Designing an Interactive Visualization to Explore Eye-movement Data
- 63 Downloads
For many user applications large data sets may be collected passively and unobtrusively in the flow of their activity, and on scales ranging from the individual to increasingly larger communities. Large data sets, however, bring a concomitant need for tools to help understand what the data indicates. With the emergence of smart eyewear and the availability of sophisticated but affordable eye-tracking devices, eye movement data becomes a source of detailed information about a user’s focus and indirectly about their cognition and attention. Visualizing this usefully in terms meaningful for diagnosis however, remains a challenge. In this paper we report a new data representation from significant data sets generated by a gaze-controlled digital reading application for second language speakers. Current tools provide data sets aimed primarily towards statistical analysis of patterns: our focus is on end-user exploration of data sets in domain terms, so that practical implications can be readily identified. The visualization of horizontal eye movement data allows rapid diagnosis of problem areas in texts, informing educators immediately of individual or wider issues. The general applicability of this visualization to other applications is discussed.
KeywordsVisualization Gaze control Data analytics Eye movements Horizontal eye movement plot (HEMP)
Unable to display preview. Download preview PDF.
- 3.Pernice, K., Whitenton, K., Nielsen, J.: How People Read on the Web: The Eyetracking Evidence, NNGroup, 2014Google Scholar
- 5.SR Research Data Viewer http://www.sr-research.com/dv.html, (last visited) 2016
- 9.Few, S.: Information Dashboard Design: The Effective Visual Communication of Data. O’Reilly. 2006Google Scholar
- 10.Sowa J.F., Categorization in Cognitive Computer Science. Cohen, H., Lefebvre, C.: Handbook of Categorization in Cognitive Science. Elsevier, 141-163, 2006Google Scholar
- 11.Silver D., Huang A., Maddison C.J., Guez A., Sifre L., van den Driessche G., Schrittwieser J., Antonoglou I., Panneershelvam V., Lanctot M., Dieleman S., Grewe D., Nham J., Kalchbrenner N., Sutskever I., Lillicrap T., Leach M., Kavukcuoglu K., Graepel T., Hassabis D.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)CrossRefGoogle Scholar
- 12.Gammack, J., Morey, J., Thornquist, E.S.: Innovative reading support for non-native readers of university digital texts. Doman, E., Bidal, J.: Departing from Tradition: Innovations in English Language Teaching and Learning. Cambridge Scholars Publishing. 128-147, 2016Google Scholar
- 13.Morey, J., Gammack, J., Thornquist, E.S.: Interface development for a gaze-controlled reading support application. IEEE Information and Communication Technology Research (ICTRC). 214-217, 2015Google Scholar
- 14.Shovman, M.M, Szymkowiak, A., Bown, J.L., Scott-Brown, K.C.: Changing the View: towards the theory of visualization comprehension. 13th International Conference Information Visualization 135-138, 2009Google Scholar
- 15.Everling, S., Fischer, B.: The Antisaccade: A Review of Basic Research and Clinical Studies Neuropsychologia. 25(8), 774-777, 1998Google Scholar