Academic performance of recently accepted students is one of the main issues in Higher Level Institutions since first scholar periods trend to be the most difficult ones for students. Some institutions offer leveling courses to develop students basic knowledge for later courses. However, it is not clear if these help students in more advanced courses. This work presents an analysis, using decision trees, for predicting marks in two mathematics courses based on different criteria of the performance on a previous leveling course. This allows finding the factors that impact in the marks obtained in posterior courses and determining if the leveling one is helping students to improve their academic performance.

In an educational environment, a large amount of information is generated, when properly analyzed, can be useful in decision-making. Educational Data Mining uses Data Mining techniques for analyzing academic information, in such a way that knowledge of different educational aspects can be obtained, being one of the most studied, the academic performance. In the Universidad Autónoma Metropolitana, leveling courses were created so that students who have recently entered

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