Manuscript received December 6, 2022; revised December 16, 2022; accepted January 28, 2023.
Abstract—One of the biggest challenges in higher
educational institutions is to avoid students’ failures. Globally
student dropout is a serious issue. Risk of dropouts can be
identified at an earlier stage using machine learning classifiers,
as they have gained more popularity in both academia and
industry. The research team suggests that early prediction
facilitates educators and higher education administrators to
take necessary measures to prevent dropouts. Data for the
research were collected from 530 Indian students when they
were engaged in online learning during pandemic crisis. This
research work involves two phases. In first phase, hybrid
ensemble strategy is focused that integrates two powerful
machine learning algorithms namely Random Forest (RF) and
eXtreme Gradient Boosting (XGBoost) for early at-risk
prediction. The result is a fast procedure for classification of
at-risk students which is competitive in accuracy and highly
robust. Prediction models are developed using ensemble
learning, furthermore ensemble models are combined into a
single meta-model, which provides best outcomes to enable
higher education institutions for predictive analysis. Moreover,
it correctly classified students’ at-risk regarding accuracy,
precision, recall and F1-score with values of 93%, 91.52%,
96.42% and 93.91% respectively. In second phase, prediction
model is deployed by creating a web application using. Net
framework to sense students’ sentiments using Azure cognitive
services text analytics (Application Programming Interface)
API for detecting cognitive behavioral outcomes in online
learning environment.
Index Terms—At-risk, cognitive services, ensemble, machine
learning, online learning environment
Ananthi Claral Mary.T and Arul Leena Rose. P. J are with the
Department of Computer Science, College of Science and Humanities, SRM
Institute of Science and Technology, Chengalpattu, India.
*Correspondence: leena.rose527@gmail.com (A.L.R.P.J.)
Cite: Ananthi Claral Mary.T and Arul Leena Rose.P. J*, "Ensemble Machine Learning Model for University Students’ Risk Prediction and Assessment of Cognitive Learning Outcomes," International Journal of Information and Education Technology vol. 13, no. 6, pp. 948-958, 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).