A multi-componential methodology for exploring emotions in learning

Abstract

Studies on emotions in learning are often based on interviews conducted after the learning. These do not capture the multi-componential nature of emotions, nor how emotions are related to the processes of learning. We see emotions as dimensional, multi-componential responses to personally meaningful events and situations. In this methodologically advanced pilot study we developed a multi-componential methodology, capable of providing complementary information on emotions in professional learning. For this purpose, we used a within-subject design applied to a single individual, with a focus on emotions during professional learning. Within a laboratory setting, the subject was shown personally meaningful video extracts from a learning situation in which she had previously participated. The data were gathered through (i) self-reports of emotions via the Emotion Circle (EC) online assessment tool, (ii) measures of autonomic nervous system (ANS) activity obtained via Electrodermal Activity (EDA) and Heart Rate Variability (HRV), (iii) behavioural registration of facial expression and gaze, and (iv) the Stimulated Recall Interview (SRI). Selfreports of emotions via EC, and also the emotion-driven SRI, were found to be productive, not only in detailing and explaining emotions experienced during the viewing of the videos, but also in bringing about reflective learning and novel insights. EDA and HRV provided complementary information on the subject's ANS activity during the learning process. We present conclusions and future challenges in applying a multi-componential methodology to research emotions within professional learning. Eteläpelto et al 7 | F L R AcknowledgmentProfessor Anneli Eteläpelto's highly-meritorious professional career in the field of adult education at University of Jyväskylä (Finland) will continue in her new role as an emerita. We greatly appreciate her years of dedication to research, and especially all the inspiring ideas she has introduced over the years! Among other roles, she has served the academic community as the coordinator of EARLI SIG14 "Learning and Professional Development" and hosted the SIG meeting in 2008. Further, she is renowned for her repeated successes in securing prestigious grants from the Academy of Finland and elsewhere. Anyone who has had been fortunate enough to collaborate with Anneli has probably found her an inspiring colleague and, sometimes, surprising in her contributions. In her new role as a professor emerita, she will undoubtedly continue to contribute actively to the discourse on learning and professional development. This special issue describes a starting point for new direction of research, which she has actively advocated, and we hope she will remain involved in these developments also in the future to see the vision for which she advocated fully realised.All the best and warm regards on behalf of all your colleagues! Christian, Raija, Erno, and Stephen 8 | F L R Recent studies have shown that processes of group-based identity learning are particularly imbued...

What happens to your body during learning with computer-based environments? Exploring negative academic emotions using psychophysiological measurements

“…EDA data followed a significant linear trend corresponding to HR data, which is, considering the self-report results, in line with our second hypothesis. Prior research (e.g., Eteläpelto et al, 2018 ; Kreibig, 2010 ) showed that high EDA values are indicators for emotionally high activation, which corresponds with our findings. Consequently, EDA can be used as a reliable measure for emotional activation during learning.…”

“…Since we see emotions as a two-dimensional model, both, valence and activation must be examined to capture emotions comprehensively. Then, merging EDA and HR data reveals a physiological pattern, which can identify emotional states (e.g., Barrett & Russell, 1999 ; Eteläpelto et al, 2018 ; Larsen & Diener, 1992 ; Levenson et al, 2017 ). Furthermore, only the valence can declare if the emotion is positive or negative, which is crucial for successful learning.…”

“…The most-reported academic emotions are anxiety, enjoyment, hope, pride, relief, anger, boredom, and shame (e.g., Duffy et al, 2018 ; Järvenoja et al, 2017 ; Loderer et al, 2020 ; Pekrun et al, 2002 ). Academic emotions are mainly evaluated post facto using self-report data (e.g., Boekaerts, 1999 ; Eteläpelto et al, 2018 ; Magno, 2011 ; Pekrun et al, 2011 , 2017 ; Vermeer et al, 2000 ). However, a notable drawback of self-reports is that emotional states must be experienced consciously to report on them.…”

Emotions in Learning at Work: a Literature Review

“…Furthermore, at a time when measurement technology, for example, relating to physiological data, has taken huge developmental steps, one can look forward to research designs combining real-time measurements with self-report data in studies on emotions and learning in the workplace (cf. Eteläpelto et al 2018). Indeed, there is growing interest in studies considering, for example, artificial intelligence and learning, which gathers both physiological and behavioural data about emotion.…”

Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review

“…The link between emotion and learning is widely investigated as emotion is integral to the learning process and influences students’ learning outcomes [ 6 , 7 , 13 , 14 ]. Positive emotions, such as enjoyment or pride, are positively related to learning, whereas negative emotions, such as frustration, tend to have a negative impact on learning [ 15 , 16 , 17 , 18 ].…”