Striking the Balance: How Variability Shapes Retrieval Practice and Worked Examples for Transfer Learning

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Abstract
The ability to generalize and apply knowledge to new situations is the hallmark of effective learning. There are multiple ways to improve learning towards generalization. Retrieval practice and worked examples have both been shown to improve learning, retention, and generalization. The literature on category learning also suggests that exposure to more varied examples, rather than the same example repeatedly, improves generalization. However, while retrieval practice is usually investigated with repeated items, worked examples are usually investigated using varied content. Across two experiments, we investigated how combining content variability with worked examples or retrieval practice promoted generalization when learners received initial instruction before practice (Experiment 1) and when they did not (Experiment 2). We found that when learners received initial instruction, retrieval practice led to better generalization than worked examples, regardless of whether the stimuli were repeated or varied. When initial instruction was not provided, worked examples resulted in better generalization than retrieval practice when items were repeated. However, retrieval practice produced comparable generalization to worked examples when items were varied. These findings suggest that variability supports generalization, particularly when learners must induce underlying rules, and that retrieval practice, traditionally associated with memory retention, can also facilitate inductive learning when applied to varied items. This study emphasizes the importance of adapting learning strategies to align with the instructional context and task demands.
Funding Information
  • Carnegie Mellon University

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