Journal article
Static vs. Dynamic Representations and the Mediating Role of Behavioral Affect on E-Learning Outcomes
International journal of human-computer interaction, Vol.39(17), pp.3312-3323
2023
DOI: 10.1080/10447318.2022.2096187
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.
Details
- Title: Subtitle
- Static vs. Dynamic Representations and the Mediating Role of Behavioral Affect on E-Learning Outcomes
- Creators
- Yuzhi Sun - Oregon State UniversityDavid A. Nembhard - University of Iowa
- Resource Type
- Journal article
- Publication Details
- International journal of human-computer interaction, Vol.39(17), pp.3312-3323
- Publisher
- Taylor & Francis
- DOI
- 10.1080/10447318.2022.2096187
- ISSN
- 1044-7318
- eISSN
- 1532-7590
- Language
- English
- Electronic publication date
- 07/25/2022
- Date published
- 2023
- Academic Unit
- Business Analytics; Industrial and Systems Engineering
- Record Identifier
- 9984277456502771
Metrics
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