Analysis of the Learning Experience in a Chemical AR Application Using Process Mining

Abstract

Augmented Reality (AR) and its use in science education are getting more attention as the technology develops. It has been proven that the current affordances of this technology enable learners to interact with knowledge as never before. For example, visualizing invisible phenomena, training with unsafe situations, or improving spatial skills. Previous studies have extensively listed AR's advantages for learners; however, how students interact with the technology is a less explored perspective. In this study, we investigated how students with different expertise in the laboratory interact with MAR Lab, a mobile augmented reality application for teaching titration to students. Furthermore, by analysing the log data of their actions using process mining, future improvements were proposed.