Improving Students’ Long-Term Retention Performance in ARRS with Personalized Retention Schedules

Traditional practices of spacing and expanding retrieval practices have typically fixed their spacing intervals to one or few predefined schedules. Few have explored the advantages of using personalized expanding intervals and scheduling systems to adapt to the knowledge levels and learning patterns of individual students. In this work, we are concerned with estimating the effects of personalized expanding intervals on improving students’ long-term mastery level of skills. We developed a Personalized Adaptive Scheduling System (PASS) in ASSISTments’ retention and relearning workflow. After implementing the PASS, we conducted a study to investigate the impact of personalized scheduling on long-term retention by comparing results from 97 classes in the summer of 2013 and 2014. We compared to using the Automated Reassessment and Relearning system (ARRS), a system we have already built that spaces out practice. We observed that students in PASS outperformed students in traditional scheduling systems on long-term retention performance (p = 0.0002), and that in particular, students with medium level of knowledge demonstrated reliable improvement (p = 0.0209) with an effect size of 0.27. In addition, the data we gathered from this study also helped to expose a few issues we have with the new system. These results suggest personalized knowledge retrieval schedules are more effective than fixed schedules and we should continue our future work on examining approaches to optimize PASS.

Xiong, X., Wang, Y., & Beck, J. B. (2015, March). Improving students' long-term retention performance: a study on personalized retention schedules. InProceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 325-329). ACM.