Content Matters: A Computational Investigation into the Effectiveness of Retrieval Practice and Worked Examples.

In this paper we argue that artificial intelligence models of learning can contribute precise theory to explain surprising student learning phenomena. In some past studies of student learning, practice produces better learning than studying examples, whereas other studies show the opposite result. We reconcile and explain this apparent contradiction by suggesting that retrieval practice and example study involve different learning cognitive processes, memorization and induction, respectively, and that each process is optimal for learning different types of knowledge. We implement and test this theoretical explanation by extending an AI model of human cognition — the Apprentice Learner Architecture (AL) — to include both memory and induction processes and comparing the behavior of the simulated learners with and without a forgetting mechanism to the behavior of human participants in a laboratory study. We show that, compared to simulated learners without forgetting, the behavior of simulated learners with forgetting matches that of human participants better. Simulated learners with forgetting learn best using retrieval practice in situations that emphasize memorization (such as learning facts or simple associations), whereas studying examples improves learning in situations where there are multiple pieces of information available and induction and generalization are necessary (such as when learning skills or procedures).