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For novice learners of statistics, successful recognition of a statistical concept in a problem requires an understanding of abstract rules and principles. Whereas experts focus on structure, novices typically rely on surface features such as the storyline presented in the problem. However, novices can learn to foster expert-like strategies with exposure to examples that vary the surface features to promote structure-based learning. This strategy may be further improved by increasing the number of examples used during initial learning. The purpose of this study was to examine the effects of short-term guided training on problem recognition in novices. Three statistical concepts were each illustrated with two or three examples which had high or low content variability. Results support the use of three high content variable examples as a simple and timeeffective implementation to learning.

Number and Content Variability of Instructive Examples Promote Structurebased Learning