How Effective is Example Generation for Learning Declarative Concepts?

摘要

Declarative concepts (i.e., key terms and corresponding definitions for abstract concepts) represent foundational knowledge that students learn in many content domains. Thus, investigating techniques to enhance concept learning is of critical importance. Various theoretical accounts support the expectation that example generation will serve this purpose, but few studies have examined the efficacy of this technique. We conducted three experiments involving 487 undergraduates to investigate the effects of example generation on concept learning and examined factors that may moderate its effectiveness. Students read a short text that introduced eight concepts. Some students were then prompted to generate concrete examples of each concept followed by definition restudy, whereas others only restudied definitions for the same amount of time. Two days later, students completed final tests involving example generation and definition cued recall. Meta-analytic outcomes indicated that example generation yields moderate improvements in learning of declarative concepts, relative to restudy only. Each experiment also included additional groups to investigate potential moderators. Example generation tended to be more effective with spaced versus massed restudy. Despite strong correlations between the quality of examples generated during practice and final test performance, experimental manipulations that improved example quality did not improve learning. In sum, the current work establishes that example generation enhances concept learning and provides an important foundation for further investigating factors that moderate its benefits to learning.