In studies that generate course recommendations based on similarity, the typical enrollment data used for model training consists only of one record per student-course pair. In this study, we explore and quantify the additional signal present in course transaction data, which includes a more granular account of student administrative interactions…

A Machine Learning framework for predicting enrollment is proposed. The framework consists of Amazon Web Services SageMaker together with standard Python tools for data analytics, including Pandas, NumPy, MatPlotLib, and ScikitLearn. The tools are deployed with Jupyter Notebooks running on AWS SageMaker. Based on three years of enrollment history,…

Purpose: The engineering courses offered in Somali universities attract many students, ranging between 300 and 500 every semester, making the management and delivery of the course challenging. The increasing popularity of massive open online courses (MOOCs) has led to rapid growth in enrollment, posing difficulties in effectively managing and…

Learning analytics have proved promising capabilities and opportunities to many aspects of academic research and higher education studies. Data-driven insights can significantly contribute to provide solutions for curbing costs and improving education quality. This paper adopts a two-phase machine learning approach, which utilizes both…

This dissertation analyzes the ways that college applicants and college students navigate through higher education with a focus on ways to improve their experience. In the first chapter, I examine the ways that college applicants change their application and enrollment decisions when using the Common Application. In the second chapter, I highlight…