Stressed or engaged? Addressing the mixed significance of physiological activity during constructivist learning. (July 2023)

• Record Type: Journal Article
• Authors: Sung, Gahyun; Bhinder, Harum; Feng, Tianyi; Schneider, Bertrand
• Is Part Of: Computers & education. Volume 199(2023)
• Publication Date: 2023-07
• Subjects: Constructivist pedagogy -- Engagement -- Physiology -- Multimodal learning analytics; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2023.104784
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: The current paper qualifies previous assumptions on the link between physiology and learner states during constructivist learning, employing a study design where electrodermal activity data (EDA) is collected across two distinctive pedagogies -- direct instruction and hands-on learning. Our results are surprising in that higher arousal during hands-on learning is not indicative of higher learning gains, nor is the strength of the EDA response clearly different when transitioning across pedagogies. However, qualitative investigations show that varying sources of physiological arousal likely to be present in hands-on learning, such as stress, may have led to this mixed connection between EDA and learning during hands-on learning. Moreover, identical tests for the direct instruction section show that higher EDA during this more passive form of learning is indeed indicative of higher learning gains. Our machine learning prediction models achieved fair performance for both learning gains and engagement, providing support for the use of physiological data in gauging and supporting learner states despite its polysemy. We conclude with informed suggestions for building theory-based, nuanced connections between EDA and constructivist learning. Highlights: Learners' electrodermal activity (EDA) levels and peaks were positively associated with learning gains. EDA levels did not clearly differ between direct instruction and hands-on learning; however, EDA synchrony levels did. EDA synchrony did not have consistent ties with learning outcomes, contrary to findings in previous peer dyad research. An EDA-based predictive model achieved substantial explanatory power for key learning outcomes.