Building adaptive support systems requires a deep understanding of why users get stuck or faceproblems during a goal-oriented task and how they perceive such situations. To investigate this, wefirst chart a problem space, comprising different problem characteristics (complexity, time, availablemeans, and consequences). Secondly, we map them to LEGO assembly tasks. We apply these in alab study equipped with several tracking technologies (i.e., smartwatch sensors and an OptiTracksetup) 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 towardsa) understanding user behaviour in problem situation and b) building upon this knowledge to informthe design of adaptive support systems. As a result, we provide the GOLD dataset (Goal-OrientedLego Dataset) for further analysis.    «

Building adaptive support systems requires a deep understanding of why users get stuck or faceproblems during a goal-oriented task and how they perceive such situations. To investigate this, wefirst chart a problem space, comprising different problem characteristics (complexity, time, availablemeans, and consequences). Secondly, we map them to LEGO assembly tasks. We apply these in alab study equipped with several tracking technologies (i.e., smartwatch sensors and an OptiTracksetup) to assess w...    »