang Li & Mujie Guan & Jiang Huang, 2023. "E-Learning Behavior Categories and Influencing Factors of STEM Courses: A Case Study of the Open University Learning Analysis Dataset (OULAD)," Sustainability, MDPI, vol. 15(10), pages 1-22, May.

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