Visualisation and Analysis of Multiuser Gaze Data: Eye Tracking Usability Studies in the Special Context of e-Learning Gergely Rakoczi (PhD Student) Institute for Design and Assessment of Technology Vienna University of Technology Vienna, Austria gergely.rakoczi@tuwien.ac.at Margit Pohl (PhD Advisor) Institute for Design and Assessment of Technology Vienna University of Technology Vienna, Austria margit.pohl@tuwien.ac.at I. PROBLEM STATEMENT The motivation for my dissertation derives from my master thesis (for reference see [1]) where I have applied the usability method of eye tracking within the context of e- learning. During this work I have been recurrently confronted with the issue, that the cumulative visualization of recorded eye movements (of several test users) is significantly affected by lack of clarity. Current visualization methods of gaze data may lead to unspecific interpretation in form of ambiguous or inconsistent results during the final analysis process. It is not infrequent that inappropriate visualizations mislead researchers and provoke false conclusions of academic as well as economic studies. The most common visualization methods for cumulative gaze data are heatmaps as well as gaze plots – for further reference see state-of-the-art works from [2,3]. The former is suitable for an overview of eye movements’ intensities, density as well as the general distribution of the learner’s visual attention. However, this visualization method neglects fundamental information for example about the temporal distribution (order of fixation/saccades), general traceability of gaze, (cumulative) start as well as end of learner’s visual explorations and additional information about visual transitions between areas of interest (AOI). The second considers most of the above mentioned aspects, however cannot be fully applied due its insufficient form of representation for multiuser-gaze data. In Figure 1 an exemplary gaze plot for a single user is shown. In contrast Figure 2 represents a gaze plot of 10 test users for the same visual stimulus. Both figures clearly show that there is an imminent need for cumulative gaze plot visualization for multi-user gaze data. The second topic of my PhD concerns the linking of eye tracking studies’ results to e-learning technologies. Eye tracking research has been largely adapted to marketing or human-computer-interaction in general - however it has not been commonly used for learning technologies or e-learning applications in particular. Furthermore the reliability of existing eye tracking studies (within both and economic settings) may be impaired due to ambiguous interpretation. It is a general issue that the analysis process of these eye tracking results is not carried out by basic guidelines or (internationally approved) standards. An aim of my dissertation is to develop a practical framework respectively a set of guidelines for the interpretation process in order to minimize ambiguity during analysis of gaze data. As a side effect this framework should contribute to the improvement of the learning technologies’ quality. Figure 1: single-user gaze plot Figure 2: Ambigious gaze plot for multi-user gaze data Eye movements and subsequently the visual attention of learners highly depend on the user interface as well as on the usability of modern learning technologies. As shown in academic research the learner’s visual exploration, navigation and problem solving strategies form a crucial part of the learning process itself. A better understanding and subsequently an effective mapping, monitoring or supervising of learners’ eye movements by various methods of eye tracking may lead to an improved learning process. 2012 12th IEEE International Conference on Advanced Learning Technologies 978-0-7695-4702-2/12 $26.00 © 2012 IEEE DOI 10.1109/ICALT.2012.15 738