Publication Details

Abstract

While Markov chains are widely used in business and industry, they are used within higher education only sporadically. Furthermore, when used to predict enrollment progression, most of these models use student level as the classification variable. This study uses grouped earned student credit hours to track the movement of students from one academic term to the other to better identify where students enter or leave the institution. Results from this study indicate a high level of predictability from one year to the next. In addition, the use of the credit hour flow matrix can aid administrators in identifying trends and anomalies within the institution’s enrollment management process.

Keywords: Markov chains, enrollment projections, enrollment management, enrollment trends, enrollment

Authors 

• Rex Gandy, Austin Peay State University
• Lynne Crosby, Austin Peay State University
• Andrew Luna, Austin Peay State University
• Daniel Kasper, Austin Peay State University
• Sherry Kendrick, Austin Peay State University

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