The Sistema de Medición de la Calidad de la Educación (Education Quality Measurement System, SIMCE) is an annual survey designed to evaluate the Chilean educational system through standard tests that measure the abilities and knowledge of the students. The results of the SIMCE exams provide important information for analyzing the learning processes of the students. Additionally, these results allow the identification of strengths and weaknesses for the elaboration of public policies. In this paper, we design a semi-supervised regressor called TTOSOM as Regression Model (TTOSOM-RM), that inherits the properties of the neural network called Tree-based Topology Oriented SOM (TTOSOM) [1]. The goal is to predict the performance of fourth grade students of the Chilean educational system in the SIMCE test. The proposed model successfully predicts the SIMCE scores, producing a lower absolute mean error when compared with other state-of-the-art methods.