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Contextual Inquiry Methodology
Contextual Inquiry | Our Method | Our Models Contextual Inquiry is an approach to answering the question, "What should we build to help people do their work better?" It involves observing people in their normal environment and looking for process, culture, task sequence, document and communication flow, physical environment constraints, and work artifacts. Whenever possible, it is ideal to observe people in their own work areas because people cannot often verbalize what they need, actions speak louder than words, and having conversations with users in the context of their work creates richer data. It is easier to point to a screen, explain relevant features, and demonstrate how it works than to explain what would happen in a particular situation. Often times, important details are lost.Those details are important as the data is used to inspire, constrain, prune, and guide design ideas. Cross functional team studies the data, brainstorms ideas, and evaluates their feasibility. This creates a situation of "Grounded brainstorming" because ideas are driven by the data on customers' work practices. Methods are the first set of HCI tools, but without the ability to be flexible and tailor themselves to specific situations, they are worthless. Contextual Inquiry was a great help to our project because we were able to be flexible with it. Early in the project, we learned that video-taping wasn't an option for our CIs. We solved this first challenge by taking extremely detailed notes, and often audio-taping the sessions. Another challenge we faced was with the fast-paced academic environment working with a more laid-back public sector client. Often, we faced difficulty getting in touch with people and scheduling meetings. We got around this difficulty by working ahead whenever possible. As much as possible, we tried not to let scheduling problems be bottlenecks to our process. Another way we applied the Contextual Inquiry method was to develop new models for better communication. By (roughly) combining the sequence and flow models, we were able to demonstrate the complexity of the current processes. ![]() These models were a great help for us in understanding the CI, communicating to fellow team members, creating new designs, and consolidating our findings into one large picture. ![]()
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| © Carnegie Mellon University, Masters of Human-Computer Interaction, CitiStat project: Peter Centraf, Lisa Edelman, Lorrianne Nault, Matt Sharpe, Adrian Tang | |||||||||