As part of a Title III federal grant awarded to Bay Path University, the institution has planned to create a predictive model for traditional undergraduate persistence and another for financial aid warning flags. This is final installment in a series of posts that follow the steps in the CRISP-DM framework on our journey to produce the former.
This study aims at providing explanations of students’ behaviors on LMS by incorporating dispositional dimensions (e.g., self-regulation and emotions) into conventional learning analytics models. Using a combination of demographic, trace, and self-reported data.
Should students be told what the data predict about their chances of success? Corporate leaders in predictive analytics business consider the issue posed by an Inside Higher Ed blogger.
Data and analytics are at the center of today’s student success movement. Increased interest and access to institutional data are helping colleges and universities identify systematic and structural barriers to retention and graduation. Predictive analytics is making proactive advising possible at scale.
W. Li, C. Brooks, and F. Schaub. Proceedings of the 9th International Conference on Learning Analytics & Knowledge, page 411--420. New York, NY, USA, ACM, (2019)