A comparative study on the use of spatiotemporal maps for teaching data visualization in a hybrid learning context

Abstract

ABSTRACT Data visualization is a complex matter and so is teaching it in the context of engineering and data science. Even more challenging is teaching in a midst of once in a century pandemic. In this paper, we show the results of a workshop conducted with second-year students on a university degree in data science, which took place in the context of pandemic restrictions in a hybrid learning environment. About half of the students were in the classroom and the other half at home. They learned to use STMaps, a tool developed in the scope of the SILKNOW project. We transferred research outcomes to the classroom; similarly, the results of evaluating the tool were transferred to the project. But more importantly, we found insights that the learning process of students that were online was not the same quality as of those that were physically in the classroom. While both groups performed well in terms of correctly fulfilling the tasks proposed during the workshop, the students at home were less motivated and perceived the tasks more cumbersome to achieve. From a research perspective, this activity was perceived as very useful, and similar activities are being planned for each academic year.