Making Sense of Student Success and Risk Through Unsupervised Machine Learning and Interactive Storytelling

Abstract

This paper presents an interactive AI system to enable academic advisors and program leadership to understand the patterns of behavior related to student success and risk using data collected from institutional databases. We have worked closely with advisors in our development of an innovative temporal model of student data, unsupervised k-means algorithm on the data, and interactive user experiences with the data. We report on the design and evaluation of FIRST, Finding Interesting stoRies about STudents, that provides an interactive experience in which the advisor can: select relevant student features to be included in a temporal model, interact with a visualization of unsupervised learning that present patterns of student behavior and their correlation with performance, and to view automatically generated stories about individual students based on student data in the temporal model. We have developed a high fidelity prototype of FIRST using 10 years of student data in our College. As part of our iterative design process, we performed a focus group study with six advisors following a demonstration of the prototype. Our focus group evaluation highlights the sensemaking value in the temporal model, the unsupervised clusters of the behavior of all students in a major, and the stories about individual students.