Optimizing Long-Term Retention of Abstract Learning

In a distributed learning strategy, study time is spread across multiple study sessions, without increasing total study time. The benefits of distributed practice, also known as spaced practice, on learning of rote-memory tasks (e.g., spelling, addition, and cued-recall of word pairs) are well known. However, few researchers have looked at the effects of distributed practice on the learning of abstract materials (e.g., physics problems, logical deductions, and algebra). We examined the effects of distributed practice on learning the abstract task of matrix multiplication. In Experiment 1, we taught participants matrix multiplication in either a massed (i.e., 0-day interstudy gap), or distributed (i.e., 7-day interstudy gap) format and tested students at 2 or 21 days after completion of the last study session. Results showed no significant differences between the massed and spaced groups. However, when only those participants scoring 80% or greater on study session one were included in the analyses, a benefit of spacing was seen at the 21-day retention interval. Although not statistically significant, this leads us to believe that spacing does have benefits for abstract learning when the task is mastered initially. Experiment 2 looked at overlearning as another learning strategy. In overlearning, all study takes place in one session, but participants continue to study after mastery of material has been achieved. It is commonly accepted that overlearning is a beneficial strategy, but it is unknown whether the benefits are worth the time invested. We assessed the effects of two levels of massed practice to gauge the benefits of overlearning on long-term retention. Participants completed either 2 or 8 matrix multiplication problems (i.e., low or high massing, respectively) and were tested 1 or 4 weeks after the study session. Results showed a benefit of high massing when analyses included participants who mastered the material (i.e., scored over 50%) during the study session. However, this higher degree of learning was not particularly efficient, because this latter result suggests that overlearning may not be worth the time invested