Abstract Research shows that for novices learning from worked examples is superior to unsupported problem solving. Additionally, several studies have shown that learning from examples results in faster learning in comparison to supported problem solving in Intelligent Tutoring Systems. In a previous study, we have shown that alternating worked examples and problem solving was superior to using just one type of learning tasks. In this paper we present a study that compares learning from a fixed sequence of alternating worked examples and tutored problem solving to a strategy that adaptively decides how much assistance to provide to the student. The adaptive strategy determines the type of task (a worked example, a faded example or a problem to solve) based on how much assistance the student needed in the previous problem. In faded examples, the student needed to complete one or two steps. The results show that students in the adaptive condition learned significantly more than their peers who were presented with a fixed sequence of worked examples and problems.