Student satisfaction plays an important role in determining the quality,
retention, and reputation of an institution. However, limited survey
data can reduce the accuracy of predictive models. This study explores
how Genetic Algorithm based data augmentation can improve dataset
reliability and enhance analysis using LASSO and Ordinal Regression.
By generating synthetic responses, GA expands the dataset while
maintaining statistical accuracy, leading to better feature selection and
ranking. LASSO Regression identified key factors influencing student
satisfaction, such as career services, curriculum relevance, faculty
support, and extracurricular activities, while Ordinal Regression
pointed out that administrative inefficiencies negatively affect
satisfaction levels. The results highlight that academic and career-
related aspects have a greater impact on student satisfaction than
infrastructure facilities. To enhance student experiences, institutions
should focus on faculty mentorship, career counseling, and aligning
the curriculum with industry needs. This study shows that GA-based
data augmentation can significantly improve predictive modeling for
student satisfaction analysis and offers practical recommendations for
institutional development. Future research can incorporate machine
learning techniques for more accurate predictions and tailored
strategies to improve student success.
P. Priyadarshini, K.T. Veeramanju
Srinivas University, India
Student Satisfaction, Genetic Algorithm, Ordinal Regression, LASSO Regression
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 16 , Issue: 1 , Pages: 3769 - 3777 )
Date of Publication :
April 2025
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