Abstract
The test-optional movement in higher education generates the need for creative solutions in academic advising. As a first step in developing an artificial intelligence-based academic advising system, the results of a longitudinal study are used to measure the learning gain of four essential skills (writing, reading, mathematics, and critical thinking). Association rules are generated to a) find the sequence of general education courses that are taken in sequence to generate learning gains and b) identify other factors that may be used to replace or supplement standardized test scores in course placement decisions. The practical implications of the results indicate a sequence of general education courses that support essential skills and provide the first steps in building an AI-academic advising system.
Recommended Citation
Sandouka, Kari, "Using Association Rules to Inform Academic Advising: The First Steps to an AI-Academic Advising System" (2023). MWAIS 2023 Proceedings. 15.
https://aisel.aisnet.org/mwais2023/15