Using an Adaptive Learning Tool to Improve Student Performance and Engagement in a University Course

Abstract-College courses are haunted by participation and grade problems, particularly in large or online classes. This experiment investigates the impact of an adaptive learning (AL) system, CogBooks®, on student achievement in a blended statistics course. We employed a quasi-experimental experiment with two groups: an AL system (N=100) group and a group of students instructed using traditional techniques (N=100). Partic- ipation was measured with validated surveys, and performance was measured with standardized grades. Results indicated a statistically significant difference in the AL group, a 15 percent boost in average grades (p < 0.05) and a 20 percent boost in reported participation measures compared to the control group. The system’s real-time feedback and individually tailored learning paths efficiently addressed individual students’ needs. Surprisingly, our solution is offline-capable, offering accessibility in low-bandwidth settings—a major benefit compared to cloud- capable solutions. These findings present AL as a cost-effective, scalable solution to higher education, with potential applications to STEM and humanities courses. Long-term retention effects and compatibility with generative AI tools might be topics for future research.