Iterative Design Process

Development was conducted using an iterative user-centered approach, as shown in Figure 4.3. Users were included throughout development process, starting with collaborative requirements discussion, testing of early prototypes, and iterative development of requested features. The process consists of three major steps that are aligned to the dimensions of the design space presented in Section 4.2.

In an initial step, the type of context that should be captured is selected. The selection depends on the workspace and underlying goals of employees. Task context helps to improve and compare performance. Affective context is relevant for well-being as well as social interaction with customers and colleagues. Social context supports the analysis of social contacts. Depending on the workplace and the specific type of data, each of the

Select Context Capturing Prototype Feedback Design Prototype evaluation System Evaluation

context types may be used for a different purpose. For example, social context of care staff can help to analyze task performance because the time spent with each patient is directly related to the work task. In summary, the selection of context that should be captured requires discussion and interaction with the end users (i.e., the employees).

Based on this decision, prototypes to capture this context are built and tested. A prototype may use off-the-shelf tools and visualizations of the data can rely on the raw data. The evaluation has to take place in the target context to test whether sufficient data can be captured and how much effort is required. The first test at the workplace should take place as early as possible to identify unexpected challenges. For instance, in the care home, several staff members had problems writing or typing. Therefore, self-reporting approaches in the care home should not contain any text entries. Even showing raw data to employees in a very simple form can provide feedback whether the quality and amount of data are useful for reflection. These first reflection sessions were facilitated by a researcher and evaluated with semi-structured interviews. If the developed prototype does not deliver the required quality (e.g., an insufficient amount of data or an unexpected bias) or if the efforts and costs are too high, the prototype must be adapted and tested again.

The discussions in the first step and feedback during the prototype evalu- ations provide many insights regarding the possible design of the feedback. However, during these tests, employees imagine what the final feedback will look like and which new and deeper insights will become possible with more-advanced visualizations. The first visualization prototypes will most likely not fulfill these expectations completely. Consequently, another cycle of iterations is required. In many cases, the captured data must be filtered and aggregated to minimize the effort to analyze the data.

In the two design studies, the focus is on the second step – the evalu- ation and improvement of capturing prototypes. In both design studies, prototypes for capturing context have been developed and evaluated.

Context

This chapter explores the support of reflection by capturing affective aspects following the design process defined in Section 4.3. The first step in this process is the decision regarding the relevant context according to the design space defined in Section 4.2. The affective context was chosen because tracking of affective context during the work day can help to identify the experiences that trigger an emotional response. These experiences most likely indicate critical events that are worth reflecting. There are three additional benefits to capturing affective context. First, emotionally arousing experiences are better recalled in the long-term [138]. Hence, tracking of affective context during the work day could help to identify remembered experiences and timespans. Reflective learning support could refer to these episodes and also point to events that might have been forgotten. Secondly, the affective context is relevant in nearly all workplaces. A developed solution has the potential of being generalizable to other work domains. Finally, wearable sensors have been developed in affective computing (see Section 2.3) that do not interfere with work tasks [56, 57, 62].

The conducted design process consisted of three major steps, as depicted in Figure 5.1. The structure of this chapter follows these three steps. As explained above, the decision to capture affective context was the starting point. In a first step, a prototype to capture affective context was developed. To this end, a requirements analysis was conducted, as described in Section 4.1.3. After comparing self-reporting and sensors- based approaches in relation to the hospital setting, an available sensor system was chosen as first prototype. This system was evaluated in an initial ethnographically inspired study to deepen the understanding of affective aspects in a hospital. The study is reported in Section 5.2. The results indicated that a different, more flexible system is required. The

Mobile xAffect Affective Context Heart rate Sensors Study on Stroke Unit

Figure 5.1: Design process affective context capturing

deduced requirements informed the development of mobile extensions of the xAffect system [206] that are presented in Section 5.3. The final section summarizes the main findings on capturing affective context.

5.1 Tracking Affect on a Stroke Unit

The healthcare environment challenges employees with dynamic tasks that must be conducted in a limited time. Each of these tasks might literally be vital for a patient. As a result, employees in social care have the highest rate of stress-related illnesses in the UK [134]. The personal reasons behind this number vary according to the particular workplace and individual mindsets of the staff. Therefore, there is no general solution, so the underlying reasons must be identified. Reflection by the individual staff members is one option to identify the appropriate reaction. Reflective practice is seen as a particularly promising approach in care professions [3]. In addition, research has shown the impact of collaborative reflection on work in healthcare professions [34, 139].

The following section compares automated approaches to self-reporting approaches for capturing affective aspects on a stroke unit. It describes the selection of the used sensor and the evaluation of an alternative self- reporting approach.