Advanced Distributed Learning and ERP: interaction in augmented reality, haptic manipulation with 3D models and learning styles.

The Advanced Distributed Learning (ADL) is a learning process mediated by new technologies. The ADL also makes use of Augmented Reality, which takes place through processes of virtual manipulation. The experimental research on Augmented Reality markers and 3D objects focuses on the possibilities of interaction and manipulation of virtual forms in reality, that allows us to touch and interact with objects that do not exist but that are observable through a screen. Literature shows that there are different modules within the occipitotemporal cortex that receive both visual and somatosensory inputs and it explains how these can be integrated in the learning process. These cortical modules can be active in the evaluation of the various aspects of surface properties of objects, such as the 3D shape, as well as in visual and tactile movements. The purpose of this work is to analyze ERP components (P1, N2, P3) variations, during two different kinds of learning training (T): the same objects are manipulated either in Augmented Reality or during the condition of real haptic manipulation and the variations due to different learning styles are investigated. 12 university students were recruited for the study (mean age 23.11). The subjects were evaluated through a 4 scales style learning test: Visual Verbal (VV), Visual Non Verbal (VnV), Kinesthetic (K), Analytical (A). The subjects performed a training lasting 5 minutes consisting of haptic manipulation of 3D models, obtained through modeling a 3D Blender 2.74 and manipulation in Augmented Reality, presented through Dune® Aurasma models. After each training the subjects had to perform a recognition task of the same stimuli (presented in 2D), during an EEG recording. A General Linear Model was computed to investigate research hypothesis. Statistical Analysis reveals significance values in ERP components analyzed. N1 showed significant values in Analytic Learning (p = 0.007), Training (p = 0.00) and interaction between A * TL (p = 0.014). N2 showed significant value in Visual non Verbal Learning style (p = 0.00), Training (p = 0.01) and interaction VnV * T (p = 0.00). P3 showed significant value in Visual Verbal Learning style (p = 0.01). The subjects with high scores in Analytic Learning style show higher amplitude in the Pz channel, in P1 component. The subjects with high scores of Visual Non Verbal Learning style show higher amplitude in the Centrals, Occipitals and Parietals Channels, in N2 component. The subjects with Visual Verbal Learning, present higher amplitude in Frontals, Centrals, Parietals, and Occipitals Channels, in P3 component. We can conclude that learning styles are involved in perceptual levels during recognition tasks and according to the prominent style, processing involves different ERP components and different brain areas. The learning style affects more these variations when the mode of training is through Augmented Reality, where the visuomotor process is prevalent.