Emily Barnes¹*, James Hutson², Karriem Perry³

¹Capitol Technology University, Maryland
²Lindenwood University, United States
³Capitol Technology University, Maryland

*Corresponding Author: Emily Barnes, Capitol Technology University, Maryland.

Received: June 03, 2024; Published: June 24, 2024

Abstract

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly enhance data-driven decision-making in educational settings. By highlighting the factors influencing adult learners' educational success, such as age and socioeconomic status, this research supports the strategic implementation of tailored educational policies and interventions, aimed at improving the retention and graduation rates of adult learners in higher education institutions.

Keywords: Machine Learning; Adult Learners; Educational Outcomes; Predictive Analytics; Degree Completion

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Citation

Citation: Emily Barnes., et al. “Predictive Power of Machine Learning Models on Degree Completion Among Adult Learners".Acta Scientific Computer Sciences 6.7 (2024): 79-96.

Copyright

Copyright: © 2024 Emily Barnes., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.