In this paper, an automatic lesson generation system is presented which is suitable in a learning-by-mimicking scenario where the learning objects can be represented as multiattribute time series data. The dance is used as an example in this paper to illustrate the idea. Given a dance motion sequence as the input, the proposed lesson generation system automatically generates the lesson plan for students. It first extracts patterns from the input dance sequence to form the learning objects. The prerequisite structure is then built by considering the relations between the learning objects. Afterward the knowledge structure is constructed from the prerequisite structure based on the knowledge space theory. Finally, the learning path is derived according to an easy-to-complex manner while respecting the prerequisite relations. A user study that involved 40 students was conducted to evaluate the proposed work. The average learning time required for the treatment group (learning with the proposed system) was found to be lower than that of the control group (learning by free browsing) thus demonstrating the learning efficiency of the proposed system. The feedback from the questionnaires indicated that a majority of the subjects showed positive response toward the usefulness and rationality of our proposed system.