Maximizing students' retention via spaced review: practical guidance from computational models of memory.
During each school semester, students face an onslaught of material to be learned. Students work hard to achieve initial mastery of the material, but when they move on, the newly learned facts, concepts, and skills degrade in memory. Although both students and educators appreciate that review can help stabilize learning, time constraints result in a trade-off between acquiring new knowledge and preserving old knowledge. To use time efficiently, when should review take place? Experimental studies have shown benefits to long-term retention with spaced study, but little practical advice is available to students and educators about the optimal spacing of study. The dearth of advice is due to the challenge of conducting experimental studies of learning in educational settings, especially where material is introduced in blocks over the time frame of a semester. In this study, we turn to two established models of memory-ACT-R and MCM-to conduct simulation studies exploring the impact of study schedule on long-term retention. Based on the premise of a fixed time each week to review, converging evidence from the two models suggests that an optimal review schedule obtains significant benefits over haphazard (suboptimal) review schedules. Furthermore, we identify two scheduling heuristics that obtain near optimal review performance: (a) review the material from μ-weeks back, and (b) review material whose predicted memory strength is closest to a particular threshold. The former has implications for classroom instruction and the latter for the design of digital tutors.
Department of Computer Science and Institute of Cognitive Science, University of Colorado.
Mohammad M
MM
Khajah
Robert V
RV
Lindsey
Michael C
MC
Mozer
D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology; D008960:Models, Psychological; D012153:Retention, Psychology / Q000502:physiology
ACT-R; Learning; MCM; Memory model; Optimization; Review; Spacing effect