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Syst."],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>Information visualizations are an efficient means to support the users in understanding large amounts of complex, interconnected data; user comprehension, however, depends on individual factors such as their cognitive abilities. The research literature provides evidence that user-adaptive information visualizations positively impact the users\u2019 performance in visualization tasks. This study attempts to contribute toward the development of a computational model to predict the users\u2019 success in visual search tasks from eye gaze data and thereby drive such user-adaptive systems. State-of-the-art deep learning models for time series classification have been trained on sequential eye gaze data obtained from 40 study participants\u2019 interaction with a circular and an organizational graph. The results suggest that such models yield higher accuracy than a baseline classifier and previously used models for this purpose. In particular, a Multivariate Long Short Term Memory Fully Convolutional Network shows encouraging performance for its use in online user-adaptive systems. Given this finding, such a computational model can infer the users\u2019 need for support during interaction with a graph and trigger appropriate interventions in user-adaptive information visualization systems. This facilitates the design of such systems since further interaction data like mouse clicks is not required.<\/jats:p>","DOI":"10.1145\/3446638","type":"journal-article","created":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T22:27:25Z","timestamp":1621549645000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Predicting Visual Search Task Success from Eye Gaze Data as a Basis for User-Adaptive Information Visualization Systems"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5470-4850","authenticated-orcid":false,"given":"Moritz","family":"Spiller","sequence":"first","affiliation":[{"name":"INKA\u2014Innovation Laboratory for Image Guided Therapy, Health Campus Immunology Infectiology and Inflammation (GC-I3), Otto-von-Guericke-University, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying-Hsang","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Southern Denmark, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Zakir","family":"Hossain","sequence":"additional","affiliation":[{"name":"The Australian National University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Gedeon","sequence":"additional","affiliation":[{"name":"The Australian National University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"Geissler","sequence":"additional","affiliation":[{"name":"Otto-von-Guericke-University, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"N\u00fcrnberger","sequence":"additional","affiliation":[{"name":"Otto-von-Guericke-University, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,5,20]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Bower","author":"Anderson John R.","year":"1974","unstructured":"John R. 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