Building adaptive support systems requires a deep understanding of why users get stuck or face problems during a goal-oriented task and how they perceive such situations. To investigate this, we first chart a problem space, comprising different problem characteristics (complexity, time, available means, and consequences). Secondly, we map them to LEGO assembly tasks. We apply these in a lab study (N = 22) equipped with several tracking technologies (i.e., smartwatch sensors and an OptiTrack setup) to assess which problem characteristics lead to measurable consequences in user behaviour. Participants rated occurred problems after each task. With this work, we suggest first steps towards a) understanding user behaviour in problem situations and b) building upon this knowledge to inform the design of adaptive support systems. As a result, we provide the GOLD dataset (Goal-Oriented Lego Dataset) for further analysis.    «

Building adaptive support systems requires a deep understanding of why users get stuck or face problems during a goal-oriented task and how they perceive such situations. To investigate this, we first chart a problem space, comprising different problem characteristics (complexity, time, available means, and consequences). Secondly, we map them to LEGO assembly tasks. We apply these in a lab study (N = 22) equipped with several tracking technologies (i.e., smartwatch sensors and an OptiTrack setu...    »