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A Multi-Componential Analysis of Emotions during Complex Learning with an Intelligent Multi-Agent System

Abstract : In this paper we discuss the methodology and results of aligning three different emotional measurement methods (automatic facial expression recognition, self-report, electrodermal activation) and their agreement regarding learners’ emotions. Data was collected from 67 undergraduate students from a North American university who interacted with {MetaTutor}, an intelligent, multi-agent, hypermedia environment for learning about the human circulatory system, for a 1 hour learning session (Azevedo et al., 2013, Harley, Bouchet, \& Azevedo, 2013). A webcam was used to capture videos of learners’ facial expressions, which were analyzed using automatic facial recognition software ({FaceReader} 5.0). Learners’ physiological arousal was measured using Affectiva’s Q-Sensor 2.0 electrodermal activation bracelet. Learners self-reported their experience of 19 different emotional states (including basic, learner-centered, and academic achievement emotions) using the Emotion-Value questionnaire (Harley et al., 2013). They did so on five different occasions during the learning session, which were used as markers to align data from {FaceReader} and Q-Sensor. We found a high agreement between the facial and self-report data (75.6%) when similar emotions were grouped together along theoretical dimensions and definitions (e.g., anger and frustration) (Harley, et al., 2013). However, our new results examining the agreement between the Q-Sensor and these two methods suggests that electrodermal ({EDA}/physiological) indices of emotions do not have a tightly coupled (Gross, Sheppes, \& Urry, 2011) relationship with them. Explanations for this finding are discussed.
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Submitted on : Monday, October 19, 2015 - 10:44:30 AM
Last modification on : Friday, March 22, 2019 - 1:38:05 AM


  • HAL Id : hal-01217164, version 1


Jason M. Harley, François Bouchet, M. Sazzad Hussain, Roger Azevedo, Rafael A. Calvo. A Multi-Componential Analysis of Emotions during Complex Learning with an Intelligent Multi-Agent System. The 2014 Annual meeting of the American Educational Research Association, Apr 2014, Philadelphia, PA, United States. ⟨hal-01217164⟩



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