Based on our empirical studies of effective human tutoring, we developed an Intelligent Tutoring System, iList, that helps students learn linked lists, a challenging topic in Computer Science education. The iList system can provide several forms of feedback to students. Feedback is automatically generated thanks to a Procedural Knowledge Model extracted from the history of interaction of students with the system. This model allows iList to provide effective reactive and proactive procedural feedback while a student is solving a problem. We tested five different versions of iList, differing in the level of feedback they can provide, in multiple classrooms, with a total of more than 200 students. The evaluation study showed that iList is effective in helping students learn; students liked working with the system; and the feedback generated by the most sophisticated versions of the system is helpful in keeping students on the right path.