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

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.

Self-Directed Learning Online: An Opportunity to Binge

“…For instance, researchers have looked at making an assignment due each day (Dawar & Murphy, 2020), and some have looked at having students schedule their online lectures (Baker et al, 2019). Other researchers have begun using data analytics to identify potential procrastinators so that they can intervene early (Abidi et al, 2020). Our research suggests that encouraging early access and use of the online material, even if binged, can result in greater learning and satisfaction.…”

SA-FEM: Combined Feature Selection and Feature Fusion for Students’ Performance Prediction

“…The algorithm achieved excellent results in numerous datasets. In-class prediction often aims to allow teachers and students to correct promptly, and Syed et al [20] proposed a gradient-enhanced automatic optimization model to identify and predict students' procrastination behavior. The model allows teachers to monitor and correct student behavior instantly.…”

Prediction of Dilatory Behavior in eLearning: A Comparison of Multiple Machine Learning Models

“…In contrast, models that explicitly try to predict delay as the outcome variable are rather scarce. Exceptions include studies conducted in [12], [32], [33], and [34], who all intended to classify students based on homework submission data. Ten different ML algorithms (ZeroR, OneR, ID3, J48, Random Forest, decision stump, JRip, PART, NBTree, and Prism) were implemented in [12] to classify students as procrastinators or non-procrastinators based on feature vectors.…”

“…Binary classifiers were employed in [33], using four supervised-learning algorithms (logistic regression, decision tree, gradient boosting machine, and Random Forest) to classify students as procrastinators or non-procrastinators based on data that was extracted from an intelligent tutoring system (ITS). Among the ML algorithms, gradient boosting had the best performance in terms of classification precision.…”