{"title":"用户自适应信息可视化:利用眼睛注视数据推断可视化任务和用户认知能力","authors":"B. Steichen, G. Carenini, C. Conati","doi":"10.1145/2449396.2449439","DOIUrl":null,"url":null,"abstract":"Information Visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to each individual user. To this end, this paper presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict the user's visualization tasks, as well as user cognitive abilities including perceptual speed, visual working memory, and verbal working memory. We show that such predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are discussed in view of designing visualization systems that can adapt to each individual user in real-time.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"150","resultStr":"{\"title\":\"User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities\",\"authors\":\"B. Steichen, G. Carenini, C. Conati\",\"doi\":\"10.1145/2449396.2449439\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Information Visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to each individual user. To this end, this paper presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict the user's visualization tasks, as well as user cognitive abilities including perceptual speed, visual working memory, and verbal working memory. We show that such predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are discussed in view of designing visualization systems that can adapt to each individual user in real-time.\",\"PeriodicalId\":87287,\"journal\":{\"name\":\"IUI. International Conference on Intelligent User Interfaces\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"150\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IUI. International Conference on Intelligent User Interfaces\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2449396.2449439\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IUI. International Conference on Intelligent User Interfaces","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2449396.2449439","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities
Information Visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to each individual user. To this end, this paper presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict the user's visualization tasks, as well as user cognitive abilities including perceptual speed, visual working memory, and verbal working memory. We show that such predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are discussed in view of designing visualization systems that can adapt to each individual user in real-time.