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Full-Text Articles in Education
Enhancing Adult Learner Success In Higher Education Through Decision Tree Models: A Machine Learning Approach, Emily Barnes, James Hutson, Karriem Perry
Enhancing Adult Learner Success In Higher Education Through Decision Tree Models: A Machine Learning Approach, Emily Barnes, James Hutson, Karriem Perry
Faculty Scholarship
This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key …
Strategic Integration Of Ai In Higher Education And Industry: The Ai8-Point Model, Emily Barnes, James Hutson
Strategic Integration Of Ai In Higher Education And Industry: The Ai8-Point Model, Emily Barnes, James Hutson
Faculty Scholarship
The AI8-Point Model, derived from extensive experience in technology, AI, and higher education administration, addresses the critical need for cost-effective, high-impact strategies tailored to higher education. Despite the transformative potential of AI in enhancing student engagement, optimizing processes, and improving educational outcomes, institutions often struggle with practical implementation. The AI8-Point Model fills this gap by offering strategies that balance cost and impact. Visualized as a circle divided into four quadrants, the model encompasses phases of student engagement and institutional interaction: pre-enrollment beyond institutional control, pre-enrollment within institutional control, post-enrollment within institutional control, and post-enrollment beyond institutional control. Each quadrant contains …
Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry
Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry
Faculty Scholarship
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 …
Optimizing Adult Learner Success: Applying Random Forest Classifier In Higher Education Predictive Analytics, Emily Barnes, James Hutson, Karriem Perry
Optimizing Adult Learner Success: Applying Random Forest Classifier In Higher Education Predictive Analytics, Emily Barnes, James Hutson, Karriem Perry
Faculty Scholarship
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 …
Ethical Imperatives And Challenges: Review Of The Use Of Machine Learning For Predictive Analytics In Higher Education, Emily Barnes, James Hutson, Karriem Perry
Ethical Imperatives And Challenges: Review Of The Use Of Machine Learning For Predictive Analytics In Higher Education, Emily Barnes, James Hutson, Karriem Perry
Faculty Scholarship
The escalating integration of machine learning (ML) in higher education necessitates a critical examination of its ethical implications. This article conducts a comprehensive review of the application of ML for predictive analytics within higher education institutions (HEIs), emphasizing the technology's potential to enhance student outcomes and operational efficiency. The study identifies significant ethical concerns, such as data privacy, informed consent, transparency, and accountability, that arise from the use of ML. Through a detailed analysis of current practices, this review underscores the need for HEIs to develop robust ethical frameworks and technological infrastructures to navigate these challenges effectively. The findings reveal …