Journal Paper

#80 

@article{Chundury2022, title={Contextual In-Situ Help for Visual Data Interfaces}, author={Pramod Chundury and Mehmet Adil Yalcin and Jonathan Crabtree and Anup Mahurkar and Lisa M. Shulman and Niklas Elmqvist}, url={https://users.umiacs.umd.edu/~elm/projects/contextual-help/contextual-help.pdf}, year={2022}, date={2022-09-09}, journal={Information Visualization}, abstract={As the complexity of data analysis increases, even well-designed data interfaces must guide experts in transforming their theoretical knowledge into actual features supported by the tool. This challenge is even greater for casual users who are increasingly turning to data analysis to solve everyday problems. To address this challenge, we propose data-driven, contextual, in-situ help features that can be implemented in visual data interfaces. We introduce five modes of help-seeking: (1) contextual help on selected interface elements, (2) topic listing, (3) overview, (4) guided tour, and (5) notifications. The difference between our work and general user interface help systems is that data visualization provide a unique environment for embedding context-dependent data inside on-screen messaging. We demonstrate the usefulness of such contextual help through two case studies of two visual data interfaces: Keshif and POD-Vis. We implemented and evaluated the help modes with two sets of participants, and found that directly selecting user interface elements was the most useful.}, keywords={}, }