A Novel Predictive Modeling for Student Attrition by Utilizing Machine Learning and Sustainable Big Data Analytics

Technology student 330 QH301-705.5 T Physics QC1-999 Engineering (General). Civil engineering (General) Sustainable Development Goal Chemistry machine learning attrition rate big data TA1-2040 Biology (General) QD1-999 SDG 4
DOI: 10.20944/preprints202408.1298.v1 Publication Date: 2024-08-22T00:30:12Z
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.
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