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Año/Year 2019:

Clave: RV  Ref: IJEE'19
William Villegas-Ch, Xavier Palacios-Pacheco, Diego Buenaño-Fernández, Sergio Luján-Mora. Comprehensive Learning System Based on the Analysis of Data and the Recommendation of Activities in a Distance Education Environment. International Journal of Engineering Education (IJEE), 35(5), p. 1316-1325. ISSN: 0949-149X.

Traditional teaching, based on techniques in which students develop a passive function, has proven to be an inefficient method in the engineering learning process. Universities have been forced to improve their teaching methods and have found a partial solution in open source platforms; these platforms have allowed a greater collaboration between institutions that improve the contribution of technology to education. There are cases of collaboration between universities where their sole objective is to promote student learning and the automation of educational processes. The massification of this type of technological tools allows the use of systems and platforms commonly used in the business world. This adoption of open source tools has proven to be very effective in educational environments and has offered several benefits such as the reduction of costs and the constant updating of information systems. One of the frequent cases in which there are collaborative projects based on learning is the analysis of educational data that seek to detect students’ deficiencies and to take actions before they abandon their studies. In this work, we propose the design of an integral learning system in which business intelligence, expert systems, learning management systems and different learning techniques converge. This integration seeks to create a system capable of recommending different activities that focus on the needs of students.
  



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