Examining the Impact of Analytics-Informed Academic Advising on First-Year, First-Term Engineering Students

• In Event: Poster Session 4
  In Poster Session: Quantitative Studies of Data Use

Abstract

This study investigated the impact of predictive analytics on student behavior and academic decision-making. Specifically, we examined the use of predictive analytics by academic advisers among first-year, first-term undergraduate students majoring in engineering and its effect on change of major. Data from multiple institutional data sources were utilized. Logistic regressions were conducted to determine influential characteristics. A propensity score analysis was conducted to examine the effect of analytics informed advising, revealing that students receiving analytics informed advising were significantly more likely to change majors. This study contributes to the base of knowledge on how the use of predictive analytics effects higher education students and provides scholars with motivation to further define the role and application of analytics in higher education.

Authors

• Sylvester C Upah, Iowa State University

• Yu Chen, Louisiana State University