{"id":154190,"date":"2002-01-01T00:00:00","date_gmt":"2002-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/quantitative-analysis-of-scrolling-techniques\/"},"modified":"2018-10-16T20:27:45","modified_gmt":"2018-10-17T03:27:45","slug":"quantitative-analysis-of-scrolling-techniques","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/quantitative-analysis-of-scrolling-techniques\/","title":{"rendered":"Quantitative Analysis of Scrolling Techniques"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We propose a formal experimental paradigm designed to help evaluate scrolling interaction techniques. Such a method is needed by interaction designers to quantify scrolling performance, thereby providing a tool to evaluate and improve upon new techniques. We systematically vary the scrolling distance as well as the required tolerance of scrolling. Distance and tolerance are the parameters of Fitts\u2019 Law, which traditionally has been applied to the evaluation of pointing devices in tasks involving rapid, aimed movement to visible targets. Scrolling involves acquisition of targets well beyond the edges of the screen, yet Fitts\u2019 Law models our experimental data very well.<\/p>\n<p>We apply our paradigm to the IBM ScrollPoint and the IntelliMouse Wheel. Our experimental approach reveals a crossover effect in performance versus distance, with the Wheel performing best at short distances but the ScrollPoint performing best at long distances. We also demonstrate that the performance of the Wheel can be significantly improved using an acceleration algorithm. These results show that our approach yields a practical and rigorous method for the evaluation of scrolling techniques.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose a formal experimental paradigm designed to help evaluate scrolling interaction techniques. Such a method is needed by interaction designers to quantify scrolling performance, thereby providing a tool to evaluate and improve upon new techniques. We systematically vary the scrolling distance as well as the required tolerance of scrolling. Distance and tolerance are the [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13551,13552],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-154190","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-graphics-and-multimedia","msr-research-area-hardware-devices","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"CHI '02 Proceedings of the SIGCHI Conference on Human Factors in Computing 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