Workplaces offer a plethora of data that can be captured or is already available in digital form. The selection of data is driven by the considera- tions explained in Section 4.1.2: learners must be able to relate their own experiences to the data and come to new insights. Furthermore, the design challenges described in Section 4.1.3 highlighted that the goal should be a small subset of data to minimize interference with ongoing work processes. The decision of which data are relevant is a typical example of therelevance
paradox [129]. While a designer has all data at hand, it is difficult to
decide which subset will be relevant in a reflection session because of (a) the unpredictability of context relevance and (b) the subjectivity and need for interpretation of context.
• Unpredictability of relevance: Since the outcome of reflection cannot be clearly predicted, (i) it is unclear which context is useful and (ii) more context information has to be captured than will probably be used afterward. Developers can only estimate which data might be useful and, thereby, they have to balance required effort and estimated impact. Which data are useful to reflect on an experience? Which experiences are worth reflecting?
• Subjectivity and need for interpretation: It is inherently difficult to identify data that relate to a concrete experience. While hardware sensors and IT systems can capture a growing part of the context, the perception of this context and its interpretation is hard to estimate. The perception by a user depends on existing experiences and biases. Only the user can provide the necessary feedback to select the relevant subset of data for later reflection and, in many cases, this selection is already part of a reflective learning process. Hence, designers cannot solely rely on fully automated ways of capturing (such as hardware sensors or mining of existing data). A combination of multiple sensors may provide additional hints on the relevance of the data, but applications can involve the user into this process. During the workshops reported in Section 4.1.3, participants repeatedly referenced three types of context in stories on reflection: task context, affective context, and social context. They are promising candidates to support reflection at the workplace with data. This is not an exhaustive list, but can help to identify alternative options. For instance, the location is relevant in many professions, but is mainly interpreted in the sense of social or task context (i.e., “what did I do there?” or “who was I with?”) The different roles of these three context types in the reflective process are described below. In addition, example tools and related challenges for measuring this context are provided.
• Task context describes activities that are directly related to work tasks. The data are easy to understand and seen as relevant by employees and management. The required data are often already present in existing documentation of work (e.g., as treatment records in a hospital), but might not be available for technical or legal reasons. Activities at a PC can be captured with commercial tools like
RescueTime [130]. Sensors can augment tools and the environment with sensing capabilities that capture task context. For instance, wearIT@work [15] supported workers in a car factory by augmenting tools with RFID tags. All interaction with tools was captured by an RFID reader at the glove. However, capturing task context can be perceived as an undesired performance-monitoring by employees. The resulting resistance of employees can lead to manipulation of data or the corresponding tool may not be used at all.
In summary, task context is relevant in nearly all workplaces. The developed solutions are often tailored to the specific tools or applica- tions, but help to provide better understanding of the selected work processes.
• Affective context describes all context information that is related to emotions and moods. Emotions and moods are indicators for relevant time spans during the work day. Important events are often experienced with a higher level of arousal. Furthermore, continuously high arousal may indicate an overburdening of employees. Therefore, affective context can act as a marker for relevant time spans as well as additional data to analyze the impact of emotions on an experience. Moreover, awareness of mood and emotions is important for all employees that directly interact with customers or patients. This kind of work is also called emotional labor [131]. Affective computing (see Section 2.3) has developed a large variety of sensors [57, 62, 56]
and self-reporting approaches [191] to record affective context. Affective context is useful in a variety of roles and has been a central point in reflective learning (see Section 2.1.2). The available sensors and tools are designed to be used across work contexts. However, data about our emotions are highly privacy relevant.
• Social context records the interactions with colleagues, customers, and patients. In interviews, employees often begin explaining an experience by mentioning the people that were present. Persons and their names act as a memory cue for experiences that were made together. Moreover, in many professions, the social contacts structure the day. Physicians, for example, treat one patient after the other and talk with relatives and colleagues. In this case, the social contacts correspond to the task context. Social contacts can
be face-to-face or mediated by technology. Technology-mediated contacts can be analyzed using the corresponding communication tool and social network analysis (SNA) [118]. A variety of sensors and mobile phone applications can record face-to-face contacts (see Section 3.2.2).
Social context is relevant for many professions, is privacy critical, and can be captured by sensors and applications across contexts. All three types of context can provide a rather objective or subjective view on a situation, depending on the capturing method that will be discussed in Section 4.2.2. The comparison of these possibly conflicting views can induce a cognitive dissonance, reveal new insights, and eventually lead to a change in behavior. A subjective and personal experience is the result of the individual’s interpretation of the situation. The return to experience and re-evaluation of an experience in the model of Boud et al. [4] targets this subjective and personal experience. In being subjective, it might not always reflect an actual situation, but is interwoven with personal beliefs and expectations. Subjective and personal experiences are accessible only for tools and others when the learner articulates this knowledge. This articulation might be complicated by the tacit nature of a part of the knowledge at work. If knowledge is articulated, it will generally be subjective because it reassembles the mindset of the learner. This view can be further distorted by the method of recording (e.g., notes might be misinterpreted after reading them at a later time).
Objective perspectives differ from experiential data in their underlying perception of the situation as well as their interpretation of the gathered data. For instance, sensors have only a limited view of a situation as they capture a small subset of an experience in great detail. A learner generally has a broader perspective, but might interpret the perception or even the experience incorrectly. As Moon points out,“If learners do not learn from
experience but from their perception of experiences, there are implications for the nature of guidance required by learners in order to make sense of experience” [26].