Education Commons^™

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

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 …

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 …

Bridging The Divide: Improving Digital Humanities Pedagogy By Networking Higher Education And Secondary Education Faculty In St. Louis, Geremy Carnes, Margaret K. Smith

Faculty Scholarship

In 2021, faculty at Lindenwood University and Southern Illinois University Edwardsville (SIUE) led the formation of a Saint Louis Digital Humanities (STL DH) Network of faculty and scholars at area universities, schools, and cultural institutions.1 The Lindenwood and SIUE campuses bookend the St. Louis metro area, a region whose strong geospatial presence offers fruitful opportunities for digital humanities (DH) education but which also suffers from long, deeply ingrained economic and racial segregation. While other regional DH networks exist, the STL DH Network is unique in taking undergraduate education and secondary education— and particularly equitable access to education—as its chief focus. …

College Major Area And Career Commitment: Rethinking Steam In Educational- Vocational Guidance, James Hutson, Sara Bagley, Colleen Biri

Faculty Scholarship

Objective: The exigency for higher education to exhibit outcomes aligning with career competencies has intensified, driven by external pressures favoring job-specific training. Amidst shifting career tendencies of Generations Y and Z and the advent of artificial intelligence-led automation, the valuation of different college majors has come under scrutiny. This study aims to dissect the prevailing assumptions and explore the satisfaction and career commitment levels among individuals with career-focused degrees.

Methods: Utilizing a mixed-methods approach, the study delves into the relationships among areas of study, career commitment, self-esteem, and self-efficacy among alumni from a private Midwestern liberal arts college. Instruments …

Study Abroad - Your Future Self Will Thank You, Stephanie Afful, Rebecca Foushée, Colleen Biri

Faculty Scholarship

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