1 Paper 4837-2020 SAS ® BI Platform to I mprove Use of Analytics in Higher Education: Do the Stats Match the Intent? Sean W. Mulvenon, University of Nevada, Las Vegas ABSTRACT A national trend for all university and college administrators is increased pressure from legislators and other education advocates to increase persistence and graduation rates of all students. Learning analytics (LA) models have been implemented nationwide with the use of Learning Management Systems (LMS) software to identify access patterns of students to support materials, identify those who might be performing below expectations, and to use this information to be more effective in providing educational support. A quick review of data structures and the common metrics employed revealed numerous statistical anomalies that might be problematic in use of LA and LMS in higher education. For example, one dubious metric identified was "duration", which was an aggregate of the time a student used to complete practice exams. If a student required 50 minutes to complete three practice exams, the information provided was 50 minutes and the final score on the last exam, with no information provided on the number of attempts. A student requiring 20 minutes to score 5/10, then 15 minutes to score of 7/10, and finally 5 minutes to obtain 9/10 is a very different pattern of progression, and is important in order to understand student persistence. This session presents an integrated data model using SAS ® Business Intelligence Platform to improve the accuracy and interpretation of analytics in order to improve student persistence and graduation rates in higher education. I NTRODUCTI ON Postsecondary institutions must endure consistent pressure to increase persistence and completion of students by parents, business, educational advocacy groups and legislators. The days of postsecondary institutions relying on the personal responsibility of college students to attend class, complete homework or seek assistance on their own are long gone. The new expectation is the postsecondary institution has a fiduciary responsibility to actively support and guide students to academic success. As such, postsecondary institutions are constantly seeking academic resources to proactively support their efforts with students to increase persistence and completion. Learning Management Systems (LMS) have become popular on postsecondary campuses as a method to identify and provide the necessary resources for students. A challenge with use of LMS at UNLV has been in identifying and understanding the definitions of many of the variables selected. Additionally, numerous labels employed in postsecondary education, such as persistence and completion , have nebulous interpretations and meanings. The use of LMS data and use of various labels to describe performance raises the question “What is really being measured?” and “What are you attempting to measure?” The answer to both of those questions, and to demonstrate the challenges of improving higher education analytics, is demonstrated through application/development of a model to improve persistence and completion at UNLV. The purpose of this paper is to present a proactive model referred to as the “Elevator Model” that demonstrates a “Data Lake to Dashboard” approach using SAS as a single source solution; and that actively resolves challenges associated with undefined or nebulous labels in higher education.