Improving Long-Term Retention Level in an Environment of Personalized Expanding Intervals.
Xiaolu Xiong, Joseph Barbosa Beck · Educational Data Mining · 2015
The ability to retain a skill long-term is one of the three indicators of robust learning. Researchers in Intelligent Tutoring Systems (ITS) and Educational Data Mining (EDM) have focused increasing attention on predicting students’ long-term retention performance as well as attempting to find effective methods to help improve student knowledge retention. But traditional practices of spacing and expanding retrieval practices have typically fixed their spacing intervals to one or few predefined schedules. In this work, we introduce the Personalized Adaptive Scheduling System (PASS) in ASSISTments’ retention and relearning workflow and we have evidence to show that the PASS is helping to improve students’ long-term retention performance.