Description
Augmented Reality (AR) technologies have evolved rapidly over the last years, particularly with regard to user interfaces, input devices, and cameras used in mobile devices for object and gesture recognition. While early AR systems relied on pre-defined trigger images or QR code markers, modern AR applications leverage machine learning techniques to identify objects in their physical environments. So far, only few empirical studies have investigated AR's potential for supporting learning and task assistance using such marker-less AR. In order to address this research gap, we implemented an AR application (app)with the aim to analyze the effectiveness of marker-less AR applied in a mundane setting which can be used for on-the-job training and more formal educational settings. The results of our laboratory experiment show that while participants working with AR needed significantly more time to fulfill the given task, the participants who were supported by AR learned significantly more.
The Effect of Marker-less Augmented Reality on Task and Learning Performance
Augmented Reality (AR) technologies have evolved rapidly over the last years, particularly with regard to user interfaces, input devices, and cameras used in mobile devices for object and gesture recognition. While early AR systems relied on pre-defined trigger images or QR code markers, modern AR applications leverage machine learning techniques to identify objects in their physical environments. So far, only few empirical studies have investigated AR's potential for supporting learning and task assistance using such marker-less AR. In order to address this research gap, we implemented an AR application (app)with the aim to analyze the effectiveness of marker-less AR applied in a mundane setting which can be used for on-the-job training and more formal educational settings. The results of our laboratory experiment show that while participants working with AR needed significantly more time to fulfill the given task, the participants who were supported by AR learned significantly more.