Across many domains, Intelligent Tutoring Systems (ITSs) are used to facilitate practice, providing a customized learning environment and personal tutoring experience for students to learn at their own pace through effective student modeling and feedback. Most current ITSs are built around cognitive learning theories including Ohlsson's theory on learning from performance errors and Anderson's ACT theories of skill acquisition which focus primarily on providing negative feedback or corrective feedback, facilitating learning by correcting errors. Research into the behavior and methods used by expert tutors suggest that experienced tutors use positive feedback quite extensively and successfully. This research investigates positive feedback; learning by capturing and responding to correct behavior, supported by cognitive learning theories. The research aim is to develop and implement a systematic approach to delivering positive feedback in Intelligent Tutoring Systems, in particular SQL-Tutor, a constraint-based tutor which instructs users in the design of Structured Query Language (SQL) database queries. An evaluation study was conducted at the University of Canterbury involving a control group of students who used the original version of SQL-Tutor giving only negative feedback and an experimental group using the modified version of SQL-Tutor where both negative and positive feedback were given. Results of the study show that students learn quite similarly from one system to another, however those in the experimental group take significantly less time to solve the same number of problems, in fewer attempts compared to those in the control group. Students in the experimental group also learn approximately the same number of concepts as students in the control but in much less time. This indicates that positive feedback results in increased amount of learning over a shorter period of time and improves the effectiveness of learning in ITSs.