Visualizing Large Quantities of Educational Datamining Information
Sandra Pereira Gama, Daniel Jorge Viegas Gonçalves · 2014
Providing the educational community with tools to analyze educational processes may result in a more effective education. Applying Data Mining techniques to educational data results in information on educational settings which, however, comprehend an extensive set of symbolic patterns that are usually difficult to understand. Visualization, due to its potential to display large quantities of data, may overcome this limitation. We used the results of educational data mining techniques that had been applied to analyze the interdependence among courses in a university program and studied visualization mechanisms to enable the analysis of such patterns. We created a multi-level visualization, in which each level depicts a semester with corresponding courses. We have studied visual connectors to display a high number of interrelations between courses. User tests have shown the effectiveness of a connector which combines visual merging techniques with Bezier curves to represent course interrelation.