Emotions and Informal Learning Environments

Emotions play an important role in learning experiences, guiding behaviors, influencing motivations, and informing thinking (Barrett, 2006; Storbeck & Clore, 2007). In our research we have used measurement techniques and technologies from the affective neurosciences (i.e. gaze tracking, skin conductance-measurement, facial and gestural analysis) to explore the relationship between emotional state and engagement within and across informal science exhibits. One of our recent projects investigates three different dimensions of emotion: level of activation (on a spectrum from passive to highly stimulated), positive feelings, and negative feelings. Our research suggests that when children simultaneously experience negative feelings (such as confusion or frustration) and are highly activated, they also show signs of stronger engagement behaviors (i.e. longer dwell times, more attention to certain exhibit features, and increased signs of concentration). This suggests that deep, meaningful learning in informal environments can coincide with some level of confusion or frustration that is followed by attempts to find some kind of resolution through which subsequent positive feelings can arise. This is a noteworthy finding as confusion and frustration are feelings that designers and educators often strive to minimize when developing learning experiences (Linn, Chang, Chiu, Zhang, & McElhaney, 2010). The challenge for museum exhibition and education professionals, therefore, is to figure out ways to design learning experiences that offer visitors a challenge that is beyond their initial grasp, but whose eventual resolution can be achieved with support from the exhibits’ or programs’ design. In this session, we will describe the affordances and challenges of using technologies such as gaze tracking glasses and wristbands that measure skin-conductance for learning about what visitors experience in informal learning environments, and will share insights on whether and how these technologies might be used in the service of supporting deep learning in museums. Speaker(s) Session Leader : Sunewan Chunhasuwan, Research Assistant, Museum of Science, Boston MCN 2016 Presenting Sponsor: Piction New Orleans, LA