Distributed Practice
Distributed practice is a learning technique where study or practice sessions are spaced out over time, rather than concentrated in a single block (massed practice). It leverages the psychological spacing effect to improve long-term retention and skill acquisition by allowing time for memory consolidation between sessions. This approach is widely used in education, training, and skill development to enhance learning efficiency and durability.
Developers should use distributed practice when learning new programming languages, frameworks, or complex concepts to improve retention and mastery over time, such as when preparing for certifications or building expertise in a new technology stack. It is particularly effective for long-term projects or continuous learning goals, as it reduces cognitive overload and prevents burnout compared to cramming. For example, spacing out study sessions for a machine learning course over weeks leads to better understanding than intensive weekend sessions.