Bad smells affect maintainability and performance of model-to-model transformations. There are studies that define a set of transformation bad smells, and some of them propose techniques to recognize and–according to their complexity–fix them in a (semi)automated way. In academia it is necessary to make students aware of this subject and provide them with guidelines to improve the quality of their transformations. This project presents the most common bad smells made by master students from Universidad de los Andes, and compares them with those from publicly available repositories of Epsilon transformation language transformations, with the purpose of knowing whether programming style affects the incidence of smells.