Document Type
Article
Publication Title
ACTA Scientific Computer Sciences
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.
Publication Date
6-2024
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Barnes, Emily; Hutson, James; and Perry, Karriem, "Predictive Power of Machine Learning Models on Degree Completion Among Adult Learners" (2024). Faculty Scholarship. 652.
https://digitalcommons.lindenwood.edu/faculty-research-papers/652