Michael Quade, M. Halbrügge, Klaus-Peter Engelbrecht, S. Albayrak, S. Möller
{"title":"Predicting task execution times by deriving enhanced cognitive models from user interface development models","authors":"Michael Quade, M. Halbrügge, Klaus-Peter Engelbrecht, S. Albayrak, S. Möller","doi":"10.1145/2607023.2607033","DOIUrl":null,"url":null,"abstract":"Adaptive user interfaces (UI) offer the opportunity to adapt to changes in the context, but this also poses the challenge of evaluating the usability of many different versions of the resulting UI. Consequently, usability evaluations tend to become very complex and time-consuming. We describe an approach that combines model-based usability evaluation with development models of adaptive UIs. In particular, we present how a cognitive user behavior model can be created automatically from UI development models and thus save time and costs when predicting task execution times. With the help of two usability studies, we show that the resulting predictions can be further improved by using information encoded in the UI development models.","PeriodicalId":297680,"journal":{"name":"Proceedings of the 2014 ACM SIGCHI symposium on Engineering interactive computing systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2014 ACM SIGCHI symposium on Engineering interactive computing systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2607023.2607033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Adaptive user interfaces (UI) offer the opportunity to adapt to changes in the context, but this also poses the challenge of evaluating the usability of many different versions of the resulting UI. Consequently, usability evaluations tend to become very complex and time-consuming. We describe an approach that combines model-based usability evaluation with development models of adaptive UIs. In particular, we present how a cognitive user behavior model can be created automatically from UI development models and thus save time and costs when predicting task execution times. With the help of two usability studies, we show that the resulting predictions can be further improved by using information encoded in the UI development models.