Higher education institutions have access to a vast amount of student data that is recorded during the process of admissions and throughout the course of the program. After the outbreak of the COVID 19 pandemic most universities around the world had to either adopt a blended or a complete online mode of teaching. Students were required to access resources through a virtual learning platform and interaction with this environment also left an important trail of valuable information. Data analysis and machine learning can provide insights and predictions about students’ performances, online activities and academic progress. Advanced analytics can provide necessary tools to decision makers to analyze and interpret the data appropriately and make informed decisions that improve the student academic outcomes and experience. The latest machine learning tools will not only provide insights that could be utilized to improve the quality of teaching and student performance but also build predictive models that could guide future decisions. Advanced Analytics can predict the future academic success of the students and completion of courses with a very high degree of accuracy. The higher education institutions will be able to leverage these tools to provide insights for other business decisions as well. In this capstone project I aim to explore data from a virtual learning environment and derive valuable conclusions through data exploration and build a predictive model by using advance analytics tools.
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Dsouza, Preethi, "Using Advanced Analytics to Assist Stakeholders in Higher Education Institutions" (2021). Thesis. Rochester Institute of Technology. Accessed from