Understanding Human Activity through In-Situ Observations: A Qualitative Approach

In the field of human-computer interaction (HCI), researchers use a method called "in situ activity analysis" to study how people interact with technology in real-world settings. This approach involves observing and analyzing individuals’ actions and behaviors in their natural environment, such as homes or workplaces, rather than in a laboratory setting.
The term "in situ" means "in the place," and this method aims to capture the unique context of each individual’s activity. By understanding how people interact with technology in their everyday lives, researchers can identify patterns and trends that can inform the design of more intuitive and user-friendly interfaces.
To conduct in situ activity analysis, researchers use various qualitative methods, such as observing participants’ actions and analyzing their conversations or verbalizations. This approach is inspired by frameworks used in social sciences and ergonomics, but it is a more reduced version of activity analysis for agent-based modeling (ABM).
The ABM approach involves placing a human participant in a simulated situation and observing their responses rather than collecting general information through an interview. The ABM is adjusted through a step-by-step approach based on the knowledge of the participant’s real activity.
Three principles formed the qualitative basis for the SMACH project: 1) individuals are autonomous in realizing their tasks, 2) the agent is embedded in the situation, and 3) the focus is on understanding how the agent interacts with the environment rather than just observing its behavior.
In summary, in situ activity analysis is a method used to study how people interact with technology in their natural environments. By observing and analyzing individuals’ actions and behaviors, researchers can gain insights into how to design more intuitive and user-friendly interfaces. This approach is inspired by frameworks used in social sciences and ergonomics but is adapted for agent-based modeling.