NDLI: Improving students' long-term retention performance: a study on personalized retention schedules

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Improving students' long-term retention performance: a study on personalized retention schedules

Content Provider	ACM Digital Library
Author	Xiong, Xiaolu Beck, Joseph Barbosa Wang, Yan
Abstract	Traditional practices of spacing and expanding retrieval practices have typically fixed their spacing intervals to one or few predefined schedules [5, 7]. 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 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.
Starting Page	325
Ending Page	329
Page Count	5
File Format	PDF
ISBN	9781450334174
DOI	10.1145/2723576.2723636
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Personalization Retrieval practice Spacing effect Intelligent tutoring system Knowledge retention
Content Type	Text
Resource Type	Article

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