Comparison of applications for educational data mining in Engineering Education
Diego Buenaño-Fernández, Sergio Luján-Mora
1st IEEE World Engineering Education Conference (EDUNINE 2017), p. 81-85, Santos (Brazil), March 19-22. ISBN: 978-1-5090-4886-1. https://doi.org/10.1109/EDUNINE.2017.7918187
(EDUNINE'17b) Congreso internacional / International conference
Currently there are many techniques based on information technology and communication aimed at assessing the performance of students. Data mining applied in the educational field (educational data mining) is one of the most popular techniques that are used to provide feedback with regard to the teaching-learning process. In recent years there have been a large number of open source applications in the area of educational data mining. These tools have facilitated the implementation of complex algorithms for identifying hidden patterns of information in academic databases. The main objective of this paper is to compare the technical features of three open source tools (RapidMiner, Knime and Weka) as used in educational data mining. These features have been compared in a practical case study on the academic records of three engineering programs in an Ecuadorian university. This comparison has allowed us to determine which tool is most effective in terms of predicting student performance.