Evidence and Theory for why the Best Example-Problem Ratio To Optimize Learning Gain Depends on Knowledge Content

Abstract Many experiments have demonstrated that more practice testing and less studying of examples produces better learning whereas other experiments show the opposite, that more example study and less practice produces better learning. We present empirical and computational evidence that resolves and explains this apparent inconsistency. We show how a practice testing instructional treatment supports memory learning processes needed for verbatim fact content whereas an example-integrated instructional treatment supports inductive learning processes needed for general skill content. In an experiment comparing both instructional treatments on both types of content, we observe a cross-over interaction such that participants learn verbatim facts better from pure practice testing but learn general skills better from example-integrated practice. We use a computational learning architecture, AL, to create an executable theory that explains and predicts these results. Simulated students developed in AL interactively learn from the materials provided in the same four conditions as human learners and reproduce the same cross-over interaction. We further demonstrate that the benefits of integrated examples for general skill learning are a result of, and thus explained by, AL’s inductive learning mechanisms whereas the benefit of practice for verbatim fact learning result from AL’s memory learning mechanisms.