In science education, building models of dynamical systems is a promising method for understanding various natural phenomena and artifacts with scientific concepts. It is, however, difficult to learn skills and concepts necessary for modeling. Though several model-building learning environments (MBEs) have been developed with potentially useful methods for assisting students, the verification of them has been limited so far. Most studies evaluated their effectiveness by measuring the degree of model completion by students, or total learning effect that consists of several types of assistance. In this study, we investigated how students learn modeling skills and concepts of system dynamics through modeling dynamical systems, focusing on how students' behavior and understanding are influenced by the type of assistance and students' prior knowledge. We implemented the function that detects the difference of a model by students from the correct model and gives one of the two types of feedback: "structural explanation" indicates structurally erroneous parts of a model by students to promote students' model completion, while "behavioral explanation" suggests erroneous behavior of a model by students to promote students' understanding about the cause of error. Our experiment revealed the following: (1) Students assigned to structural explanation showed high model completion, but their understanding depended on whether they used the feedback appropriately or not. (2) Students assigned to behavioral explanation showed less model completion, but once they completed models, they acquired a deeper understanding.