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. However, we have no precise understanding of the reason for this effect. In addition to existing theoretical results, we use a machine-learning agent that learns cognitive skills from examples and problem solving experience, SimStudent, to provide a computational model of the problem order question. We conduct a controlled simulation study in three different math and science domains (i.e., fraction addition, equation solving and stoichiometry), where SimStudent is tutored by automatic tutors given problems that have been used to teach human students. We compare two problem orders: the blocked problem order, and the interleaved problem order. The results show that the interleaved problem order yields as effective or more effective learning in all three domains, because the interleaved problem order provides more or better opportunities for error detection and correction to the learning agent. Examination of the agent's performance 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.