Abstract: Eye tracking is a great way to triangulate multiple data sources for deeper, more complete knowledge of how instructional materials are really being used and emotional connections made. Using sensor based biometrics provides a detailed local analysis in real time expanding our ability to collect science based data for a more comprehensive level of understanding, not previously possible, for teaching and learning. The knowledge gained will be used to make future improvements to instructional materials, tools, and interactions. The literature has been examined and a preliminary pilot test was implemented to develop a methodology for research in Instructional Design and Technology. Eye tracking now offers the addition of objective metrics obtained from eye tracking and other biometric data collection with analysis for a fresh perspective.
Abstract: In this paper, a new approach for quality assessment
tasks in lossy compressed digital video is proposed. The research
activity is based on the visual fixation data recorded by an eye
tracker. The method involved both a new paradigm for subjective
quality evaluation and the subsequent statistical analysis to match
subjective scores provided by the observer to the data obtained from
the eye tracker experiments. The study brings improvements to the
state of the art, as it solves some problems highlighted in literature.
The experiments prove that data obtained from an eye tracker can be
used to classify videos according to the level of impairment due to
compression. The paper presents the methodology, the experimental
results and their interpretation. Conclusions suggest that the eye
tracker can be useful in quality assessment, if data are collected and
analyzed in a proper way.