%0 Conference Paper %1 conf/ieeevast/BlascheckBBEW16 %A Blascheck, Tanja %A Beck, Fabian %A Baltes, Sebastian %A Ertl, Thomas %A Weiskopf, Daniel %B Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST) %D 2016 %E Andrienko, Gennady L. %E Liu, Shixia %E Stasko, John T. %I IEEE %K 2016 A01 B01 from:leonkokkoliadis sfbtrr161 visus visus:blaschta visus:ertl visus:weiskopf %P 141-150 %R 10.1109/VAST.2016.7883520 %T Visual Analysis and Coding of Data-rich User Behavior %U https://ieeexplore.ieee.org/document/7883520 %X Investigating user behavior involves abstracting low-level events to higher-level concepts. This requires an analyst to study individual user activities, assign codes which categorize behavior, and develop a consistent classification scheme. To better support this reasoning process of an analyst, we suggest a novel visual analytics approach which integrates rich user data including transcripts, videos, eye movement data, and interaction logs. Word-sized visualizations embedded into a tabular representation provide a space-efficient and detailed overview of user activities. An analyst assigns codes, grouped into code categories, as part of an interactive process. Filtering and searching helps to select specific activities and focus an analysis. A comparison visualization summarizes results of coding and reveals relationships between codes. Editing features support efficient assignment, refinement, and aggregation of codes. We demonstrate the practical applicability and usefulness of our approach in a case study and describe expert feedback. %@ 978-1-5090-5661-3

@inproceedings{conf/ieeevast/BlascheckBBEW16, abstract = {Investigating user behavior involves abstracting low-level events to higher-level concepts. This requires an analyst to study individual user activities, assign codes which categorize behavior, and develop a consistent classification scheme. To better support this reasoning process of an analyst, we suggest a novel visual analytics approach which integrates rich user data including transcripts, videos, eye movement data, and interaction logs. Word-sized visualizations embedded into a tabular representation provide a space-efficient and detailed overview of user activities. An analyst assigns codes, grouped into code categories, as part of an interactive process. Filtering and searching helps to select specific activities and focus an analysis. A comparison visualization summarizes results of coding and reveals relationships between codes. Editing features support efficient assignment, refinement, and aggregation of codes. We demonstrate the practical applicability and usefulness of our approach in a case study and describe expert feedback.}, added-at = {2020-10-09T12:31:50.000+0200}, author = {Blascheck, Tanja and Beck, Fabian and Baltes, Sebastian and Ertl, Thomas and Weiskopf, Daniel}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/272071092bea887cf012c35324f41b8a5/mueller}, booktitle = {Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST)}, description = {Visual Analysis and Coding of Data-rich User Behavior}, doi = {10.1109/VAST.2016.7883520}, editor = {Andrienko, Gennady L. and Liu, Shixia and Stasko, John T.}, ee = {http://doi.ieeecomputersociety.org/10.1109/VAST.2016.7883520}, interhash = {4654e4d6014d0013153a686ae1f5ab5d}, intrahash = {72071092bea887cf012c35324f41b8a5}, isbn = {978-1-5090-5661-3}, keywords = {2016 A01 B01 from:leonkokkoliadis sfbtrr161 visus visus:blaschta visus:ertl visus:weiskopf}, pages = {141-150}, publisher = {IEEE}, timestamp = {2020-10-09T10:31:50.000+0200}, title = {Visual Analysis and Coding of Data-rich User Behavior}, url = {https://ieeexplore.ieee.org/document/7883520}, year = 2016 }