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Distribution of practice in motor learning and development

This is an excerpt from Motor Learning and Development-3rd Edition With HKPropel Access by Pamela S. Beach,Melanie Perreault,Ali Brian & Douglas H. Collier.

Is it better for learning to have fewer but longer practice sessions or more frequent but shorter practice sessions? Distribution of practice refers to the amount of practice within each period and the amount of rest between practice sessions to ensure optimal learning of motor skills (Magill & Anderson, 2013). This body of knowledge usually compares schedules of practice called massed and distributed. Massed practice involves longer practice sessions that involve many practice trials. This is contrasted to distributed practice, which has fewer practice trials in shorter practice sessions. Massed practice schedules have fewer practice sessions than distributed practice schedules. When the time between trials is a focus, massed practice has minimal or short rest periods, whereas distributed practice has longer rest intervals.

Early research (1930s to 1950s) on distributed practice investigated the length of the intertrial interval—that is, the rest time between trials (Magill & Anderson, 2013). Reviews of this literature point to the importance of the type of task when making this determination. Continuous skills are learned better with distributed schedules of practice than with massed schedules, whereas the reverse is true of discrete skills. Thus, swimming, dancing, and skiing would benefit from distributed practice, and hitting a golf ball or baseball would benefit from massed practice. Continuous tasks likely benefit from distributed practice because they can be physiologically fatiguing, whereas discrete tasks have naturally occurring breaks between trials.

Later research focused on examining the length of time between practice sessions, which has generally supported distributed practice. In a classic study, Baddeley and Longman (1978) found that postal workers who practiced a typing skill only once a day for one hour performed better than those who practice two times a day for one hour, one time a day for two hours, or two times a day for two hours. In medicine, there is a tradition of full-day training of surgical skills. Spruit and colleagues (2015) examined the effectiveness of this training by comparing the performance and retention of trainees who practiced for either three 75-minute sessions massed in a single day or three 75-minute sessions distributed over three weeks. The distributed practice was superior to massed practice in terms of skill acquisition as well as short- and long-term retention.

Little research has addressed the optimal number and length of practice sessions, but in general, researchers recommend more frequent and shorter sessions, as would occur in distributed practice (e.g., Kwon et al., 2015). More recently, Verhoeven and Newell (2018) proposed a new theoretical model that attempts to consolidate the many effects of practice distribution on performance to predict the optimal practice distribution. The model uses three assumptions (outlined next) to develop time scales that can be used to estimate between and within practice intervals.

1. The warm-up decrement (see chapter 6) is the result of a loss of set, which gets larger with an increase in the rest interval duration.
2. Fatigue, both physical and mental, has the strongest negative influence on performance in practice sessions and experimental designs with short rest intervals, but it can be alleviated by providing a rest break.
3. Learning a skill requires a rest interval that is long enough for the cellular and molecular mechanisms associated with memory formation to take place. However, if this rest interval is arbitrarily long, forgetting may occur. (p. 163)

Although distributed practice is advisable in many practice contexts, unfortunately, it is not always practical. Many educational, recreational, and rehabilitation situations have specified practice times, and practitioners have little flexibility in allotting practice time. For example, teachers know the number of days during the week that a given class will have physical education, and youth soccer teams have predetermined use of the practice fields. To complicate matters further, research has found that participants show a greater preference for massed practice (Son & Simon, 2012); thus, learner buy-in to a distributed practice schedule could be problematic to practitioners even when schedules allow for it.

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