Understanding users in the wild

Apaolaza, Aitor; Harper, Simon; Jay, Caroline

doi:10.1145/2461121.2461133

Cited by 28 publications

(24 citation statements)

References 15 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In particular, we found out that various bots and random internet requests made the server unstable and led to us closing the proxy connection altogether. More details about the capture tool can be found in [2] 1 .…”

Section: Capture Solutionmentioning

confidence: 99%

Longitudinal Analysis of Low-Level Web Interaction through Micro Behaviours

Apaolaza

Harper

Jay

2015

Proceedings of the 26th ACM Conference on Hypertext &Amp; Social Media - HT '15

Self Cite

View full text Add to dashboard Cite

Figure 1: Evolution of the duration of users' episode over time in each phase. ABSTRACTTo truly understand how people learn to navigate and use a Web site or application, we need to collect real usage data over extended periods of time. Detailed Web interaction data gathered in the wild (from URLs visited, to keystrokes and mouse movements) has the potential to provide an indepth, ecologically valid view of interaction, and enable an understanding of how behaviour evolves over time. Interpreting such data is extremely challenging, however. We present a longitudinal data-driven analysis of fine-grained interaction data captured from 14,000 recurrent users over 12 months. At the core of our approach is the aggregation of low-level interaction data into micro behaviours. By analysing changes in these behaviours as a function of users' accumulated interaction time, we were able to demonstrate how users' interaction evolves as they become more familiar with a Web page. The results demonstrate that monitoring micro behaviours offers a simple and easily extensible post hoc means of understanding how Web-based behaviour evolves over time.

show abstract

Section: Capture Solutionmentioning

confidence: 99%

Longitudinal Analysis of Low-Level Web Interaction through Micro Behaviours

Apaolaza

Harper

Jay

2015

Proceedings of the 26th ACM Conference on Hypertext &Amp; Social Media - HT '15

Self Cite

View full text Add to dashboard Cite

show abstract

“…High level web usage data, such as the links clicked, or time spent on a page, can give us valuable information about the user experience, demonstrating, for example, the differences between blind and sighted users' browsing experiences [2]. When considering lower-level interaction data, interpretation becomes extremely difficult, however, due to the variability of the data, the huge quantities of it, and the requirement to contextualize it, in order to interpret it accurately [1]. Mobile devices and wearable technology can provide data from other modalities via a multitude of sensors, further widening the scope of information that can be logged, and the complexity of analysing it.…”

Section: User Modellingmentioning

confidence: 99%

"I Woke Up as a Newspaper"

Evans

Kerlin

Jay

2015

Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

Spending the day as a newspaper, with your faculties and HCI curiosity intact, would allow rich observation of the interaction process: the reader's touch, gaze, expression... all relating valuable information about the user experience. Digital devices -unlike newspapers -have the capacity to log interaction data. We are still some way from fully exploiting it however, due to the data's size and complexity. Rather than simply logging data and trying to make sense of it, we suggest designing-in detailed UX-analytics. We report our experience challenging interaction designers to consider what they would like to know about the user, and how they could capture this data -from the starting point of a sentient newspaper.

show abstract

“…We determined that the modified version of UsaProxy was a better approach. More detail about the comparison can be found in [2].…”

Section: State Of the Researchmentioning

confidence: 99%

Identifying emergent behaviours from longitudinal web use

Apaolaza

2013

Proceedings of the Adjunct Publication of the 26th Annual ACM Symposium on User Interface Software and Technology - UIST '13 Ad

Self Cite

View full text Add to dashboard Cite

Laboratory studies present difficulties in the understanding of how usage evolves over time. Employed observations are obtrusive and not naturalistic. Our system employs a remote capture tool that provides longitudinal low-level interaction data. It is easily deployable into any Web site allowing deployments in-the-wild and is completely unobtrusive. Web application interfaces are designed assuming users' goals. Requirement specifications contain well defined use cases and scenarios that drive design and subsequent optimisations. Users' interaction patterns outside the expected ones are not considered. This results in an optimisation for a stylised user rather than a real one. A bottom-up analysis from low-level interaction data makes possible the emergence of users' tasks. Similarities among users can be found and solutions that are effective for real users can be designed. Factors such as learnability and how interface changes affect users are difficult to observe in laboratory studies. Our solution makes it possible, adding a longitudinal point of view to traditional laboratory studies. The capture tool is deployed in real world Web applications capturing in-situ data from users. These data serve to explore analysis and visualisation possibilities. We present an example of the exploration results with one Web application.

show abstract

Understanding users in the wild

Cited by 28 publications

References 15 publications

Longitudinal Analysis of Low-Level Web Interaction through Micro Behaviours

Longitudinal Analysis of Low-Level Web Interaction through Micro Behaviours

"I Woke Up as a Newspaper"

Identifying emergent behaviours from longitudinal web use

Contact Info

Product

Resources

About