Capturing Method

There are often several methods to capture the same or similar data at a workplace. For instance, affective aspects can be monitored by sensors [57, 56] or self-reporting apps [191]. The best method depends on the requirements of the workplace, because the selection of the capturing method defines the qualities of the resulting data and the effort to capture these data.

• Quality of resulting data includes, among other factors, sampling frequency, precision, and accuracy. A higher sampling frequency and precision are beneficial to the point at which no new information is contained. Conversely, the accuracy can be lowered by a systematic bias that does not have to be negative as long as users are aware of this bias. For instance, self-reporting of data introduces the bias of the user into the system. The bias itself makes it easier for a user to reflect on the data. Moreover, the bias may become the topic of reflection as in Echo [7]. An unknown bias, however, may lead to incorrect conclusions in the reflection session. Sensors can introduce a bias as well (e.g. by being positioned in only one of several rooms). The sampling rate may be inappropriate to measure particular events, or the selected sensors may exhibit a general lack in accuracy.

• Efforts and costs to capture the data are the main reasons why a system is not accepted by employees or a decision is made against installing the system in the first place. The efforts and costs include: costs to the employer, effort for the employee, and legal constraints. All three can come in varying and unexpected forms. For exam- ple, developers are often not aware of the required effort to train employes to use the system. These barriers exist and should be carefully analyzed in collaboration with the end users. Efforts that are accepted in one place may not be accepted in another.

Designers will often encounter a tradeoff between effort and quality of the data. A diary application can be used once per week or every day. The amount of data and the possible insights will differ. Likewise, more complex and expensive sensors can often deliver a higher accuracy. The methods to capture data fall into one of the following three categories:

• Self-reporting of data relies on the active effort of a user to report events and the user’s own impressions. Mobile applications and blogs have largely replaced the classic handwritten journal. Because of user involvement, the resulting data will be biased by the reporting person. Some QS applications attempt to minimize this bias by restricting the input to a specified structure that can later be analyzed automatically. For instance, the MoodMap App [191] restricts the input to a single click in a two-dimensional space. However, as in the case of the MoodMap App, the collected information is often directly related to the personal subjective experience.

Self-reporting can be used in a wide variety of scenarios, but requires the cooperation and acceptance of users. The motivation of users will determine the amount and quality of captured data. The user interface plays a crucial role in guiding and motivating the user. • Observer-reporting in its properties to self-reporting, except that the

effort to capture the data is moved to an observer. The observer can be a single mentor, for instance an experienced nurse in a care home, or a large group of observers. The feedback from groups is especially valuable because, although the bias from each observer influences the final result, the overall result will contain only an average bias. The aggregation of this feedback can provide an objective external perspective on an event. Customer surveys build on this principle. However, as observers have to capture data by themselves, their motivation is crucial for success. The Live Interest Meter app [91] supports presenters by feedback from their audience, which is motivated to provide such feedback by leveraging on their interest to listen to an engaging talk.

Observer-reporting can be applied when observers are available and if they can be motivated to share their views on an experience. Observer data can become more objective by aggregating feedback from multiple observers.

• Automatic capturing of data is realized by either sensors or monitor- ing applications. Automatic approaches can capture one detail (e.g. the room temperature) at a high sampling frequency and precision. The user acceptance depends on two conflicting arguments. Sen- sors and applications remove the reporting burden by automatically

recording data, but the monitored person is no longer directly in control of the recorded data. This can lead to feeling monitored by the management and lower the acceptance of such systems among employees. Automatic capturing systems must include a means that enables employees to reclaim ownership of the data. Sensor data are often judged as more objective, but can be biased by the technology or the usage of this technology. These biases are not often obvious to a user and must be communicated clearly.

Automatic capturing methods can deliver a higher sampling frequency and precision. They often provide a very limited perspective, but a maximum granularity on an experience. The costs to design and introduce a sensor system are related to required hardware and software, so they tend to be higher than reporting approaches. Furthermore, hardware and software often limit existing sensors and tools to very specific domains. For instance, the gloves and tools in the wearIT@work prototype [15] are tied to car manufacturing in one factory.

The type of physical tasks conducted by the users will guide the type of capturing method. For example, if users need freedom of movement and cannot use their hands to input any data, self-reporting can be done only with custom input methods [90]. If, however, users can embed the manual capturing of the data in their current working tasks (e.g. because they work at a desk or these data have to be recorded anyway), self-reporting can be the better option.

Although sensors are expected to provide an objective perspective, planned or involuntary interpretation, done by the learner or the capturing method, might skew the view of the perceived situation. Hence, the differ- ence between objective and subjective data are only relative. Conversely, an objective perspective provided by automatic means (e.g., a video or a picture) already includes subjective bias. The person taking a picture with the camera is focusing on a selected aspect and cuts out other relevant ele- ments. Sensors can capture with high precision, but they lack the broader view of a human observer. Additionally, by selecting the monitored detail, a subjective bias is already introduced. However, data that are biased by subjective impressions can become more objective through aggregation across several incidents. For instance, the different perspectives provided

by observers are subjective and biased, but by quantifying and summa- rizing the feedback of multiple observers, a more objective feedback is possible.