Visualizing Learning Analytics and Educational Data Mining Outputs

The increase in supply and demand of on-line courses evidences a new educational paradigm mediated by information and communication technologies. However, an issue in this new paradigm is the high number of students who drop out (85% on average). Some of them blame the lack of instructor support. This support needs the analysis of students' data to guide teachers' decision-making. Learning Analytics (LA), Educational Data Mining (EDM) and Data Visualization (DataViz) are some tools for this analysis, but teachers do not receive appropriate technological support to use them. So, we used DataViz to help teachers understand the output from the application of LA and EDM algorithms on the students' data. We evaluated if instructors understood the information in the visualizations, and asked their opinion about the visualizations' (1) utility; (2) ease of use; (3) attitude towards use; (4) intention to use; (5) aesthetics; (6) the color scheme used; and (7) the vocabulary used. The results indicate that instructors understood the information in the visualizations and the majority of them had favorable opinions, but we noticed the vocabulary used needs improvement.