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
AUTHORS (5)
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.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (0)
CITATIONS (3)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....