Abstract:

Studies on work-related professional learning have shown that learning outcomes and processes are strongly influenced by the emotional aspects of learning. Our own studies on learning in education and health care work have demonstrated that agentic learning taken place in the negotiation and crafting of professional identities was imbued with multiple and mixed emotions, including joy, love, anger, shame, fear, anxiety, happiness, and sadness (Hökkä, Vähäsantanen, Paloniemi & Eteläpelto, 2017; Vähäsantanen, Hökkä, Paloniemi & Eteläpelto, 2016; 2017). However, questions remain concerning how to capture process data on emotions and learning, applying valid indicators and reliable methods of data collection and analysis. Previous studies on emotions have noted the limitations of subsequent self-reported data, and the lack of user-friendly and valid tools to capture changing emotions during learning processes. It has thus been suggested that user-friendly on-line tools for concurrent self-reporting of emotions are needed, and should thus be developed. In the rigorous development of such tools, self-reports should be cross-validated in relation to the observational and psycho-physiological data. In our REAL research project (The Role of Emotions in Agentic Learning at Work; funded by the Academy of Finland, 2016–19), we are developing an on-line application called Emotion Circle (EC) for adults’ self-assessment and reporting of their shifting emotions in the settings of professional learning. In this presentation we will describe how the development of the first prototype version of the EC has taken place in the laboratory conditions, and what are major outcomes and developmental challenges found so far. In developing EC within a laboratory setting, we play the recordings of the selected moments, perceived by the subjects as most and least influential for their learning. Self-reports using EC are thus collected from subjects who watch the selected critical episodes. Subjects are asked to evaluate the nature and quality of their emotions using EC which utilizes a colorful graphic interface containing written emotion-related words. EC makes it possible to collect subjects’ self-assessments of their situation-specific emotions. It transforms and displays the process data into a format which can be synchronized with other kind of (physiological and behavioral) process data collected at the same time. In using EC, we seek to avoid the memory bias of retrospective interviews. Using EC we collect situation-specific data on the quality of perceived emotions plus their dynamic continuity. In addition to the self-reports, we measure the emotions using physiological indicators of the autonomous nervous system, and behavioral data on attention-focusing and facial expressions. In the development of EC, we use automatic facial expression recognition to validate self-reported data. From eye-tracking data we can gain insight into what subjects fixate on when they watch videotaped critical episodes from learning situations. In addition, eye-tracking data shows how subjects fixate on EC, providing an important indicator for assessing the user-friendliness of the application. In the first phase, we are developing the EC application for research purposes. In the long run, we have the purpose to develop EC as an ecologically valid innovative application for the reflection and promotion of learning processes in authentic work-related learning contexts.

Keywords:

Learning processes, emotions, on-line application, process data.