Nan Li, William W. Cohen, Kenneth R. Koedinger
The order of problems presented to students is an important variable that affects learning effectiveness. Previous studies have shown that solving problems in a blocked order, in which all problems of one type are completed before the student is switched to the next problem type, results in less effective performance than does solving the problems in an interleaved order. While results are starting to accumulate, we have little by way of precise understanding of the cause of such effect. Using a machine-learning agent that learns cognitive skills from examples and problem solving experience, SimStudent, we conducted a controlled simulation study in three math and science domains (i.e., fraction addition, equation solving and stoichiometry) to compare two problem orders: the blocked problem order, and the interleaved problem order. The results show that the interleaved problem order yields as or more effective learning in all three domains, as the interleaved problem order provides more or better opportunities for error detection and correction to the learning agent. The study shows that learning when to apply a skill benefits more from interleaved problem orders, and suggests that learning how to apply a skill benefits more from blocked problem orders.
The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_24.