Version 1
: Received: 17 August 2024 / Approved: 19 August 2024 / Online: 20 August 2024 (12:39:10 CEST)
How to cite:
Kok, C. L.; Ho, C. K.; Chen, L.; Koh, Y. Y.; Tian, B. A Novel Predictive Modeling for Student Attrition by Utilizing Machine Learning and Sustainable Big Data Analytics. Preprints2024, 2024081298. https://doi.org/10.20944/preprints202408.1298.v1
Kok, C. L.; Ho, C. K.; Chen, L.; Koh, Y. Y.; Tian, B. A Novel Predictive Modeling for Student Attrition by Utilizing Machine Learning and Sustainable Big Data Analytics. Preprints 2024, 2024081298. https://doi.org/10.20944/preprints202408.1298.v1
Kok, C. L.; Ho, C. K.; Chen, L.; Koh, Y. Y.; Tian, B. A Novel Predictive Modeling for Student Attrition by Utilizing Machine Learning and Sustainable Big Data Analytics. Preprints2024, 2024081298. https://doi.org/10.20944/preprints202408.1298.v1
APA Style
Kok, C. L., Ho, C. K., Chen, L., Koh, Y. Y., & Tian, B. (2024). A Novel Predictive Modeling for Student Attrition by Utilizing Machine Learning and Sustainable Big Data Analytics. Preprints. https://doi.org/10.20944/preprints202408.1298.v1
Chicago/Turabian Style
Kok, C. L., Yit Yan Koh and Bowen Tian. 2024 "A Novel Predictive Modeling for Student Attrition by Utilizing Machine Learning and Sustainable Big Data Analytics" Preprints. https://doi.org/10.20944/preprints202408.1298.v1
Abstract
Student attrition poses significant societal and economic challenges, leading to unemployment, lower earnings, and other adverse outcomes for individuals and communities. To address this, predictive systems leveraging machine learning and Big Data aim to identify at-risk students early and intervene effectively. This project focuses on extracting key parameters from past dropout data to construct a predictive model and alert authorities to intervene promptly. Two preliminary trials refine machine learning models, establish evaluation standards, and optimize hyperparameters. These trials facilitate systematic exploration of model performance and data quality assessment. Achieving 100% accuracy in dropout prediction, the study identifies academic performance as the primary influencer, with early-year subjects like Mechanics and Materials, Design of Machine Elements, and Instrumentation and Control having significant impact. The longitudinal effect of these subjects on attrition underscores the importance of early intervention. Proposed solutions include early engagement and support or restructuring courses to better accommodate novice learners, aiming to reduce attrition rates.
Keywords
machine learning; big data; attrition rate
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
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.
