A concept map is a diagram that depicts suggested relationships between concepts. The relationships are marked by a relevance degree that denotes the level of correlation between any two concepts. Concept map is a graphical tool used to structure and organize knowledge. In this project, a concept map will be generated based on a real-life dataset of how questions are answered by students (correctly or incorrectly) and the weight of the concepts in the questions. Several algorithms have been proposed to automatically construct concept maps. However, all these algorithms use Apriori algorithm to discover the frequent itemsets and get the association rules. Apriori algorithm requires several database scans, and thus, it is not efficient. A tree-based approach (i.e., FP tree algorithm) adopted in this project to overcome the drawbacks of the Apriori algorithm in the construction of concept maps for adaptive learning systems.