Verónica Rivera-Pelayo

Applying quantified self approaches to support reflective learning

This paper presents a framework for technical support of reflective learning, derived from a unification of reflective learning theory with a conceptual framework of Quantified Self tools -- tools for collecting personally relevant information for gaining self-knowledge. Reflective learning means returning to and evaluating past experiences in order to promote continuous learning and improve future experiences. Whilst the reflective learning theories do not sufficiently consider technical support, Quantified Self (QS) approaches are rather experimental and the many emergent tools are disconnected from the goals and benefits of their use. This paper brings these two strands into one unified framework that shows how QS approaches can support reflective learning processes on the one hand and how reflective learning can inform the design of new QS tools for informal learning purposes on the other hand.

Live interest meter: learning from quantified feedback in mass lectures

There is currently little or no support for speakers to learn by reflection when addressing a big audience, like mass lectures, virtual courses or conferences. Reliable feedback from the audience could improve personal skills and work performance. To address this shortcoming we have developed the Live Interest Meter App (LIM App) that supports the gathering, aggregation and visualization of feedback. This application allows audience members to easily provide and quantify their feedback through a simple meter. We conducted several experimental tests to investigate the acceptance and perceived usefulness of the LIM App and a user study in an academic setting to inform its further development. The results of the study illustriate the potential of the LIM App to be used in such scenarios. Main findings show the need for motivating students to use the application, the readiness of presenters to learn retrospectively, and distraction as the main concern of end users.

Context Becomes Content: Sensor Data for Computer-Supported Reflective Learning

Wearable devices and ambient sensors can monitor a growing number of aspects of daily life and work. We propose to use this context data as content for learning applications in workplace settings to enable employees to reflect on experiences from their work. Learning by reflection is essential for todays dynamic work environments, as employees have to adapt their behavior according to their experiences. Building on research on computer-supported reflective learning as well as persuasive technology, and inspired by the Quantified Self community, we present an approach to the design of tools supporting reflective learning at work by turning context information collected through sensors into learning content. The proposed approach has been implemented and evaluated with care staff in a care home and voluntary crisis workers. In both domains, tailored wearable sensors were designed and evaluated. The evaluations show that participants learned by reflecting on their work experiences based on their recorded context. The results highlight the potential of sensors to support learning from context data itself and outline lessons learned for the design of sensor-based capturing methods for reflective learning.

Building Expert Recommenders from Email-Based Personal Social Networks

In modern organisations there is the necessity to collaborate with people and establish interpersonal relationships. Contacting the right person is crucial for the success of the performed daily tasks. Personal email corpora contain rich information about all the people the user knows and their activities. Thus, an analysis of a person’s emails allows automatically constructing a realistic image of the surroundings of that person. This chapter aims to develop ExpertSN, a personalised Expert Recommender tool based on email Data Mining and Social Network Analysis. ExpertSN constructs a personal social network from the email corpus of a person by computing profiles—including topics represented by keywords and other attributes such as recency of communication—for each contact found in the emails and by extracting relationships between people based on measures such as co-occurrence in To and CC fields of the emails or reciprocity of communication. Having constructed such a personal social network, we then consider its application for people search in a given work context. Through an analysis of several use cases, we have derived requirements for a query language that allows exploiting the personal social network for people search, taking into account a variety of information needs that go well beyond classical expert search scenarios known from the literature. We further discuss the application of the people search interface in a personal task management environment for effectively retrieving collaborators for a work task. Finally, we report on a user study undertaken to evaluate the personal social network in ExpertSN that shows very promising results.

Persuasion and reflective learning: closing the feedback loop

Reflecting about past experiences can lead to new insights and changes in behavior that are similar to the goals of persuasive technology. This paper compares both research directions by examining the underlying feedback loops. Persuasive technology aims at reinforcing clearly defined behaviors to achieve measurable goals and therefore focuses on the optimal form of feedback to the user. Reflective learning aims at establishing goals and insights. Hence, the design of tools is mainly concerned with providing the right data to trigger a reflection process. In summary, both approaches differ mainly in the amount of guidance and this opens up a design space between reflective learning and persuasive computing. Both approaches may learn from each other and can use common capturing technologies. However, tools for reflective learning require additional concepts and cues to account for the unpredictability of relevance of captured data.

