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

Abstract

There is a strong increase in the use of devices that measure physiological arousal through electrodermal activity (EDA). Although there is a long tradition of studying emotions during learning, researchers have only recently started to use EDA to measure emotions in the context of education and learning. This systematic review aimed to provide insight into how EDA is currently used in these settings. The review aimed to investigate the methodological aspects of EDA measures in educational research and synthesize existing empirical evidence on the relation of physiological arousal, as measured by EDA, with learning outcomes and learning processes. The methodological results pointed to considerable variation in the usage of EDA in educational research and indicated that few implicit standards exist. Results regarding learning revealed inconsistent associations between physiological arousal and learning outcomes, which seem mainly due to underlying methodological differences. Furthermore, EDA frequently fluctuated during different stages of the learning process. Compared to this unimodal approach, multimodal designs provide the potential to better understand these fluctuations at critical moments. Overall, this review signals a clear need for explicit guidelines and standards for EDA processing in educational research in order to build a more profound understanding of the role of physiological arousal during learning.

Towards hybrid human‐AI learning technologies

“…Artificial intelligence can help detect and diagnose other learner characteristics and progress features during learning. For example, important emerging research areas include self‐regulated learning (SRL; Fan, Lim et al, 2022; Järvelä & Bannert, 2019), emotion (du Boulay, 2018; Horvers et al, 2021), motivation (Duffy & Azevedo, 2015; Rodrigo et al, 2008), engagement (Pedro et al, 2013), and collaboration (Olsen et al, 2014: Onan et al, 2019). Developments around SRL are outlined below as an example of these novel developments.…”

Context-Aware Classrooms as Places for an Automated Analysis of Instructional Events

“…Fundamentally, answering the question of how to teach in an enactivist way is trying to answer the question: "How is like to be a teacher or students in a classroom?" As the previous, this approach is fully compatible with the multimodal analysis of the classroom environment [31], but needs a step further: the account of first-person perception within its environment. To data about attendees' location, speech, gesture, and posture, one has to add information about gaze, emotion or other physical-based measures (e.g., electrodermal) [30].…”

“…Up to now, enactivist CACs per se do not exist yet, even though recent apparatus like mobile eye trackers [36,37], electrodermal response trackers [31] or other types of sensors capturing various individual data, like pulsimeters, body temperature [38], as well as multimodal learning analytics capturing emotion and gaze recognition systems [39], can help gather and analyze instructional situations in an enactivist way: firstperson-based and accounting for context more fully. As a promising example of what an enactivist CAC could be, researchers [40] developed ACORN, a multimodal machine learning system that analyzes audio and video features of instructional events footages to infer classroom climate, as modeled in the Classroom Assessment Scoring System (CLASS) [41], a reliable and well-studied classroom observation system.…”

Scoping Review of Bioelectrical Signals Uses in Videogames for Evaluation Purposes

“…[18] focuses on "Affective Gaming", and how the signals collected by different sensors can provide information to the player through the game and how the gaming experience can be changed by taking into account the emotional state of the player. [19] reviews the use of the signal generated by the electrical activity of the skin (EDA) in different studies applied to learning and [20] review the use of electrocardiography (ECG) out of hospitals. Some others reviews like [21] are focused on providing overviews of techniques for biosignal processing.…”