A framework for understanding outcomes of mutual learning situations in IT projects
Abstract
How do we analyse and understand design decisions derived from mutual learning (ML) situations and how may practitioners take advantage of these in IT projects? In the following we present a framework of design decisions inferred from ML situations that occurred between end-users and stakeholders in two participatory design workshops. The participants of the workshops were tasked with redesigning the graphical user interface (GUI) of an electronic ambulance record (EAR). Users participated primarily in the first workshop while stakeholders as well as users participated in the second. We identify the concepts of actionable and volatile ML situations that may result in solidification of a decision of either the design of the artefact or the development process. These concepts are applied post-hoc to four exemplary ML situations that occurred during the workshops: a) a ML situation where design issues of ease of use were solved by users; b) a situation where usability issues beyond the users' grasp were identified in the first workshop and solved by stakeholders and users together during the second workshop; c) and d) situations where usability issues were solved by the users in the first workshop but not discussed with stakeholders despite the fact that the users had proclaimed that the redesign could have consequences for the stakeholders in the long run. By applying the framework to the ML situations as we define situation "a" and "b" as actionable and situation "c" and "d" as volatile.
