Emotion regulation tendencies, achievement emotions, and physiological arousal in a medical diagnostic reasoning simulation

Abstract

Despite the importance of emotion regulation in education there is a paucity of research examining it in authentic educational contexts. Moreover, emotion measurement continues to be dominated by self-report measures. We address these gaps in the literature by measuring emotion regulation and activation in 37 medical students' who were solving medical cases using BioWorld, a computer-based learning environment. Specifically, we examined students' habitual use of emotion regulation strategies as well as electrodermal activation (emotional arousal) from skin conductance level (SCL) or skin conductance response (SCR), as well as appraisals of control and value and selfreported emotional responses during a diagnostic reasoning task in [Blinded]. Our results revealed that medical students reported significantly higher habitual levels of reappraisal than suppression ER strategies. Higher habitual levels of reappraisal significantly and positively predicted learners' self-reported pride. On the other hand, higher habitual levels of suppression significantly and positively predicted learners' self-reported anxiety, shame, and hopelessness. Results also revealed that medical students experienced relatively low SCLs and few SCRs while interacting with BioWorld. Habitual suppression strategies significantly and positively predicted medical students' SCLs, while SCRs significantly and positively predicted their diagnostic efficiency. Findings also revealed a significant, positive predictive relationship between SCL and shame and anxiety and the inverse relationship between SCL and task value. Implications and future directions are discussed.

Are we together or not? The temporal interplay of monitoring, physiological arousal and physiological synchrony during a collaborative exam

“…An emerging body of learning research (e.g., Harley et al 2015;Harley et al 2019) have investigated physiological arousal in connection with the arousal dimension of emotion in the traditional circumplex model (Russell 1980), which reflects how physiologically activating the emotion is (Pekrun 2006). Still, many theories of emotion (see e.g., Scherer and Moors 2019) also acknowledge the role of information processing in evoking the physiological arousal and steering function, in terms of how arousal is appraised as an emotion (Schachter and Singer 1962), especially in the context of collaborative learning.…”

Examining physiological and self‐report indicators of empathy during learners’ interaction with a queer history app

“…This program of research principally contributed to the literature by examining the emotional engagement of using mobile apps to learn about history; a gap in mobile app research that is important to address because of the important links between emotions and academic achievement (Pekrun, 1992; Pekrun & Linnenbrink‐Garcia, 2014; Pekrun, Lichtenfeld, Marsh, Murayama, & Goetz, 2017). To‐date, we have consistently found that learners tend to experience higher levels of enjoyment and curiosity and low levels of boredom, frustration and anxiety while using such apps to learn about history, including the one examined in this paper (Harley, Liu et al ., 2019; Harley et al ., 2016, 2018, 2020; Poitras et al ., 2019). These findings are important because they further highlight the potential of using mobile apps to teach students about history.…”

“…Many methods are available to measure physiological arousal, such as cortisol sampling from saliva (Jamieson, Mendes, Blackstock, & Schmader, 2010; Spangler, Pekrun, Kramer, & Hofmann, 2002), heart rate (ie, cardiac vagal tone; Butler, Wilhelm, & Gross, 2006; Dan‐Glauser & Gross, 2015; Li et al ., 2009) and pupil dilation (Scrimin, Altoè, Moscardino, Pastore, & Mason, 2016). In the current study, we used skin conductance (SC) because it is one of the most widely researched physiological channels for measuring emotion (Harley, 2015; Harley et al ., 2015; Harley, Jarrell, et al ., 2019; Kapoor, Burleson, & Picard, 2007; Kreibig, Samson, & Gross, 2015; Mauss & Robinson, 2009; Picard, Fedor, & Ayzenberg, 2016), especially in technology‐rich learning environments (Calvo & D'Mello, 2010; Harley, 2015; Woolf et al ., 2009) where the importance of multimodal data and developing the analytical approaches to leverage it, is becoming widely acknowledged (Azevedo, Taub, & Mudrick, 2018; D’Mello, Dieterle, & Duckworth, 2017).…”

Enhancing the Learning Experience Using Real-Time Cognitive Evaluation

“…Affect sensitive computerized learning systems have shown considerable promising results in analyzing and improving the learning experience [1], [2]. Methodological, technical and analytical approaches are being constantly explored to help these systems leverage the knowledge of the learners' behavior to analyze and support the learning process.…”