From stress awareness to coping strategies of medical staff: supporting reflection on physiological data

Nurses and physicians on a stroke unit constantly face pressure and emotional stress. Physiological sensors can create awareness of ones own stress and persuade medical staff to reflect on their own behavior and coping strategies. In this study, eight nurses and physicians of a stroke unit were equipped with a wearable electrocardiography (ECG) and acceleration sensor during their everyday work in order to (a) make them aware of stress and (b) support the re-calling of experiences to identify stressors. In an interview one week later, the participants were asked to recollect stress related events through the examination of the sensor data. Although high activity levels diminished the expressiveness of the data, physicians and nurses could recall stressful events and were interested in their physiological signals. However, existing coping strategies turned out as barriers to the adoption of new tools. Future persuasive applications should focus on integration with existing coping strategies to scaffold the reflection process.

In-App Reflection Guidance for Workplace Learning

In-app reflection guidance for workplace learning means motivating and guiding users to reflect on their working and learning, based on users’ activities captured by the app. In this paper, we present a generic concept for such in-app reflection guidance for workplace learning, its implementation in three different applications, and its evaluation in three different settings (one setting per app). From this experience, we draw the following lessons learned: First, the implemented in-app reflection guidance components are perceived as useful tools for reflective learning and their usefulness increases with higher usage rates. Second, smart technological support is sufficient to trigger reflection, however with different implemented components also reflective learning takes place on different stages. A sophisticated, unobtrusive integration in the working environment is not trivial at all. Automatically created prompts need a sensible timing in order to be perceived as useful and must not disrupt the current working processes.

LIM App: Reflecting on Audience Feedback for Improving Presentation Skills

In order to successfully give a lecture or do a presentation in a conference, presenters need certain skills as well as previous preparation. In such scenarios, reflective learning offers a great potential to improve professional skills and presenters performance by relying on data captured during the presentation. For this purpose, we developed the Live Interest Meter LIM App which allows capturing, aggregating and visualizing live feedback from the audience. After developing the first prototype, testing it and conducting a user study, we developed the second prototype presented in this paper. This further development made emphasis on the recalling and revisiting of past experiences by exploring the collected data. We conducted the evaluation of the LIM App with three university lectures. Our evaluation showed positive results regarding the capturing and sharing of feedback to improve presentation skills. Whilst the LIM App guided the lecturers to reflect and to remember their presentations better, some time and advice to get accustomed to using it is still needed so that it is optimally integrated in their presentations.

Introducing Mood Self-Tracking at Work: Empirical Insights from Call Centers

The benefits of self-tracking have been thoroughly investigated in private areas of life, like health or sustainable living, but less attention has been given to the impact and benefits of self-tracking in work-related settings. Through two field studies, we introduced and evaluated a mood self-tracking application in two call centers to investigate the role of mood self-tracking at work, as well as its impact on individuals and teams. Our studies indicate that mood self-tracking is accepted and can improve performance if the application is well integrated into the work processes and matches the management style. The results show that (i) capturing moods and explicitly relating them to work tasks facilitated reflection, (ii) mood self-tracking increased emotional awareness and this improved cohesion within teams, and (iii) proactive reactions by managers to trends and changes in team members’ mood were key for acceptance of reflection and correlated with measured improvements in work performance. These findings help to better understand the role and potential of self-tracking at the workplace, and further provide insights that guide future researchers and practitioners to design and introduce these tools in a work setting.

In-App Reflection Guidance: Lessons Learned Across Four Field Trials at the Workplace

This paper presents a concept for in-app reflection guidance and its evaluation in four work-related field trials. By synthesizing across four field trials, we can show that computer-based reflection guidance can function in the workplace, in the sense of being accepted as technology, being perceived as useful and leading to reflective learning. This is encouraging for all endeavors aiming to transfer existing knowledge on reflection supportive technology from educational settings to the workplace. However, reflective learning in our studies was mostly visible to limited depth in textual entries made in the applications themselves; and proactive reflection guidance technology like prompts were often found to be disruptive. We offer these two issues as highly relevant questions for future research.