Empirical studies have shown that learning from worked examples is an effective learning strategy. A worked example provides step-by-step explanations of how a problem is solved. Many studies have compared learning from examples to unsupported problem solving, and suggested presenting worked examples to students in the initial stages of learning, followed by problem solving once students have acquired enough knowledge. Recently, researchers have started comparing learning from examples to supported problem solving in Intelligent Tutoring Systems (ITSs). ITSs provide multiple levels of assistance to students, adaptive feedback being one of them. The goal of this research is to investigate using examples in constraint-based tutors by adding examples into SQL-Tutor. SQL-Tutor is a constraint-based tutor that teaches the Structured Query Language (SQL). Students with different prior knowledge benefit differently from studying examples; thus, another goal of the research is to propose an adaptive model that considers the student’s prior knowledge for providing worked examples. Evaluation of this research produced promising results. First, a fixed sequence of alternating examples and problems was compared with problems only and examples only. The result shows that alternating examples and problems is superior to the other two conditions. Then, a study was conducted, in which a fixed sequence of alternating worked examples and tutored problem solving is compared with a strategy that adapts the assistance level to students’ needs. The adaptive strategy determines the type of the task (a worked example, a faded example or a problem to be solved) based on how much assistance the student received in the previous problem. The results show that students in the adaptive condition learnt significantly more than their peers who were presented with the fixed sequence of worked examples and problem solving. The final study employed eye tracking and demonstrated that novices and advanced students study SQL examples differently. Such information can be used to provide proactive rather than reactive feedback messages to students’ actions.