Data Mining to increase teaching performance in Engineering Education

From a global perspective, engineers are the strategic human capital for the development of countries. That is because the industrial sector is increasingly competitive. The contribution of engineers is essential for the strengthening of value chains and innovation, especially in developing countries. In particular, in Argentina, over the last decade, the government has invested heavily in many programs to contribute to the increase in the enrolment rate of engineers careers. However, this was not enough to visualise significant increases. The primary objective of this work is to present the results of the application of data mining techniques directed at measuring the teaching performance in Engineering Education.The unit of study will be by three set of knowledge: Basic Sciences, Basic Technologies and Applied Technologies of the Engineering Careers. The indicators are metrics around student performance and behaviour. We apply principal component analysis (PCA) to find key factors that describe the set of knowledge defined. Finally, we establish five criteria to explain each one. These criteria are vertical articulation, retention strategies, teaching methodologies, evaluation methodologies and learning methods and techniques