Case-Based Reasoning (CBR) is a methodology in artificial intelligence that uses specific previous experiences as basis for reasoning about new similar situations. In providing individualized instruction, tutors learn from their experiences and use these experiences as foundations for identifying the appropriate instructional activities. Most of the approaches used in designing tutoring systems that adapts to its learners use the rule-based approach. If rules were used, a lot of work will be done chaining rules only to find out that it is not useful [Jona, 1998]. Cases can quickly recognize whether a teaching strategy is relevant to apply in a given situation. This paper presents how CBR model can be used to enable the tutor model to use previously successful instructional strategies to the present learning scenario.