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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. 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. 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.