Profiling sympathetic arousal in a physics course: How active are students?

Abstract

Low arousal states (especially boredom) have been shown to be more deleterious to learning than high arousal states, though the latter have received much more attention (e.g., test anxiety, confusion, and frustration). Aiming at profiling arousal in the classroom (how active students are) and examining how activation levels relate to achievement, we studied sympathetic arousal during two runs of an elective advanced physics course in a real classroom setting, including the course exam. Participants were high school students (N = 24) who were randomly selected from the course population. Arousal was indexed from electrodermal activity, measured unobtrusively via the Empatica E4 wristband. Low arousal was the level with the highest incidence (60% of the lesson on average) and longest persistence, lasting on average three times longer than medium arousal and two times longer than high arousal level occurrences. During the course exam, arousal was positively and highly correlated (r = .66) with achievement as measured by the students' grades. Implications for a need to focus more on addressing low arousal states in learning are discussed, together with potential applications for biofeedback, teacher intervention, and instructional design.

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

“…Specifically, the students collaborated in pairs to complete an engineering design task, and the authors used hand/wrist movement, electro‐dermal activation, and voice activity detection, for modeling how students engage with the task, in terms of the reasoning strategies the used. Furthermore, in a face to face classroom setting, Pijeira‐Díaz, Drachsler, Kirschner, and Järvelä () utilized the EDA, Galvanic Skin Conductance, temperature and the accelerometer data, to measure simultaneous arousal levels among the students with respect to the students' mood, motivation, affect and collaborative engagement. Results shown that low arousal was the predominant state, whereas all students were never in high arousal states in the classroom, at the same moment.…”

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

“…These devices can shed light on physiological reactions and learning processes that are not explicitly stated in existing theoretical frameworks (Azevedo and Gašević in press). Yet, the scarce amount of studies that have used such sensors in the context of collaborative learning are promising, indicating that they can, for example, track learning challenges (Malmberg et al 2019), predict learning outcomes (Pijeira-Díaz et al 2018), and indicate sharing among group members in monitoring (Haataja, Malmberg, & Järvelä, 2018). That is why it is highly appealing to not only investigate what students verbally express in a learning situation, but to also study the synchrony across individual physiological reactions, invisible to normal human observation and underlying these non-verbal expressions.…”

Temporal analysis of multimodal data to predict collaborative learning outcomes

“…From these data streams, a wide range of higher level features can be inferred including affect, attention, cognitive processing, stress and fatigue. Thus, MMLA may be applicable in a wide range of educational contexts including face-to-face interactions without technological aids (Di Mitri et al, 2017;Pijeira-Díaz, Drachsler, Kirschner, & Järvelä, 2018;Spikol et al, 2018), face-to-face technology enhanced learning (Liu et al, 2016;Viswanathan & VanLehn, 2017b) and online learning (Le, Pardos, Meyer, & Thorp, 2018). Within MMLA, many different combinations of data streams have been explored but it is not clear what makes the different combinations impactful for collaborative learning.…”