Using Learning Analytics and Visualization Techniques to Evaluate the Structure of Higher Education Curricula - Barbosa, Artur Mesquita; de Araujo Neto, Antonio Nilo; Santos, Emanuele; P. Gomes, João Paulo (Anais do Simpósio Brasileiro de Informática na Educação (SBIE); 2017: Anais do XXVIII Simpósio Brasileiro de Informática na EducaçãoDO - 10.5753/cbie.sbie.2017.1297, 2017)
In this paper, we propose a data mining technique that evaluates a curriculum’s structure based on academic data collected from Computer Science students from 2005 to 2016. Our approach is based on the Synthetic Control Method (SCM), which builds a linear model describing the relation between courses based on student performance information. The proposed model is compared to a linear regression model with positive coefficients. In addition to providing the relation between courses, it can also be used to predict students’ grades in a specific course based on their previous grades. The results are visualized in a user-friendly tool, which allows for contrast and comparison between the official structure and the structure found based on the data.