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International Journal of Multidisciplinary and Current Educational Research


This study examines the application of the Random Forest Classifier (RF) model in predicting academic success among adult learners in higher education. It focuses on evaluating the model's effectiveness using key statistical measures like accuracy, precision, recall, and F1 score across a comprehensive dataset from 2013–14 to 2021–22, which includes variables such as age, ethnicity, gender, Pell Grant eligibility, and academic performance metrics. The research highlights the RF model's capability to handle large datasets with varying data types and demonstrates its superiority over traditional regression models in predictive accuracy. Through an iterative process, the study refines the RF model to better predict educational outcomes and explores the significant predictors of academic success among adult learners. Age, attendance, and financial aid availability (Pell Grant eligibility) emerge as critical factors influencing graduation rates. The study emphasizes the need for educational institutions to leverage machine learning to develop more personalized, data-driven strategies that address the unique needs of adult learners. It proposes future research directions to further explore the impacts of socio-demographic factors on student success and to expand the application of machine learning in educational policy and practice. This research contributes to the broader discourse on enhancing adult education through advanced analytical techniques and offers insights into optimizing educational strategies to support a diverse student population.

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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.