Incorporating an LMS Learning Analytic into Proactive Advising: Validity and Use in a Randomized Experiment

This paper presents two studies examining the effectiveness of using learning analytics to inform targeted, proactive advising interventions aimed at improving student success. Study 1 validates a simple learning management system (LMS) learning analytic as predictive of end-of-term outcomes and persistence. Results suggest that this analytic measure, based on students’ activity in the LMS, has predictive utility for identifying students who might benefit from a proactive advising intervention. In Study 2, a randomized experiment with 458 undergraduate pre-major students, we test the hypothesis that an LMS-informed proactive advising intervention would improve end-of-term outcomes and persistence. Students in the treatment group exhibited, on average, an increase of nearly one-third of a grade point in their term GPAs, a reduction in DFWs earned, and an 80% higher likelihood of persisting compared to the control group. These findings provide strong evidence for the effectiveness of proactive advising interventions, where advisors’ efforts are targeted using learning analytics. They suggest that by transparently providing advisors with comprehensible insights, institutions might improve student outcomes and promote the use of data-informed interventions in academic advising.