Improving undergraduate student achievement in large blended courses through data-driven interventions

This pilot study applied Learning Analytics methods to identify students at-risk of not succeeding in two high enrollment courses with historically low pass rates at San Diego State University: PSY 101 and STAT 119. With input from instructors, targeted interventions were developed and sent to participating students (n=882) suggesting ways to improve their performance. An experimental design was used with half of the students randomly assigned to receive these interventions via email and the other half being analyzed for at-risk triggers but receiving no intervention. Pre-course surveys on student motivation [4] and prior subject matter knowledge were conducted, and students were asked to maintain weekly logs of their activity online and offline connected to the courses. Regression analyses, incorporating feature selection methods to account for student demographic data, were used to compare the impact of the interventions between the control and experimental groups. Results showed that the interventions were associated with a higher final grade in one course, but only for a particular demographic group.