Towards co-design workshops based on data-driven context detection: A pilot reflection workshop in childcare

Abstract: In designing to improve the quality of childcare, it is necessary to empathize with stakeholders in daycare. In the childcare field, there is many context-dependent know-how that only the people in the field can understand. It is essential to involve designers and non-designers in co-designing. The recent development of information and communication technologies (ICT) has allowed the acquisition of in-situ behavioral data, which is defined as “a collection of specific information, referring to data from sensors, self-logging, telemetry, or social networks which capture people’s behaviors and patterns”. Although behavioral data has not been widely used in design, co-designers' behavioral data has the potential to play an important role in supporting co-design by providing a common ground. Developing tools to support reflection uses the field’s data. The materials provide a common understanding of the scene to be considered for reflection. However, selecting which scenes to reflect and post-data collection processing requires huge efforts. Thus, there is a need to automate the scene selection based on data-driven detection of the childcare contexts. As a step towards data-driven co-design workshops, this study aims to clarify the influence of scene extraction methods on reflection by childcare workers. We collected video recordings in a room mainly used for caring for one-year-old children at a nursery in Japan. The videos used for the workshops were recorded in two ways: upon staff’s individual request, namely self-logging, during caring, and automatic scene detection based on the sound levels. One staff member participated in the data collection. As a result, 16 videos were collected, and then the staff selected the two best videos for the workshop. Two videos were excerpted from a video having been captured whole day depending on the sound level. The excerpted videos had the largest sound level during the day.The workshop was conducted with eight childcare workers. The purpose of the session was to discuss daily childcare and how to take care of children and gain new insights. The participants reflected on each video of self-logged daycare scenes, and then on the auto excerpted videos. The reflection includes, for example, what happened, how the staff behaved, how the staff should have acted, and why the staff could or could not act ideally in the videos. The recorded videos were categorized, and the participants’ perceptions were transcribed for analysis. The videos of daycare were categorized into 9 categories based on video content, and brief notes attached to a staff recording request. The quotes related to video-based reflection and possible data for supporting reflection are extracted from the transcript of the workshop. Overall, the results imply that behavioral data and auto-excerpted recorded videos have huge potential to be used for reflection. The next step of this research is to clarify the issue and benefit of using behavioral data for co-design by conducting a workshop among designers and daycare staff to improve the quality of childcare.

Keywords: Data-driven reflection, behavioral data, co-design

DOI: 10.54941/ahfe1006648

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