Using Predictive Analytics in Teaching and Advising: Promising Practices and Practical Considerations

Abstract

Higher education institutions are facing increasing pressure to provide evidence of student learning; teaching pedagogical best practices are moving to an increasingly individualized and student-focused learning model; and innovative technologies are allowing for greater mining of student data. Within this environment, learning and advising management systems, based on educational big data, or learning analytics, are being developed to better measure, analyze, report and predict data related to student learning, retention and completion. These learning analytics-informed systems have the potential to generate new insight into courses and student learning by creating responsive feedback mechanisms that can shape data-informed decision making as it relates to teaching, learning and advising. Despite the benefits of using learning analytics-informed learning and advising management systems, there are barriers and challenges related to broad adoption and use of innovations like these systems in higher education.

In the summer of 2015, we conducted an instrumental case study to learn more about the technological, institutional, and individual factors related to adoption and use of learning analytics-informed educational technology tools on campus.à In this short lightning talk, we will present the findings from the studyââ¬â¢s focus group interviews and observations.à Further, we will share promising practices and practical considerations related to use of learning analytics tools to measure and predict academic performance.