Static vs. Dynamic Representations and the Mediating Role of Behavioral Affect on E-Learning Outcomes

Abstract

Online learning has become increasingly commonplace, including the replacement and augmentation of traditional in-residence education, as well as ad-hoc training systems and just-in-time knowledge dissemination. However, design for instructional media to facilitate performance has relied on an inadequate understanding of the behavioral affect elicited from these designs. To investigate the degree to which excitement and engagement are predictive of individual learning outcomes in an online learning setting, we evaluate an experiment with two instructional representations (static and dynamic) using learning materials for a semaphore signaling system. We examine the excitement and engagement levels of the participants using electroencephalography (EEG) as potential mediators. Learning outcomes are measured by the evaluation scores of knowledge retention. We consider several structural equation models (SEMs) to see the underlying relationship between instructional representation, excitement, engagement, and learning outcomes. Notably, the models indicate the full mediation of behavioral affect on learning outcomes, in which both the range of excitement and the maximum level of engagement are mediating effects. This article illustrates the potential for biometrically measured affect aid in the modeling and understanding of how instructional design features ultimately impact performance.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Yuzhi Sun

Yuzhi Sun is a researcher at the Oregon State University Industrial Engineering program. She has a background in human factors with a specialization in human-computer interaction and cognitive psychology. Her research focuses on investigating the effect of biometrics including EEG and eye-tracking on learning outcomes.

David A. Nembhard

David A. Nembhard is a Professor of Industrial Engineering and Business Analytics at the University of Iowa. His research interest includes workforce engineering and human cognition. He has contributed to several fields of study including human factors, management science, production systems, and ergonomics.